AI Paper Digest

Friday, October 2, 2026

753 papers cs.AI · cs.LG · cs.CL ← 2026-10-012026-10-05 →

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Highlights

Rules to Tools: Executable Checks for LLM Agents in Scientific Computing

Highlight HF pick · 1▲Agents Jingjie Ning, Guojiang Zhao, Chen Xu, Shanshan Zhong, Xiaochuan Li, Ji Zeng et al. Scientific coding agents get equations and requirements as text and must judge whether their revised programs meet them. Rules to Tools (R2T) gives agents prepared executable checks of those requirements instead of text alone. In matched SciCode repair experiments, complete repair rose from 26/30 with text to 29/30 with callable checks overall, though most tasks tied and the confidence interval for the difference includes zero. In a PDE comparison, checks matched detailed text in accuracy while reducing model output by 31.2%, at the cost of higher CPU use.

Coding agents in scientific computing receive equations, boundary conditions and output requirements as text, but have no ready way to check whether each revision actually satisfies them. Rules to Tools (R2T) turns those public requirements into prepared, callable checks and compares agents given the checker against agents given identical written rules plus ordinary Python execution.

  • Each check runs a public relation, such as a Numerov recurrence residual, field reconstruction after rotation, reciprocal-cell geometry or PDE divergence and momentum residuals, on fixed probe inputs and tolerances, then returns a measurement, error report or "unavailable" status; independent benchmark graders score the final programs.
  • In matched SciCode repair runs with DeepSeek-V4.1-Flash, complete repair across two held-out task-ID cohorts is 26/30 with text vs. 29/30 with checks (13/16 vs. 15/16 and 13/14 vs. 14/14), but the eight-ID cohort's bootstrap 95% interval is [−12.5, 43.75] points, the combined sign test gives p = 0.625, and the gain disappears entirely if task 77 is excluded.
  • Two of the three tasks that favor tools (77 and 11) show no violation on the initial check, so the gains appear to come from the whole executable-support package rather than from checks catching the bug directly; giving agents the checker source to run through Python also scores 15/16.
  • Results elsewhere are mixed: a 12-task shared-definition SciCode cohort ties at 13/24, development-exposed alternate programs go 3/10 to 7/10, and matched PDE tasks go 23/24 to 24/24 with 31.2% fewer output tokens; the flow-task gain (16/28 to 21/28) comes from a cohort selected in a way that favors tools.
  • The checks are hand-authored or reviewed by the authors, authoring time was not recorded, samples are two runs per task, and public CPU use rises substantially (+38.6% in the eight-ID cohort and +87.3% across both cohorts), while output-token savings vary from cohort to cohort.

arXiv:2610.00313 PDF

Predictive Credit: Measuring What Scientific Explanations Add to Experimental Forecasts

Highlight HF pick · 1▲Agents Jingjie Ning, Xueqi Li, Yibo Kong, Dongting Li Proposes a protocol for measuring predictive credit: whether a research agent's explanation of a planned experiment improves forecasts of its outcome. Forecasts are paired so they share the same intervention, forecaster, and outcome, while the context varies between a plain description, a matched explanation, and an explanation borrowed from a different experiment. Across 336 prospective states spanning controlled learning, 12 Tox21 endpoints, and 24 OpenML tasks, the preregistered credit decision was inconclusive, and natural explanations showed no confirmed accuracy gain. A researcher-written mechanism explanation, used as a positive control, did lower forecast error by 2.6 percentage points.

Research agents attach explanations to the experiments they propose. The authors measure an explanation's predictive credit: how much it improves forecasts of the experiment's executed outcome. Each comparison holds the intervention, forecaster, and outcome fixed and varies only the context: the public description alone, the case's own (matched) explanation, or a donor explanation assigned from another state.

  • The protocol runs five checks (commitment, evidence-backed delivery, predictive gain, donor alignment, and known-signal uptake) over 336 prospective states in a controlled learning study (v5), 12 Tox21 assay endpoints, and 24 OpenML tasks. DeepSeek V4 Pro and DeepSeek V4 Flash produced paired forecasts, which were scored on point MAE, repeat drift, and a proper 80% interval score.
  • All three preregistered decisions failed to confirm predictive credit: v5 was inconclusive, Tox21's interval-score test was unmet (D−M = −.0026, 95% CI [−.0174, .0104]), and OpenML's joint formation, point-equivalence, and repeatability rule was unmet, with seed-donor intervals spanning zero in both external studies.
  • Structured prediction cards made repeated forecasts much more consistent, cutting Tox21 repeat drift by 64.5% with matched cards and 59.1% with donor cards. Because donor cards worked almost as well, this looks like output coordination rather than case-specific information, and a Flash replay actually raised matched point MAE from .01823 to .02020.
  • Assigning full cards on OpenML widened the nominal 80% intervals by 21% without improving coverage (49.3% vs. 51.4% for description-only), partly because 78 of 144 cards were empty scaffolds. Feeding all 144 raw notes directly to Flash also produced no detectable matched accuracy gain, and direction accuracy largely tracked an always-predict-improvement prior (58.6% vs. 57.7%).
  • Forecasters can use strong information when they get it: a researcher-authored mechanism note cut point MAE by 2.60 pp compared with description-only (3.795 vs. 6.391), and exact-signal calibration won 144 of 144 states. However, the conclusions are limited to DeepSeek V4 forecasters, small computational interventions, and the donor resolutions tested.

arXiv:2610.00314 PDF

Make Sparse Rewards Count: Density-Aware Reward Aggregation for Multi-Reward RL

Highlight HF pick · 14▲Reinforcement Learning Tong Zheng, Skylar Zhai, Zhan Cheng, TianMing Sha, Youling Huang, Shuo Zhou et al. When LLMs are trained with reinforcement learning against several rewards at once, some objectives learn more slowly than others, even with the per-reward normalization used in GDPO. The authors show that each reward's advantage energy is proportional to how often it gives a nonzero signal within rollout groups. They propose DARA (Density-Aware Reward Aggregation), which reweights each reward per batch by an inverse-square-root density correction. On tool calling and math reasoning, DARA learns the targeted behaviors faster than GDPO, reaching near-saturated length compliance in up to 65% fewer training steps, while final performance stays competitive.

Multi-reward RL methods like GDPO normalize each reward separately, but a reward that varies within only a few rollout groups still contributes less total learning signal per batch, so sparse objectives get learned slowly. DARA (Density-Aware Reward Aggregation) measures how often each reward is active in a batch and scales up the rarely active ones so that every reward contributes a comparable amount of signal.

  • Under idealized GDPO normalization, every active rollout group contributes exactly G−1 units of "advantage energy" (the sum of squared advantages), so a reward's batch total scales linearly with its active-group density; DARA multiplies each reward's advantages by min(w_max, √(π_ref/π_k)), where π_k is that reward's density and π_ref is the highest density in the batch, recomputing the weights every batch.
  • The main variant, DARA-Asym, amplifies only positive advantages so it doesn't add extra negative pressure when rewards conflict, while DARA-Sym scales both signs and is the version for which the energy-matching result is exact.
  • On ToolRL/BFCL-v4 tool calling, the median format reward reaches 0.8 at steps 14–15 versus 19 for GDPO and 34 for GRPO; when a third length reward is added, GDPO's average format score drops from 97.11% to 94.39%, while DARA stays at roughly 97%.
  • On math reasoning with a 4,000-token length limit, DARA reaches 99% length compliance in 41–65% fewer steps than GDPO (for example, 50 versus 142 steps on DeepSeek-R1-7B), and DARA-Sym cuts over-length responses at step 50 to 1.09%, compared with 6.82% for GDPO.
  • The main gains are in training speed rather than final quality: final accuracy is roughly on par (DARA-Asym matches GDPO on Qwen2.5-3B, and trails GRPO on Qwen3-4B-Instruct, 62.17% versus 62.99%), DARA-Sym gives up some accuracy in exchange for compliance, and the energy-matching guarantee holds only under idealized assumptions and only while the weight cap (w_max = 5) doesn't bind.

arXiv:2610.00574 PDF

ActiveSaddler: Automated Curriculum Learning for Agent Harness Optimization

Highlight HF pick · 29▲Agents Sungho Park, Wonjoong Kim, Jue Zhang, Wook-Shin Han, Pengfei Gao, Chanyoung Park et al. Automated harness optimization improves LLM agents by iteratively rewriting their prompts, tool interfaces, and control logic from execution feedback, but existing methods fix in advance which training scenarios generate that feedback. ActiveSaddler treats the choice of scenarios as an automated curriculum learning problem, modeled as a non-stationary bandit whose arms are reusable failure patterns abstracted from recurring agent failures. It estimates the learning progress from targeting each pattern and balances revisiting known weaknesses with exploring new scenarios, so the curriculum co-evolves with the harness. Compared with the same harness optimizer using a fixed scenario order, it improves test Pass@1 by 4.4 points on GAIA2 and 7.5 points on Terminal-Bench 2.0.

Harness optimizers update an LLM agent's prompts, tools, and control logic from execution feedback, but they usually fix in advance which training scenarios produce that feedback. ActiveSaddler makes the choice of scenarios adaptive: it treats it as a non-stationary bandit whose arms are failure patterns discovered during optimization, so the curriculum changes as the harness does.

  • An LLM-driven Failure-Pattern Extractor groups diagnosed failures into reusable failure-pattern arms; an Arm Prioritizer scores each arm on severity, fixability, breadth and side-effect risk, then samples one through a softmax; an Exploration Controller decides each iteration whether to revisit a known arm ("Pull") or draw unseen scenarios ("Draw").
  • With the AutoSaddler optimizer and gpt-5.5 held fixed, it reaches 59.8% Pass@1 on GAIA2 and 80.0% on Terminal-Bench 2.0, which is +4.4 pp and +7.5 pp over a fixed shuffled scenario order; it also beats easy-to-hard fixed curricula, GEPA, Meta-Harness, and the hand-built Terminus-KIRA harness (69.2%).
  • It is much cheaper end to end despite the extra optimizer calls: it reaches 58.5% development accuracy on GAIA2 for $298, while AutoSaddler needs $1,360 to get there; added to GEPA, it lifts Pass@1 from 54.2% to 57.2%.
  • Ablations show every component matters: replacing failure-pattern arms with category or scenario arms costs up to 10.8 pp on Terminal-Bench 2.0, flat arm scoring costs 4.5 to 7.5 pp, and a fixed explore-every-five-iterations schedule costs 4.5 to 8.3 pp.
  • Limitations: the curriculum decisions depend on LLM judgment from a strong model at high reasoning effort, the Terminal-Bench 2.0 test split has only 40 tasks with standard deviations up to 5 pp, and the evidence of robustness to run-to-run randomness is a single extra GAIA2 run.

arXiv:2610.00906 PDF

RPTune: Learned Context Curation for LLM Catalog Search

Highlight HF pick · 9▲Applications Chuxuan Hu, Hejie Cui, Norman Huang, Shubham Kumar Bharti, Wang-Chiew Tan, Sercan \"O. Ar{\i}k Small merchant catalogs fit inside a long-context LLM's window, but the models do not use long contexts evenly, so putting the whole catalog in the prompt does not guarantee good product search. RPTune pairs a learned curator, an encoder-reorganizer that orders and prunes products using feedback from the downstream LLM, with LLM post-training that uses a context-relative reward and automatically generated supervision grounded in the catalog. Across 7 real merchants with 100 complex conversational queries each, curation alone improves search accuracy by up to 31.4 percentage points, and post-training adds about 10.3 points on average for both proprietary and open-weight models.

Small-merchant catalogs often fit entirely in a long-context LLM's window (the median is about 51K tokens), and prompting the model with the full catalog already beats retrieval pipelines by at least 9 EM points. But LLMs use long contexts unevenly. RPTune learns to prune and reorder the catalog for the downstream model, then post-trains that model on the curated contexts, using only synthetic, catalog-grounded supervision.

  • How curation works: an encoder–reorganizer scores each product as embedding similarity plus a learned MLP adjustment, keeps the top 25%, and puts the highest-scoring items closest to the prompt. A fine-tuned EmbeddingGemma 300M encoder is trained with a contrastive loss (MNRL), and the reorganizer is trained with GRPO-style feedback from a frozen gemma-4-E4B-it, with rewards taken from LLM-judged relevance scores.
  • Curation results: across 9 frozen backbones and 7 Shopify merchants (100 multi-constraint conversational queries each), curation raises exact-match accuracy by 13.5 points on average and by up to 31.4 points for a single merchant. It improves all 63 backbone–merchant pairs and cuts latency by 28% on average, while off-the-shelf selectors such as Jina Reranker 3.5 and Gemini Embedding 2 hurt accuracy for several merchants at the same budget.
  • Post-training: GRPO on curated contexts uses a "context-relative" reward that credits the best product still present in the pruned context. It adds 10.3 EM points beyond curation, taking gemma-4-E4B-it from 10.7% to 31.0% EM, and swapping in a global or continuous reward costs about 10 points on one merchant.
  • Generalization: gains hold without retraining when the catalog doubles with new products (+35.0 EM over the baseline) and in all 49 cross-merchant train/eval pairs (+18.1 EM on average out of domain). A supervised version of the reorganizer does no better than the encoder alone, which shows the LLM-in-the-loop feedback matters.
  • Limitations: evaluation queries are synthetic and labeled by gemini-3.1-pro, and each query has a single reference answer even though about 1.85 variants per query are judged acceptable and independent judges agree only 36–46% of the time. The setup is single-turn only, assumes the whole catalog fits in context, and absolute accuracy stays modest.

arXiv:2610.00964 PDF

Scaling and Distilling Text Embeddings for Better Diffusibility

Highlight HF pick · 16▲Large Language Models Zekai Zhang, Yunjie Tian, Yanjin He, Xiaoyan Zhang, Dongdi Zhao, Qing Qu et al. Continuous diffusion language models (DLMs) generate text by running diffusion over text embeddings, which raises the question of which embedding space is easiest to diffuse over. The authors find that moving to stronger encoders in the same family (T5, then T5Gemma-1, then T5Gemma-2) greatly improves generation. Raw T5Gemma-2 embeddings, however, separate even plausible alternative words so sharply that imperfect sampling often lands on invalid embeddings. Distilling T5Gemma-2 into a student encoder trained on the teacher's decoded probabilities as soft labels pulls these alternatives closer together, and the resulting medium-sized DLM reaches a generative perplexity of 17.8 on OpenWebText, outperforming GPT-2-M (real text scores 15.4).

Continuous diffusion language models run latent diffusion over text embeddings, so the embedding space matters a lot. Stronger encoders give better generation, but T5Gemma-2's embeddings keep plausible alternative words too far apart, and imperfect sampling then lands between them on "invalid" points that decode poorly. The fix is to distill the encoder using the teacher decoder's soft probabilities, which pulls candidate words together into a connected, more diffusible region.

  • With the ELF diffusion framework held fixed and only the encoder swapped, moving from T5-small through T5Gemma-1 to T5Gemma-2-270M cuts generative perplexity by about 40% at matched entropy; ModernBERT is strong on discriminative tasks but produces repetitive text, so encoder quality alone does not predict diffusibility.
  • The student is a 9-layer encoder initialized from evenly spaced layers of the 18-layer teacher and trained with a KL loss against the frozen teacher decoder's top-256 token distributions on OpenWebText; ablations show the soft labels are what matters, since students trained with one-hot cross-entropy or embedding MSE never reach real-text entropy, and an 18-layer student also beats the teacher.
  • At 32 sampling steps, uncertain positions per 1024-token sequence drop from 142 to 42, and invalid ones from 109 to 12, compared with the raw teacher embeddings.
  • On OWT-1024, ELF-M on the distilled embeddings reaches Gen. PPL 17.8 (real text 15.4) at real-text entropy, beating raw T5Gemma-2 (19.3) and GPT-2-M (20.8); ELF-B improves from 38.5 to 31.2, and on LM1B from 60.6 to 50.5, while the student also halves encoding cost.
  • The trade-off is discriminative power (SST-2 linear-probe accuracy falls from 89.4 to 78.1); in addition, comparisons against baselines that use different encoders are not fully controlled, and larger encoders and practical-scale tasks are left for future work.

arXiv:2610.01016 PDF

Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States

Highlight HF pick · 31▲Agents Yu Luo, Jiamin Jiang, Yimin Zuo, Xidao Wen, Rongchen Gao, Yongqian Sun et al. Large language model (LLM) agents on long tasks usually store interaction history as memory, but that memory does not guarantee a coherent picture of the current world. PoS is an inference-time framework that builds and continually updates an explicit belief state, which combines an estimate of the current world state with the task requirements still unresolved. It checks the belief for consistency and watches task progress to detect Belief Trapping, where the agent keeps acting without getting closer to the goal, then applies a recovery strategy matched to the trapping pattern and the type of unresolved requirement. Across four execution and diagnosis benchmarks, PoS achieved the best overall performance on every benchmark with all three LLM backbones, and it stayed resilient as the context grew.

LLM agents on long tasks usually rely on raw interaction history or compressed memory, which can still leave them without a consistent picture of the current world. PoS (Progression of States) is an inference-time framework that instead keeps an explicit, task-conditioned belief state: an estimate of the world plus what is still unresolved. It checks this belief for consistency and recovers when the agent keeps acting without making progress.

  • The belief has four parts: an Entity–State–Relation graph of the world, the task goal, a set of epistemic gaps (what the agent still needs to learn) and a set of achievement gaps (what it still needs to accomplish); on each step the agent works on one active gap, and a separate Belief Sentinel checks each proposed update against itself and the latest observations before it is committed.
  • A belief-health score combines how long gaps stay unresolved, how often steps make no progress, and how often the world state repeats; when the score drops too low (Belief Trapping), the trap is classified by motion pattern (Static, Cycle or Drift) and by which kind of gap is stuck, and those two labels determine the constraints placed on the agent's next actions.
  • Across ALFWorld, LOCA-Bench, RCA-100 and ClinDiag, run on three backbones (Qwen3.7-Plus, Kimi-K3, GLM-5.3), PoS scores highest on every benchmark with every backbone, with relative gains over the strongest baseline of up to 22.68% on ALFWorld (88.81% vs. 72.39% with Qwen3.7-Plus) and 37.89% on RCA-100 joint accuracy.
  • Removing consistency validation costs up to 14.93 points on ALFWorld and 11.81 on LOCA-Bench, and on LOCA-Bench PoS holds steady as context grows from 96K to 256K, ending 10.67–16.00 points ahead of the best baseline at 256K.
  • The main cost is tokens: on RCA-100, accuracy rises from 24.27% to 38.83% and the task agent itself uses 20.9% fewer tokens, but total token use is 5.06× that of a raw-trajectory agent because building and checking the belief takes extra calls.

arXiv:2610.01415 PDF

Sharpening Tax in Post-Training

Highlight HF pick · 29▲Reinforcement Learning Changdae Oh, Qi Zeng, Qi Qi, Andrey Zhmoginov, Deren Lei, Yun He et al. Tests whether reinforcement learning post-training of LLMs merely sharpens existing behavior on agentic, multi-turn tool-use tasks. Pre-trained base models with a light inference harness have far lower single-shot accuracy, but often beat their post-trained counterparts in solution coverage (pass@K) given enough test-time samples. The authors find that post-training pushes tasks toward always-solved or never-solved, and they propose Sharpening Tax, a metric for the lost test-time scalability. The tax appears in most of 42 model-benchmark cases across 14 model pairs. A Bayesian sampler that adapts temperature to each prompt's difficulty, posterior-tempered group sampling (PTGS), pays a smaller tax during RL training while also improving single-shot accuracy.

Reinforcement-learning (RL) post-training of LLMs is suspected of sharpening behaviors the base model already has, raising single-shot accuracy while shrinking the set of tasks the model can solve at all. The authors find this trade-off also holds for multi-turn agentic tool use, introduce a metric for the coverage lost, and propose a per-prompt adaptive-temperature sampler that reduces that loss during RL training.

  • Pre-trained base models wrapped in a light, model-agnostic harness (a simple system prompt plus relaxed tool-call parsing) trail their post-trained counterparts on pass@1 but scale more steeply with repeated sampling and usually overtake them; on WebShop, base gemma-4-31B reaches over 85% pass@128 versus 56% for the post-trained model, and larger models reach this crossover at smaller budgets (about 3 rollouts at 31B versus more than 128 at 4B).
  • The mechanism is bimodalization of per-task success rates: for gemma-4-31B on WebShop, the share of tasks that are solved only sometimes falls from 87.6% to 30.0%, while always-solved tasks rise from 0% to 26% and never-solved tasks from 12.4% to 44.0%, so post-training buys consistency at the cost of coverage.
  • Sharpening Tax is the base model's advantage over the post-trained model in area-based test-time scalability, which works out to the expected number of failed attempts before the first success; the calibrated version is positive at a budget of 128 in 36 of 42 model-benchmark cases (14 model pairs from the Gemma-4, Ministral-3, Qwen2.5 and Qwen3.5 families, tested on BFCL v4, WebShop and ACEBench), and an estimate from 8 rollouts predicts the 32-rollout tax with Spearman ρ = 0.85.
  • PTGS (posterior-tempered group sampling) keeps a Beta posterior over each prompt's success rate, draws a difficulty estimate by Thompson sampling, and sets the rollout temperature from it, raising temperature on hard prompts and lowering it on easy ones; it needs no change to PPO or GRPO and is dropped at test time.
  • When Qwen2.5-7B-Instruct is fine-tuned on Sokoban and FrozenLake, PTGS improves both pass@1 and pass@128 for both algorithms (for example, GRPO on Sokoban goes from 55.3 to 72.5 pass@128, with the tax falling from 0.081 to 0.025); however, the study is observational because the open checkpoints' training recipes are undisclosed, thinking mode was turned off, and the scope is limited to text agents with binary success signals.

arXiv:2610.01509 PDF

Hierarchical Continuous Diffusion Language Models

Highlight HF pick · 47▲Large Language Models Hui Ren, Zihan Li, Chang Liu, Huidong Liu, Alexander Schwing When discrete diffusion language models decode several tokens in parallel, each token is sampled independently, which breaks the dependencies between them. Continuous diffusion models avoid that problem but have nothing tying their latent state to a valid token configuration until the final decode. HC-DLM (Hierarchical Continuous Diffusion Language Models) makes a continuous latent the only persistent generative state: at every step tokens are read out from the latent and fed back as a scaffold for the next latent update. The method is trained with an objective derived from a variational bound on token likelihood. At matched model size, it outperforms discrete and continuous diffusion baselines in puzzle accuracy on Sudoku and Countdown and in generative perplexity on LM1B.

Discrete diffusion language models decode tokens in parallel but sample each one independently from its marginal, which loses the dependencies between them. Continuous diffusion models share one latent state across tokens, but nothing ties that state to a valid token sequence until the final decode. HC-DLM makes a continuous latent the only state that persists across reverse steps: at every step, tokens are read out of the latent and fed back as a discrete scaffold that conditions the next latent update, and the whole two-level chain is trained from a single variational bound on token likelihood.

  • Each sampling step works in three stages: a token-conditioned denoiser predicts the clean latent and takes an Euler step, a token predictor reads out argmax tokens, and those tokens are re-noised through the discrete forward kernel; the ELBO reduces in practice to a reconstruction cross-entropy, a flow-matching loss, and an encoder-entropy regularizer.
  • On Sudoku at a matched 6M parameters, it reaches 72.41% on the out-of-distribution hard split, ahead of the hybrid CCDD (70.73%) and the best MDM variant (49.88%), though CCDD stays marginally ahead on the easy split (94.65% vs 94.21%).
  • On Countdown, it beats CCDD at 6M parameters by a margin that grows with task length, scoring 84.41% vs 81.18% on CD4 and 37.52% vs 25.35% on CD5, although the much larger 85M-parameter RDM still leads overall (87.0% and 45.8%).
  • On LM1B unconditional generation at 118M parameters, it reaches a generative perplexity of 75.5, the best of the diffusion models compared (Plaid 77.3, LangFlow 92.2, MDM 103.9), but it still trails the autoregressive Transformer (66.7) and relies on classifier-free guidance with w = 2.75.
  • Ablations show that both levels are needed: removing the token conditioning drops hard-Sudoku accuracy to 24.74%, and switching from the uniform to the absorbing discrete kernel caps it at 48.25%; however, all experiments are small-scale, and the only comparison on natural language is on a single benchmark.

arXiv:2610.02193 PDF

SILSA: Sliding-Window Slice Latents for Topology-Preserving High-Resolution 3D Generation

Highlight HF pick · 6▲Vision Tianjiao Yu, Xinzhuo Li, Yifan Shen, Ying Shen, Kiet A. Nguyen, Adheesh Sunil Juvekar et al. High-resolution 3D generation usually relies on voxel latents and multi-stage pipelines, which split continuous surfaces into many local tokens, raise cost, and weaken topological consistency for thin or highly connected shapes. SILSA represents shapes as a fixed set of overlapping slice latents along the three canonical axes. A Slice VAE encodes them, a Volumetric Anchor Lattice coordinates the slices from different axes, and the shape is generated in a single rectified-flow stage. Topology supervision matches persistence diagrams and Betti transitions across neighboring slices. Against the strongest baseline, SILSA improves PSNR by 8.7% and Betti error by 9.2% while using 70% fewer tokens than the next-most compact baseline, cutting training memory by 40.4% and inference time by 58.5%.

High-resolution 3D generators built on sparse or hierarchical voxel tokens use a token count that varies with each shape, often need multiple stages, and tend to break thin parts, holes, and connectivity. SILSA replaces them with a fixed set of 384 overlapping slice latents along the x, y and z axes, generated in a single rectified-flow stage and trained with topology losses computed on 2D cross-sections.

  • A SliceVAE encodes surface points and normals into 128 bins per axis, using an 8-bin sliding window and PointNet-style max-pooling; it scatters the three slice sequences into a coarse 3D grid and upsamples sparsely from 16³ to 256³ before extracting a mesh with differentiable Dual Marching Cubes.
  • Its topology loss matches persistence diagrams for components and holes within each slice, and matches changes in Betti numbers between neighboring slices; on reconstruction this cuts Betti error from 4.43 to 1.58 compared with training without it, and reaches 1.582 vs. 1.743 for the strongest baseline, SparseFlex (a 9.2% relative reduction).
  • During generation, a Volumetric Anchor Lattice (a shared 16³ feature grid that slice tokens read from and write to through gated updates) keeps the three axis streams consistent; without any cross-axis communication, Chamfer distance jumps from 0.59 to 5.87 and Betti error to 17.91, while plain cross-attention between axes comes close (0.60 / 1.64).
  • On image-to-3D with 200 Toys4K assets, SILSA posts the best FD, PSNR, coverage and MMD, raising PSNR from 30.12 to 32.74 and coverage from 73.12% to 79.08% over SparseFlex, though its CLIP score is slightly lower (87.94 vs. 88.22).
  • Its 384 tokens are 70% fewer than Dora's 1,280 and over 98% fewer than the variable-length tokenizers in Trellis, XCube and SparseFlex, and compared with Dora it cuts training memory by 40.4% and inference time by 58.5% (0.34 s per shape); the main limitation is that evaluation covers only 200 benchmark assets plus 50 in-the-wild images, and the topology losses supervise only the VAE, not the generator.

arXiv:2610.02201 PDF

Applications 143

Pushing CPU Speech Synthesis to the Wall: Extreme Inference Tuning under Serverless Architecture and Billing

Pakorn Nathong, Kunat Pipatanakul cross-listed Serverless platforms that bill per instance charge for CPU and memory for as long as a warm instance stays alive, so idle model state becomes a direct cost. The authors serve neural text-to-speech (TTS) on serverless CPUs while optimizing billed CPU-seconds and GB-seconds. Two techniques do the work: capping per-request CPU parallelism to avoid contention, and an instance lifecycle that releases inference and page-cache memory when idle. On Kokoro-82M the system cuts cost per audio-hour 4.1x relative to PyTorch, from $0.0631 to $0.0153, and reduces idle billed memory from 8.7 GB to 1.33 GB.

arXiv:2610.00063 PDF

SyntheticHLS: Building Diverse Synthetic High-Level Synthesis Datasets using LLMs

Stefan Abi-Karam, Miaoyan Zhou, Callie Hao cross-listed Training data is scarce for high-level synthesis (HLS), the approach of compiling C-like code into domain-specific hardware accelerators. SyntheticHLS uses LLMs in an iterative, feedback-guided mutation loop that turns seed designs into larger and more complex designs, steered by quantitative metrics of design complexity and design-space scalability. When a Quality-of-Results (QoR) prediction model is cross-validated across datasets, models trained on synthetic designs transfer well to common HLS benchmarks but not the other way around. The iteratively mutated designs produce the most generalizable training corpus of the datasets studied, and they cover a much broader design space than designs generated zero-shot.

arXiv:2610.00106 PDF

The Hidden Costs of 99% Accuracy: A Trustworthiness Audit of the Telco Customer Churn Benchmark

Soumyadeep Roy Studies using the IBM Telco Customer Churn benchmark routinely report accuracy above 95%. An audit of that literature finds four problems that accuracy and F1 reporting do not reveal. Applying SMOTE oversampling before the train/test split inflates churn-class F1 by 13.1 percentage points, because about 36% of the synthetic training points are interpolations of test instances. A near-redundant feature (TotalCharges) distorts TreeSHAP attributions, temperature scaling fails to calibrate class-weighted tree ensembles, and the cost-optimal decision threshold is 5-10 times lower than the F1-optimal one. The authors replicate the main findings on two other churn datasets and propose a four-part reporting checklist.

arXiv:2610.00118 PDF

When the AI Leaves the Tailorshop: Measuring What an LLM Advisor Leaves Behind in Complex Problem Solving

Robin Welsch cross-listed Two preregistered experiments (N=200 and N=198) had participants manage a simulated clothing factory with or without an LLM advisor, then measured how they performed after the advisor was withdrawn. AI-supported participants reported more confidence and understanding with less effort, and they performed better or went bankrupt less often while assisted. However, evidence of retained knowledge or independent capability was weak or mixed. Within the AI-supported groups, participants who modified the AI's recommendations more often showed better unaided performance and knowledge. The authors argue that evaluations of human-AI systems should measure users' understanding and independent capability, not only assisted performance.

arXiv:2610.00163 PDF

On-Device Commercial Intent Retrieval Under Size, Latency, and Privacy Constraints: A 3 MiB Retrieval System with Typed Egress Boundaries

Hyojung Han cross-listed The system infers commercial intent entirely on the user's device under three fixed constraints: a download under 3 MiB, inference under 20 ms at the 95th percentile, and no raw text or identifiers leaving the device. It uses a static embedding table distilled from a Korean sentence transformer and quantized to 4 bits, retrieving over a 6,020-leaf commercial taxonomy without any inference runtime. On real Korean shopping reviews it reaches 75.0% mid-category top-5 accuracy, but 83.5% when the query contains a taxonomy leaf name and only 45.2% when it does not. Its own score margin can flag low-confidence queries. Measured 95th-percentile latency is 3.7-5.1 ms on iPhones and a budget Android tablet.

arXiv:2610.00170 PDF

Benchmarking System One decision models against trained classifiers and language models for automated decision gates

Amir Rafe, Subasish Das Software that hands branching decisions to a model needs a declared option plus a probability it can threshold. This study compares typed System One decision models, which return such probabilities without generating text, against supervised classifiers and generative LLMs under matched conditions on workflow, intent, and social-science items. Which class of model ranks best depends on the conditions: small trained classifiers win when task labels are available, while most decision models beat a zero-shot entailment classifier when they are not. The study also documents calibration and option-order failure modes; for example, swapping yes and no flips 50.5 answers per hundred for the hosted model Jev. An intent-trained first stage that escalates to Jev matches its accuracy at 0.43 of its cost.

arXiv:2610.00346 PDF

AIR-LLM: Broadcasting AI Weights over Radio for Memory-Free Edge LLM Inference via RF Computing

Zhihui Gao, Tingjun Chen, Dirk Englund cross-listed Edge devices often lack the memory to store LLM weights, or spend too much energy loading them. AIR-LLM has a central radio, such as a 5G base station, broadcast the weights, and the device performs the matrix-vector multiplications of inference directly in the radio-frequency domain using RF mixers, so it never stores the weights. It uses MIMO spatial multiplexing to shorten airtime, plus an on-device precoder-postcoder pair to calibrate the device's own channel, so a single broadcast can serve any number of users. In simulations on ray-traced urban channels with LLaMA-3.1-8B, it increases WikiText-2 perplexity by 4.0% while using 157.7x less energy than FP16 and 40.4x less than weight-only quantization.

arXiv:2610.00465 PDF

When Do Biological Reasoning Models Use Their Biological Inputs?

Ada Fang, Nikitha Thoduguli, Lukas Fesser, Hanlin Zhang, Sham M. Kakade, Marinka Zitnik Biological reasoning models connect LLMs to biological foundation-model representations, and their benchmark accuracy is often taken as evidence that the LLM reasons over those inputs. The authors test this assumption in six such models across DNA, protein, and single-cell tasks, using input perturbations, evidence conflicts, linear probes, and analysis of reasoning traces. In BioReason and BioReason-Pro, the Evo2 and ESM3 inputs contribute little: shuffling the DNA barely changes accuracy, and in evidence conflicts the models follow the text 97.9% and 99.7% of the time, even though probes show the representations do encode task-relevant information. Other models such as ChatNT and CellWhisperer do use their biological inputs, and across checkpoints, higher accuracy does not mean the biological inputs contribute more.

arXiv:2610.00898 PDF

OR for AI That Does OR: Routing LLMs up the Escalator inside the OSCAR Framework

Jinzhi Bu, Haixin Tang, Huanan Zhang LLMs can translate business descriptions into optimization models, but the generated code may misstate constraints or objectives, so a solver returns an optimal solution to the wrong problem. OSCAR (Optimization modeling by Simulator, Coder, And Reviewer) uses an offline simulator certified against labeled examples of feasible and infeasible decisions to compare candidate formulations, and frames the choice of which LLMs to call and when to stop as a sequential decision problem, with conditions under which cost-ordered escalation is optimal and a prior-free competitive guarantee. On five benchmark problems, OSCAR reaches 95–100% accuracy using two small open-weight LLMs that each run on a single GPU, despite their single-attempt accuracies averaging 29% and 48%. Codex and Claude Code incur 3.1 and 5.8 times its average token cost, respectively.

arXiv:2610.00912 PDF

Fold'EM: Direct atomic structure inference from Cryo-EM particles

Advaith Maddipatla, M\"art-Erik M\"aeots, Marco Pegoraro, Nikolaus Dr\"ager, Roberto Covino, Sanketh Vedula et al. cross-listed Single-particle cryo-electron microscopy (cryo-EM) usually first reconstructs an electrostatic potential (ESP) map from many particle images and then fits an atomic model to it, which needs a lot of data and gets harder as map resolution drops. Fold'EM is an inference-time framework that combines priors from protein generative models directly with the raw particle images, so it determines atomic structures from a small number of particle images and skips both map reconstruction and model building. On synthetic and experimental datasets it recovers accurate structures both when particle orientations are known and when they are inferred jointly with the structure. On heterogeneous samples it also separates distinct conformational states from mixed particle populations.

arXiv:2610.01358 PDF

A Multi-Agent LLM Framework for Personalized Health Checkup Interpretation and Guidance

HyungJun Kim, Taehan Lee, Soojin Cheon A multi-agent LLM system interprets personal health checkup results by detecting each intent in a user query, routing it to a task-specific agent, running the agents in parallel, and combining their outputs. On 120 Korean compound queries over synthetic records, the multi-agent setup raised the weighted LLM-judge score from 1.695 to 1.797, consistent across four judges, and two human evaluators preferred it in about two-thirds of pairwise comparisons. The gains came from usefulness, consistency, and covering every requirement in a query, while medical safety did not differ and critical failures stayed similar (15.0% versus 13.3%). The multi-agent system cost 2.02x more and was 1.31x slower.

arXiv:2610.01451 PDF

Neither Black nor White: Balancing Semantic and Collaborative Signals with Graph-Informed Semantic IDs (GrIS)

Aleksei Medvedev, Alejandro Ariza-Casabona, Steven Derby, Gonzalo Fiz Pontiveros, Xinyang Shao, Florian Spiess Generative recommenders represent items with Semantic IDs (SIDs), which are usually built by quantizing content embeddings. The authors reframe SID construction as recursive clustering of a graph whose nodes carry item content and whose edges carry collaborative (user co-interaction) signal, so that assigning SIDs becomes hierarchical graph partitioning. The resulting framework, GrIS, recovers RQ-VAE and RQ-KMeans as the special case of an empty graph. It is instantiated as RecDMoN, which uses differentiable graph pooling, and RQ-GAE, a graph-aware extension of RQ-VAE. On several real-world datasets it beats collaborative-filtering-aware baselines by up to +52% Hit@10.

arXiv:2610.01533 PDF

pCoMole: Pareto-Constrained Molecule Editing with Discrete Flows

Tong Chen, Maximilian Holsman, Lin Zhao, Pranam Chatterjee Biomolecular therapeutics are often developed by editing a known sequence to improve several properties at once under hard biochemical and manufacturability constraints, something existing generative methods do not jointly support in discrete, variable-length spaces. pCoMole builds on discrete flow matching, steering a pre-trained Edit Flow toward user preferences via an augmented Tchebycheff utility and a Doob-h transform, with the required harmonic function approximated by short Monte Carlo rollouts over candidate edits. Demonstrations include shrinking green fluorescent protein, shortening Cas9 orthologs while preserving PAM specificity, and compressing peptide binders under seven drug-related objectives; in wet lab tests, two 229-residue designed eGFP variants retained clear green fluorescence in BL21 cells after 10 deletions.

arXiv:2610.01663 PDF

Learning PDE Dynamics between Submanifolds Using Green's Observation Operators

Jan Tauberschmidt, Jephte Abijuru, Samuel Okon, Naukshatro Bose, Sophie Fellenz, Marius Kloft et al. Many physical systems are driven and measured only on lower-dimensional surfaces inside a larger domain, yet full-domain solvers recompute the whole volume for every new source and black-box surrogates ignore that the surrounding medium stays fixed. The Green's Observation Operator (GObO) maps the ambient medium once to the Green's kernel of a linear PDE restricted to the source and observation submanifolds, so each new source costs one lower-dimensional integral and no network evaluation, with exponential kernel decay giving a finite streaming state and horizon-independent memory. On three-dimensional heat conduction and advection-diffusion, training on static sources and predicting moving sources zero-shot gives 4-8x lower error than black-box surrogates at 1.4 ms per query, and the kernel transfers across resolutions and tolerates mild nonlinearities without retraining.

arXiv:2610.01697 PDF

Learnt Attacks on Quantum Key Distribution under Channel Noise and Device Drift

Marcel Mordarski, Benjamin Gras, Abdelrahman Shehata, Daniel Budina, Roberto Bondesan cross-listed Security analyses of quantum key distribution (QKD) assume a stationary channel, but real devices drift between recalibrations. The authors model an eavesdropper who tracks that drift as a constrained Markov decision process: noise follows an Ornstein-Uhlenbeck process, and the attacker picks one attack circuit per round from a set found by jointly searching gate structure and rotation angles. On device-independent E91 under depolarising noise, a reinforcement-learning attacker raises her Holevo information from 0.135 with the best fixed circuit to 0.348 at zero detection, 98% of the upper bound. On BB84 with a drifting bit-flip channel she reaches 99% of the bound, and the search also rediscovers known analytical cloners.

arXiv:2610.01792 PDF

From Network Intrusion Detection to Blockchain-Backed Endpoint Detection and Response: Mapping the Landscape of Decentralized Detection-and-Response Architectures

Yahya Shahsavari, Sara Rouhani, Kaiwen Zhang cross-listed This Systematization of Knowledge (SoK) surveys blockchain-assisted intrusion detection and response research from 2019 to 2026, extending earlier reviews to cover Endpoint Detection and Response (EDR) and Extended Detection and Response (XDR) architectures. It proposes a three-axis taxonomy covering detection-system class (network-based, host-based, or EDR/XDR), the functional role blockchain plays, and how mature the automated response is. The gap analysis finds that a genuine per-endpoint, blockchain-anchored response loop is nearly nonexistent, which the authors attribute to consensus latency, deployment constraints, and mismatches between research communities. The paper also covers post-quantum cryptographic risk, smart-contract attack surfaces, and adversarial attacks on LLM-based detectors, and it outlines a research agenda built around hybrid on-chain and off-chain orchestration.

arXiv:2610.01872 PDF

FastCI: Efficient GPU-Intensive CI for LLM Training Frameworks

Tianshuo Qiao, Naiqian Zheng, Xiaopeng Liu, Shuguang Wang, Diandian Gu, Xuanzhe Liu et al. Continuous integration (CI) for LLM training frameworks depends on GPU-heavy tests that often run full training or evaluation, which makes CI a development bottleneck. FastCI uses runtime evidence to select the tests affected by a change and to drop tests that run the changed code in equivalent contexts. It then runs high-risk tests first and trims each test's workload along dimensions outside what that test is meant to validate. On ByteDance's production training framework, FastCI cuts CI latency by 77.5% and GPU usage by 63.9% while slightly improving coverage of modified code, and it is now deployed there.

arXiv:2610.01967 PDF

HydroJEV: A one-second, training-free screen for cyber-attack and fault attribution in water distribution networks

Tianwei Mu, Shengyan Jiang, Mingzhe Yuan, Qing Luo, Min Xiao, Wenhong Wang et al. When an alarm fires in a water distribution network's supervisory control and data acquisition (SCADA) system, operators must quickly tell a cyberattack from a physical fault, a normal transient, or a faulty sensor, yet labeled incidents are scarce and frontier LLMs take tens of seconds per decision. The authors evaluate Jev, a training-free model that returns class probabilities in about one second, against a rule tree, a supervised classifier, and seven cloud LLMs in four pre-registered rounds on an EPANET C-Town benchmark. Jev matches the rule tree in distribution, beats the supervised classifier by 0.36-0.42 macro-F1 on unseen event subtypes, and runs 20-40 times faster than frontier LLMs. A cascade that accepts benign Jev verdicts confirmed by the rule tree spares an LLM reviewer 35-38% of windows without losing macro-F1, and it holds up when transferred unchanged to two further networks.

arXiv:2610.02048 PDF

KaliBench: A Fine-Grained Benchmark for Cybersecurity Tool Use on Kali Linux with Runtime-Free Verifiable Rewards

Pengfei Li, Naufal Suryanto, Sicheng Zhang, Muzammal Naseer KaliBench measures how well large language models (LLMs) turn an analyst's intent into exact command-line invocations of cybersecurity tools, where one wrong flag or misplaced argument can break execution. It contains 8,504 query-command pairs covering 1,642 Kali Linux tools across 23 capability dimensions and 5 security phases. The pairs were verified with LLM checks, sandboxed execution, and human review, and deterministic canonicalization makes the scores reproducible and usable as rewards without running the commands. Across 24 configurations of open-weight models, none exceeds 42% exact-command accuracy in the unrestricted setting. Supervised fine-tuning and reinforcement learning with KaliBench-derived rewards bring an 8B model to performance comparable to a 685B mixture-of-experts model.

arXiv:2610.02206 PDF

124 more specialized papers

Large Language Models 117

On-Device Named-Entity Recognition: A Deployability Study of Accuracy, Cost, Reliability, and Confidence

Vinay Kumar Chaganti This deployability study of on-device named-entity recognition (NER) compares nine systems from 13M to 8B parameters: the spaCy tagger, GLiNER encoders, and locally run Qwen3 and DeepSeek-R1 models. The evaluation covers accuracy, latency, and output validity across three datasets. The source of the gold labels matters: moving from LLM-judged silver labels to human gold flips the ranking in favor of the encoders. A 4B instruct LLM is competitive on accuracy, but GLiNER matches or slightly trails it at 1/9 to 1/24 the size, with millisecond-to-second latency and zero malformed outputs, while the smallest generative models emit up to 27% invalid output on long inputs. GLiNER's confidence ranks correctness well but is overconfident, and temperature scaling roughly halves that calibration error.

arXiv:2610.00007 PDF

Fast Polynomial Transcendentals for LLMs

Robert Hu Newer GPUs scale matrix throughput faster than their special-function units, so activations such as sigmoid, tanh and SiLU can become bottlenecks. The authors replace these with short degree-3 or degree-4 polynomial programs built from fused multiply-add operations and fused into the consuming kernels on NVIDIA GB200. In isolation the polynomial paths run 1.19–2.19x faster when data sits in L2 cache. In full training they raise step throughput by up to 8.0% for routed-expert SwiGLU, with final training-loss differences of −0.107 to +0.079 against the native functions after about 100 billion tokens.

arXiv:2610.00049 PDF

Format-Aware Fusion for Fast FP4 Pretraining

Robert Hu Four-bit floating-point (FP4) Tensor Cores speed up matrix multiplication, but the overhead of computing scales, packing operands and saving backward state can cancel out the gain. The authors propose format-aware fusion, which designs each quantization step together with its scale format and the layout its consumer expects, and test it on MXFP4 and NVFP4 while pretraining a Llama-3-family 8B model on 160 billion tokens. Their fastest route reaches 37.9K tokens/s/GPU, against 18.8K for bfloat16 and 27.6K for Transformer Engine NVFP4. The MXFP4 recipe finishes with a training loss 2.11% higher than bfloat16, and downstream benchmark rankings do not always match training-loss rankings.

arXiv:2610.00053 PDF

The First Token Is Not the Verdict: Hidden Costs of Reading LLM Judges Without Generating

Gnaneswar Villuri, Hashmath Shaik, Alex Doboli Evaluation harnesses often read an LLM judge's verdict straight from the logits of its first token, without letting it generate. This readout always overstates position bias because judges frequently do not open with a verdict token: 12–49% of the time for three Qwen3 judges, and under 3% for Llama-3.1-8B and Phi-3.5-mini. On those pairs the forced read mostly picks whichever response appeared first, and it flips on 89.7% of them when the order is swapped, against 47.5% when the verdict is read after generation. Measured judge accuracy barely changes, so the distortion mainly misleads people auditing a judge. The authors recommend reporting how often a judge leads with a verdict token, which costs one forward pass and needs no labels.

arXiv:2610.00054 PDF

"very likely" Means "uncertain"? How LLMs Diverge from Humans in Linguistic Uncertainty Quantification

Jinhao Duan, Zicheng Liu, Zijie Liu, Kaidi Xu, Tianlong Chen Humans express uncertainty with verbal markers such as "possible" or "likely", but most LLM uncertainty quantification (UQ) relies on costly likelihood- or consistency-based signals. The authors compile a corpus of human uncertainty markers from the psychology and decision-science literature and find that LLMs attach numerical confidence levels to these markers that differ substantially from human readings. They also introduce an optimization-based algorithm that learns how much probability each marker should convey by fitting it to the model's empirical correctness, with no repeated sampling. This enables a marker-by-marker comparison of how humans and LLMs use confidence language.

arXiv:2610.00083 PDF

Metric-Construction Coupling Inflates Measured Synthetic Dialect Recovery

Hyojung Han (ThakiCloud) Using Korean dialect data that cannot be redistributed, the authors measure how much of the benefit of real supervision synthetic data can recover, and introduce KoDialectBench (1,000 items, released as hashes and scoring code). Recovery varies by task, reaching 91.2% of the real-data gain on region identification but 63.7% on comprehension. Generation metrics based on a dialect-marker lexicon report recovery above 100%, but their scoring inventory is entirely contained in what the synthesis rules can produce. When 20% of marker types are withheld from the rules, measured dialectness recovery drops from 91.8% to 8.1%, while pipeline-independent measures do not drop at all. The authors conclude that sharing construction and evaluation inventories can substantially inflate estimates of how much synthetic data recovers.

arXiv:2610.00164 PDF

Conflicting Supervision Moves Commitment, Not Capability: A 12.29{\sigma} arrangement effect that is exactly zero under a convention-agnostic score

Wenhui Chen When a model is trained on the same problems written under two incompatible but correct conventions, the order of that data affects which convention it adopts. The authors prove a bound in which data arrangement and learning-rate schedule enter as separate factors: a constant schedule exposes large ordering effects, while a decaying cosine schedule largely averages them out. Across ten orderings of one corpus, the arrangement shifts which convention the model commits to with a 12.29-sigma effect, yet the combined accuracy on both conventions stays nearly constant, so exact-match benchmarks cannot see the shift. Marking the convention in the prompt removes the effect and reaches 87.5% of the achievable ceiling.

arXiv:2610.00234 PDF

Attention Manifolds: Steering or Blocking Language Models by Editing Learned B-Spline Surfaces

Naveen Mysore Standard attention sends the same value vector from a source token to every receiver. This work adds attention manifolds, learned 2D B-spline surfaces that adjust each value dimension according to the query-key interaction. The surfaces are initialized to zero so pretrained behavior is preserved. On LLaMA 3.2-1B-Instruct and 3B-Instruct they lower WikiText-2 perplexity by 2–2.5 points with 0.3% parameter overhead and change greedy outputs most on ambiguous prompts. The surfaces can also be edited directly: inverting coefficients steers generation, and setting them to −1 creates walls that block value flow, which in a preliminary test redirected a harmful-instruction prompt toward general content.

arXiv:2610.00257 PDF

CAST: Cost-Aware Speculative Trees from One-Pass Block Drafters

Jungseob Lee, Sugyeong Eo Block drafters for speculative decoding score a whole block of future tokens in one pass, but standard decoding verifies only the top chain and throws the other already-scored candidates away. CAST (Cost-Aware Speculative Trees) packs those candidates into a tree that the target model verifies in a single pass, without changing the target model, the drafter, or the decoding rule. It keeps adding candidates while the expected gain outweighs the verification time measured on the actual deployment. Across five domains, three GPU generations, and two model families, CAST beats the standard chain in all eight settings, by up to 43%, and the authors prove it leaves the target output distribution unchanged under both greedy and sampled decoding.

arXiv:2610.00321 PDF

MoRA: MoE Pruning via Router Bias Learning and Expert Approximation

Yushuai Sun, Zikun Zhou, Lin Gao, Jun Yu, Wenjie Pei Mixture-of-Experts (MoE) models only activate a few experts per token, but deploying them still means loading every expert into memory. MoRA reduces that memory by removing experts. It learns a bias for each expert's router weight, trained with the language-modeling loss plus a routing-diversity regularizer, which picks out experts that matter for performance and favors keeping experts with different routing preferences. After pruning, an expert approximation step uses affine transformations of the remaining experts to stand in for the removed ones. With 25% and 50% of routed experts removed from Qwen3-30B-A3B, DeepSeek-V2-Lite, and Moonlight-16B-A3B, MoRA outperforms state-of-the-art pruning methods across nine zero-shot benchmarks.

arXiv:2610.00367 PDF

Forking: Sudden Overfitting Under Replay

Shanbin Yu, Shaoyang Guo, Haoran Zhao, Danni Yu, Ziming Liu The authors name and study "forking," a generalization failure first found during agent-driven automated research on NanoGPT. When training data is replayed, models with an n-gram memory branch show training and validation loss splitting sharply apart at each epoch boundary. The effect also appears in a DeepSeek-style model with Engram. Repeated updates sharpen the continuations seen in training and suppress unseen ones, the n-gram module amplifies this, and rare contexts account for most of the gap. Forking also appears in short, heavily repeated SFT and RL-like training, and the authors present it as a warning about adopting tricks proposed by automated research agents.

arXiv:2610.00394 PDF

EchoPress: Query-Agnostic KV Cache Pruning via Virtual Context Reconstruction

Jiawei Lin, Saibo Geng, Thomas Bourgeat KV cache pruning reduces long-context memory by evicting less important key-value pairs. KVzip scores importance by having the model repeat the context chunk by chunk, which works well but costs extra forward passes. EchoPress is a training-free method that approximates those reconstruction scores using the queries and keys already computed during prefill, and reconstructs only the first chunk to calibrate scores for the rest of the context. On LongBench and RULER with Qwen3-8B and Llama-3.1-8B-Instruct, it matches KVzip's accuracy at eviction ratios from 50% to 90%. It cuts compression overhead by 1.7-19.6x and total prefill time by up to 2.9x.

arXiv:2610.00412 PDF

Benchmarking Prompt Optimization of Large Language Models With Chess

Timoth\'ee Lesort, Alejandra L\'opez de Aberasturi G\'omez, Tristan Karch, Tom Veniat, Philippe Modard, Karl Tuyls et al. Studying automatic prompt optimization (APO) requires a benchmark that is cheap and deterministic to score, hard enough to leave room for improvement, and renewable as models advance. The authors build one from 1,118 Lichess puzzles, scored by exact match and engine evaluation, and connect puzzle gains to short game-play rollouts. They evaluate six APO algorithms on eight frozen models and measure how much each model responds to optimization and whether optimized prompts transfer across models and to game play. The benchmark is hard, with the strongest model, Gemini 3.5 Flash, solving only about 55% of puzzles. Results vary by method and model, including both gains and regressions, and the full study costs around $800.

arXiv:2610.00416 PDF

CommunityKV: Efficient Long-Context Decoding via Graph Partitioning

Joe McKenna, Anastasios Alexandridis, Nathan Susanj, Jing Liu Long-context decoding is limited by the quadratic cost of attention and by the growing key-value (KV) cache. Training-free sparse attention methods either use coarse heuristics or rely on clustering that is expensive to update during generation. CommunityKV builds a token graph from the query-key attention scores already computed during prefill, partitions it into communities, and at each decoding step retrieves only semantically coherent groups of tokens. A constant-time local rule assigns newly generated tokens to communities, so the graph never has to be fully re-partitioned. On Qwen3 and Llama-3.1 across three long-context benchmarks, it reaches up to 1.25x the generation throughput of dense attention with one graph per head, and up to 1.71x with query-group aggregation, at comparable accuracy.

arXiv:2610.00418 PDF

The Life Cycle of a Massive Activation: Stochastic Birth, Weight-Decay-Driven Growth, and Competitive Consolidation

S. Aaron McClendon, Jorge Gallego-Feliciano, Antonios Saravanos Massive activations are residual-stream channels in transformers with values far larger than typical activations, and they are associated with attention sinks. Using training-trajectory analysis and controlled interventions, the authors find that the channels carrying these sinks vary across random seeds, settle early within each run, and later concentrate onto a few redundant channels. Their central result is that weight decay causally controls when overall activation scale peaks and then declines: without decay, the scale keeps rising. A model in which AdamW-preconditioned growth balances against decay predicts peak timing that shifts log-linearly with the decay coefficient and peak magnitude that scales roughly as its inverse square root.

arXiv:2610.00423 PDF

IrekoGPT: Turning Structured Pruning into Post-Hoc Slimmable LLMs

Pietro Moriello, Pietro Buzzega, Angelo Porrello, Simone Calderara IrekoGPT converts an already-trained LLM into a slimmable model whose width can be changed at inference time. It builds on SliceGPT but keeps the projection matrices instead of pruning them, so a single model contains nested subnetworks of different widths. Each layer is calibrated across multiple compression ratios, and downstream linear layers are corrected with gradient-free ridge regression. Preliminary results on Llama and Qwen models improve on naive PCA-based slimming, with the largest gains at high compression.

arXiv:2610.00426 PDF

XOR-Trellis: Ultra-Low-Complexity Dequantization and Curvature-Aware Hadamard-Free LLM Quantization

Xiaofan Que, Nir Elkayam, Spandan Pyakurel, Shuokai Pan, Dibakar Gope Trellis-coded quantization compresses LLM weights to very low bit widths without huge codebooks, but in practice it faces two problems: slow weight reconstruction during inference and a reliance on costly Hadamard-based incoherence transforms. XOR-Trellis adds a dequantizer with a structured, hardware-efficient state-to-value mapping that still offers diverse reconstruction choices. It also adds a curvature-aware trellis path optimization objective that accounts for model sensitivity directly in the original weight coordinates. Together, these enable ultra-low-bit quantization with cheap, highly parallel dequantization and no Hadamard preprocessing.

arXiv:2610.00432 PDF

Every Batch Is Its Own Validation Set: Leave-One-Out Gradient Matching for Online Data Selection in LLM Fine-Tuning

Hongyu Chen, Xinyi Luo, Ming Zhao, Lin Tang, Zihan Xu, Jing Li et al. Online batch selection fine-tunes a model only on the most useful part of each batch, but selectors that match the batch gradient rarely beat training on the whole batch. The authors show why: each example counts toward its own matching target, which rewards noisy examples and makes the full batch optimal. Removing these self-terms gives a leave-one-out objective that estimates the update's error against the population gradient without bias, and the optimal weights follow each example's gradient signal-to-noise ratio. The resulting method, LOOM, computes this in the Adam preconditioner's metric during the normal backward pass and needs no held-out data. Across seven models from 1.5B to 8B parameters, it improves on full-batch training by about 2.3–2.4 points on Llama-3.1-8B and Qwen2.5-7B and outperforms validation-guided selectors such as GREATS and OPUS.

arXiv:2610.00436 PDF

Gumbel Straight Flow: Distilling Autoregressive Models into One-step Flow Maps

Yeongmin Kim, Arnaud Doucet, Andrew Campbell, Valentin De Bortoli, Thomas Mensink, David Ruhe Gumbel Straight Flow (GSF) distills a pretrained autoregressive language model into a continuous flow-map model that can generate text in one or a few steps. The authors prove that the pairing of Gumbel noise with token sequences, as induced by an autoregressive model, produces non-intersecting straight paths from noise to data. Training uses a flow-map semigroup objective whose velocity condition is guided directly by the autoregressive teacher. Across pretraining and downstream benchmarks, GSF outperforms current few-step language generation baselines.

arXiv:2610.00497 PDF

Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving

Haoyu Zheng, Fangcheng Fu, Binhang Yuan, Yongqiang Zhang, Liang Deng, Hao Wang et al. Scheduling requests for diffusion LLMs (dLLMs) requires estimating the cost of each request. Common proxies fail here: output length ignores that one forward pass can unmask several tokens, and step counts ignore that steps differ in cost. The Denoising Workload Surface (DWS) instead represents a request's cost as a two-dimensional probability surface over output blocks and the denoising steps within each block. A lightweight predictor that sees only the prompt estimates this surface on a single CPU core and transfers across hardware without retraining. In real serving experiments, DWS cuts cost-prediction error by up to 2.50x, and a shortest-job-first scheduler guided by it reduces end-to-end latency by up to 1.92x for online chatbots.

arXiv:2610.00499 PDF

Rules Amortize, Pairings Don't: Linguistic Structure Determines What Latent Task Representations Can Replace In-Context Learning

Gunmay Jhingran In-context learning (ICL) can sometimes be compressed into latent task vectors that reproduce few-shot behavior at zero-shot cost, but theory suggests such vectors fail on high-rank mappings. The authors train a 2.6M-parameter network that reads the geometry of a cached few-shot support set and writes an input-conditioned update into the residual stream of a frozen GPT-2-large or GPT-2-XL. On rule-like forward inflection, it matches 10-shot ICL at zero per-query prompt cost. On lemmatization, which the model can perform but few-shot prompts fail to convey, it exceeds ICL by up to 72 points (0.85 versus 0.13), while on arbitrary word pairings such as antonyms every amortizer plateaus near half of ICL accuracy.

arXiv:2610.00526 PDF

Science or Slop?: Benchmarking and Mitigating Scientific Slop in AI-Generated Papers

Yerim Oh, Young-Jun Lee, Jaewoo Ahn, Gunhee Kim, Dongyeop Kang AI-generated scientific papers can look plausible section by section while the reasoning that connects the sections breaks down, and token-based AI-text detectors miss this. The authors define six measures of this scientific slop across structure, argument, and artifacts, and build SciSlopBench, which pairs 390 AI-generated papers with human-written papers matched by research problem and contribution type. The measures identify the AI paper in each pair with 85.9% accuracy, versus 68.7% for the Binoculars detector, and higher slop scores go with lower ICLR ratings. Directly optimizing the measures triggers reward hacking, so they propose SciSlopHarness, which lets a fixed LLM revise a paper only where the experiment records support the change and closes 63% of the remaining AI-human gap compared with the strongest revision baseline.

arXiv:2610.00531 PDF

Assessing the Impact of Language Disparity on Multilingual Linguistic Ability in Large Language Models

Zhanyu Chen, Jaap Jumelet Claims about the grammatical competence of multilingual language models vary with how competence is measured, and the interaction between evaluation method, post-training, and language resource levels has not been studied systematically. The authors evaluate base and post-trained models from six families on MultiBLiMP, a syntactic minimal-pair benchmark covering 101 languages, using four evaluation methods. Post-training degrades grammatical competence, and low-resource languages bear the largest cost. Post-trained models also retain knowledge they cannot express through explicit prompting, and prompting in the native language recovers hidden competence in low-resource languages.

arXiv:2610.00540 PDF

Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimization

Zheng Lin, Shaoke Fang, Yuxin Zhang, Jinfeng Xu, Zihan Fang, Zhe Chen et al. Mixture-of-Experts (MoE) models activate only a few experts per token but still need memory for all of them, so deployment often relies on keeping only a subset. Existing pruning methods rank experts one at a time and ignore how experts interact through the gating network. DS-MoE uses a second-order expansion of the loss to separate redundancy (experts that encode overlapping representations) from synergy (experts whose errors cancel), formulates expert selection as a difference-of-submodular optimization, and solves it with a majorization-minimization algorithm that has monotonicity guarantees. In experiments, it preserves important expert combinations and outperforms state-of-the-art expert selection baselines.

arXiv:2610.00558 PDF

Can LLMs Reason Over Long Horizons? An Empirical Evaluation of Context Strategies for Longitudinal Clinical Reasoning

Taye Akinrele, Noorbakhsh Amiri Golilarz, Subash Neupane, Sudip Mittal, Shahram Rahimi Longitudinal clinical reasoning requires LLMs to find and combine evidence scattered across long patient histories, and supplying more history does not necessarily help. The authors compare five context strategies (Full, Recent, Episodic, Semantic, and Hybrid) on MedLoCoMo with four open-weight LLMs. Episodic and Hybrid context give the best accuracy and stay strongest when the supporting evidence is far back in the history, while Recent degrades the most. Strong accuracy on answerable questions did not carry over to correctly abstaining when the history does not support the question's premise.

arXiv:2610.00562 PDF

Where's Waldo? Query-language Preference under Cross-lingual Knowledge Disparities

Dayeon Ki, Ruochen Zhang, Silviu Cucerzan, Ryen W. White, Ning Gao LLMs that synthesize multilingual sources tend to favor sources in the query's language, and this matters most when language editions disagree or omit facts. The authors build Waldo, a 12K-pair question-answering benchmark from Wikipedia covering facts missing from one language edition and facts that conflict across editions. Across eight models and five languages, models generally draw on whichever language has a missing fact, but under conflicting accounts their answers strongly follow the document in the query language, so the same question asked in different languages gets different answers. Ablating attention heads linked to this preference and LoRA-based training both mitigate it, with LoRA reducing the preference gap by up to 61.5%.

arXiv:2610.00606 PDF

Closing the Loop: Practical Training Recipes for Looped Language Models

Andrei Marchenko, Viacheslav Bezrukov, Oleg Kashurin, Inessa Fedorova, Dmitry Bocharov, Yuliana Shakhvalieva et al. Looped language models apply a shared block of layers repeatedly to gain effective depth, but existing recipes need multi-stage training over trillions of tokens. The authors present a from-scratch pipeline that cuts the budget from 7.7T tokens (as used for Ouro) to 310B. Their 1.4B looped model outperforms a parameter-matched dense model trained on the same data on all 12 benchmarks, including +14 points on GSM8K and +22 on DROP, and at matched inference compute it approaches a 3.9B dense model. They also introduce a minimal recipe for converting pretrained dense models into looped ones, using a single learned input-mixing scalar and a smoothed exit loss; applied to Qwen3-1.7B-Base, it improves on an identically continued dense baseline on every benchmark tested.

arXiv:2610.00673 PDF

Bayesian Fine-tuning Yields Language Models that are as Bayesian as their Beliefs Allow

Polina Tsvilodub, Andreas Waldis, Linlu Qiu, Tal Linzen, Michael Franke Fine-tuning a language model (LM) on the outputs of an optimal Bayesian model produces near-Bayesian behavior, while standard supervised fine-tuning (SFT) on the true answers does not. The authors ask why. They define increasingly strict tests for whether an LM counts as a Bayesian decision maker, covering its behavior, its internal representations, and its computations, and apply them to a flight recommendation task. The Bayes-trained LM encodes the quantities of Bayes' rule in its middle layers and partly uses those beliefs to make its recommendations. The oracle-trained LM differs both in the beliefs it holds and in whether it acts on them, and swapping beliefs between the two models transfers part of the Bayesian advantage.

arXiv:2610.00679 PDF

Leto: Fast In-Place Recovery for LLM Training on Surviving Hardware

Geon-Woo Kim, Joon Ha Kim, Daehyeok Kim cross-listed Hardware-operable failures (HOFs) interrupt large language model training but leave the hardware usable, yet current recovery systems still reload checkpoints and rebuild process state while GPUs sit idle. Leto keeps the working model state and reusable process state on the surviving hardware and pre-initializes the remaining state in a shadow trainer. Two-tier erasure protection and chunk-level transactional updates keep the retained model state recoverable and consistent. On 6- and 72-GPU A100 clusters, Leto recovers 3.6-6.5 times faster than the best checkpointing baselines and raises productive training time by up to 13.7 percentage points, and simulation projects over 95% productive time on a 131,072-GPU cluster.

arXiv:2610.00687 PDF

Towards Robust Numerical Claim Verification

Peter R{\o}ysland Aarnes, Vinay Setty LLMs used for claim verification are brittle on numbers: small changes to a value can sharply reduce accuracy, and the authors show this holds even for frontier models. Adversarial parameter-efficient fine-tuning on numerically perturbed examples fixes much of it. Small Qwen3 models (0.6B-8B) reach 98.7% accuracy on label-flipping perturbations, against 74.0% for GPT-5.4 Pro and 73.9% for Gemini 2.5 Flash. The gains carry over to unseen perturbation types and to Spanish without target-domain data, and the same recipe adds robustness to evidence-side perturbations on VitaminC.

arXiv:2610.00689 PDF

How Divergence Becomes Decision Flips in Compressed Language Models

Beatriz Almeida Felicio Compression reports usually quantify how far a compressed language model has drifted with KL divergence, but deployments need to know how many output decisions changed. Across 802 compressed and perturbed copies of 19 open models, the authors show that total variation tracks the rate at which the top token changes (the flip rate) at a median ratio of 1.05 with no fitted constant. KL relates to flips only through a factor that varies fourfold across settings, so it ranks compressors wrongly in 11% of comparisons, against 1% for total variation. In vLLM speculative decoding, total variation predicts greedy draft acceptance within 1.1-2.4% mean relative error without the task-specific calibration that KL needs.

arXiv:2610.00694 PDF

Sequential Functional Structured Tucker Compression for Large Language Model Attentions

Jiangfeng Chen, Xinyu Wang, Tianshuo Yan, Hanwei Wu, Xiao-Wen Chang, Yang Zhang et al. Post-training compression of LLM attention usually approximates each weight matrix on its own. That ignores the structure the attention projections share and the representation shift caused by compressing earlier layers. FTC compresses layers sequentially, fitting each approximation to the already-compressed model, and jointly exploits the native query/key/value head structure under a fixed storage budget. The output projection is handled separately, and no fine-tuning or gradient-based recovery is needed. On seven decoder-only LLMs from 6B to 32B parameters, FTC gets the lowest WikiText-2 perplexity at every tested keep ratio on five modern grouped-query attention (GQA) models, with the largest gains under aggressive compression and improvements that carry over to downstream tasks.

arXiv:2610.00717 PDF

Effective Synthetic Data Curation Requires Group-Level Signals

Cathy Jiao, Chenyan Xiong Training on synthetic data at scale can degrade model generation, and most curation methods score each sample's training utility on its own. Across pre-training and post-training settings, the authors show that these individual-level signals cannot see how samples interact: synthetic datasets with different compositions can look identical sample by sample but differ sharply under group-level influence. Curating by group-level signals gives better downstream performance, especially in generation quality. Among widely used curation methods, only those that account for sample interactions improve over baseline, and the advantage grows as pipelines become more synthetic. The authors also offer a cheap diagnostic that picks out which groups of data most need group-level scoring, recovering much of the benefit at a fraction of the compute.

arXiv:2610.00779 PDF

Training-Aware Target Coverage for Synthetic Data Selection

Yang Ba, Michelle V. Mancenido, Rong Pan Adding more synthetic data does not reliably improve LLM fine-tuning: a useful example has to add information relevant to the target task without bringing in errors that cancel its benefit. The authors develop a linear theory of this tradeoff, which predicts where synthetic data helps, how much to add, and the marginal value of one more example. Based on it, they propose Training-Aware Target Coverage (TATC), which keeps candidates whose training effect benefits the target task and favors ones that cover target-relevant directions missing from the existing data. When selecting synthetic math solutions to fine-tune Qwen2.5-Math-1.5B-Instruct, TATC outperforms other selection methods on GSM8K across all selection budgets.

arXiv:2610.00814 PDF

Verbalized and Internal Probabilities Are Coupled in Large Language Models

Sinead Williamson, Jiaxuan Li, Nick Foti, Russ Webb, Masha Fedzechkina LLMs express uncertainty in two ways: through the probabilities in their sampling distribution, and through confidence they state in words or numbers when asked. Whether the two agree in general had been unclear. The authors find out by manipulating the sources of uncertainty in both training data and in-context data. Both readouts respond to how often outcomes appear in the data and to explicit probability statements in it. They are also more closely aligned than tracking the same sources independently would explain, which suggests stated probabilities can be used to probe a model's internal distribution.

arXiv:2610.00827 PDF

AnyJev Technical Report

Jiamu Zhang, Tianze Yang, Yucheng Shi, Evan Chen, Zixiang Nie, Kelly Wan et al. AnyJev turns a pretrained instruction-tuned LLM into a classifier over a fixed set of options. It reads a probability for each option from a single prefill by restricting the next-token distribution to the option tokens. Without any training, it corrects two biases: it divides out a label prior estimated from unlabelled inputs, and it averages log-probabilities over cyclic rotations of the option list to remove position bias. On two 20-option tasks, the rotations cut the order-flip rate from 0.33 to 0.14 and 0.18 and improved accuracy on all 11 models. A stopping rule that ends the rotations early lets it serve 2.2 times as many decisions per second on vLLM.

arXiv:2610.00831 PDF

VERITYGATE: A Four-Gate Schema-Level Faithfulness Framework and Paired Benchmark for Grounded LLM Narrations over Structured Evidence

Sachin Gupta LLM explanations built on structured evidence can sound fluent while drifting from that evidence. VERITYGATE is a checker with four gates that verifies the evidence IDs, entities, numbers, and claim types each generated claim declares; it does not fact-check the full prose. Across 900 instances per setting with GPT-4o-mini, Llama-3.3-70B, and Claude Sonnet 4.6, 80.3% of GPT-4o-mini claims and 47.9% of Sonnet claims fail the checks before repair. One repair pass raises survival only modestly and can lower the number of verified claims per example, so the authors argue that survival rate and output volume should be reported together.

arXiv:2610.00833 PDF

Match the Distribution, Not the Compute: Post-Training Multi-Token Prediction Heads

Prachi Badarayani, Aidan Jay, Chenghui Zhou, Dayquan Julienne, Yuan Gao, Tianwei Chen et al. Open multi-token prediction (MTP) models such as MiMo-7B, DeepSeek-V3, and Qwen3 train their drafting heads jointly with the backbone throughout pretraining. The authors test whether heads can instead be added afterward to a frozen reasoning model. Training three chained MTP heads on a frozen Qwen3-8B with plain cross-entropy on about 2.5B tokens of model-generated chain-of-thought matches or exceeds the expected speedup of jointly trained MiMo-7B while using 10^3–10^4× fewer MTP-training tokens. A relaxed, chain-aware verification rule adds a further 12–16% speedup without hurting task accuracy, and an adaptive controller that picks how many heads to use recovers up to 11–14% of the speedup lost with a fixed maximum draft length.

arXiv:2610.00888 PDF

Clock Diffusion: Efficient Semi-Autoregressive Continuous Diffusion Language Models

Yair Schiff, Omer Belhasin, Roy Uziel, Matan Rusanovsky, Ran Zilberstein, Marianne Arriola et al. Continuous diffusion language models have lacked variable-length generation and key-value cache support, and they trail autoregressive and discrete diffusion models in quality. Clock Diffusion assigns position-dependent noise schedules to define semi-autoregressive continuous diffusion language models, with block and sliding-window generation as special cases. The resulting Gaussian ClockDLMs achieve state-of-the-art diffusion likelihood bounds on OpenWebText, beating block semi-autoregressive discrete diffusion models, and match or exceed comparable discrete models on GSM8K after training on TinyGSM. The Cache Grab samplers adapt confidence-threshold token commitment and self-speculative decoding to further improve speed and quality.

arXiv:2610.00894 PDF

The Geometry of Contextual Relations: Language Models Address Facts by Order of Mention

Yufa Zhou The authors give an LLM a list of facts in context and measure how its hidden state shifts when a question switches from one fact's subject to another's, averaging this change into a steering direction they call the ordinal vector. They find that models represent the fact being asked about by its order of mention in the context, not by the name in the question, and formulate this as an ordinal addressing hypothesis. Across Qwen, Gemma, and Llama models from 1.5B to 32B parameters, these fact addresses are ordered by mention, can be steered (adding the ordinal vector makes the model answer with the next fact), occupy a low-rank subspace in which the first fact is easiest to reach, and emerge in late-middle layers early in pretraining.

arXiv:2610.00910 PDF

Efficient Task Adaptation in Large Language Models: A Survey of Weight-Based, Prompt-Based, and Embedding-Based Adaptations

Jungwon Park, Changin Choi, Jimyeong Kim, Nojun Kwak, Wonjong Rhee Methods for adapting large language models to downstream tasks have developed largely within separate paradigms, leaving their relationships and trade-offs underexplored. This survey proposes a unified framework that categorizes task adaptation by where task information is encoded: model weights (parameter-efficient fine-tuning), input prompts (in-context learning), or injected task embeddings. It integrates these into one taxonomy, analyzes each paradigm's strengths and limitations and how they evolved, clarifies cross-paradigm relationships with particular attention to emerging embedding-based methods, and highlights open problems.

arXiv:2610.00928 PDF

Role-aware Heuristic Episodic Attention for Conversational LLMs

Wanyang Hong, Zhaoning Zhang, Yi Chen, Libo Zhang, Baihui Liu, Linbo Qiao et al. As multi-turn conversations grow, LLMs lose track of standing instructions and earlier context, which the authors break into three failure modes: attention pollution, dilution and drift. REA (Role-aware Heuristic Episodic Attention) handles instructions and conversation history differently. Global constraints stay in a dedicated prefix, user turns are kept verbatim, model replies are compressed, and a heuristic retriever decides for each past turn whether to include the raw text, a compressed form or nothing. On Long-MT-Bench+, the judge score rises from 6.32 to 7.36 out of 10 while average latency drops 2.91x, and the gains carry over to backbones from 1.7B to 7B parameters and to Chinese and English role-playing tasks.

arXiv:2610.00958 PDF

The Devil Is in the Reconstruction Loss Scale: Rethinking Optimization in LLM Quantization

Chao Li, Shigeng Wang, Anbang Yao Learning-based post-training quantization (PTQ) of LLMs quantizes the model one unit at a time, usually one transformer block, and tunes auxiliary parameters at each stage by minimizing a mean squared error (MSE) reconstruction loss. Across many PTQ methods, model families, scales and settings, the authors find a consistent optimization imbalance. Reconstruction loss magnitudes differ sharply between stages, and MSE turns that difference into very uneven gradients and parameter updates, so some stages are optimized much harder than others. They show theoretically that root mean squared error (RMSE) variants at the sample, channel, token or element level normalize gradients implicitly, and that using RMSE as a drop-in replacement for MSE significantly improves quantized model quality.

arXiv:2610.00983 PDF

Distilling Directional Verification

Jungseob Lee, Sugyeong Eo, Seongtae Hong, Seungyoon Lee, Chanjun Park, Jaehyung Seo et al. A large language model used as a distillation teacher may recall a fact in one direction, such as a child's parent, yet fail to produce it in the reverse direction. When students are trained on the teacher's generated answers, they inherit this limitation. directional label distillation instead has frozen teachers score candidate answers in the direction they know, and the highest-scoring candidate becomes the student's training label. On parent–child facts, students trained on these labels score 13 to 15 points higher in open-ended accuracy than students trained on prior-corrected reverse-direction labels, and the advantage holds even when candidates are retrieved without the correct answer being inserted.

arXiv:2610.00997 PDF

Evaluating LLM-Generated Preference Distributions

Fan Huang, Minsuk Kim, C. Tyler Diggans, Filippo Radicchi Large language models (LLMs) are increasingly used in place of survey respondents or as generators of synthetic data, but little is known about the probability distributions they produce. The authors sample preference distributions over air travel, restaurants, and consumer products from nine open-weight models. Each model is self-consistent, and its most probable outcomes settle quickly under repeated sampling. Different model families and sizes, however, show little agreement even on their top outcomes, and the choice of model affects results more than prompt wording. These patterns hold under changes to temperature, under greedy decoding, and under perturbations of prompt and option order.

arXiv:2610.01000 PDF

Scaling and Distilling Text Embeddings for Better Diffusibility

Zekai Zhang, Yunjie Tian, Yanjin He, Xiaoyan Zhang, Dongdi Zhao, Qing Qu et al. Continuous diffusion language models (DLMs) generate text by running diffusion over text embeddings, which raises the question of which embedding space is easiest to diffuse over. The authors find that moving to stronger encoders in the same family (T5, then T5Gemma-1, then T5Gemma-2) greatly improves generation. Raw T5Gemma-2 embeddings, however, separate even plausible alternative words so sharply that imperfect sampling often lands on invalid embeddings. Distilling T5Gemma-2 into a student encoder trained on the teacher's decoded probabilities as soft labels pulls these alternatives closer together, and the resulting medium-sized DLM reaches a generative perplexity of 17.8 on OpenWebText, outperforming GPT-2-M (real text scores 15.4).

arXiv:2610.01016 PDF

Capturing In-Context Learning Dynamics with Task Operators

Guangzhi Xiong, Zhenghao He, Bohan Liu, Sanchit Sinha, Wenqian Ye, Aidong Zhang In-context learning (ICL) requires reprocessing every demonstration at inference time. Prior methods that compress ICL into fixed activation vectors fail on tasks where the output depends on detailed interaction with the input. The authors show that each attention head's output under ICL is an affine transformation of its output without the context, and that this transformation's parameters stay stable across samples of the same task. Their Task Operator (TO) replays this transformation as an analytically derived update to the attention output projection, and it achieves the best overall performance among prior compression methods across lexical, algorithmic, and reasoning tasks. The extracted knowledge concentrates in a sparse task-specific circuit, and averaging operators from separate demonstration batches allows many-shot scaling without a longer context window.

arXiv:2610.01054 PDF

Kernelized Activation Steering

Laziz U. Abdullaev, Minh-Hieu Pham, Bach Do, Khoat Than, Tan M. Nguyen Standard activation steering methods such as Difference-in-Means (DiM) add one fixed vector to every activation, which ignores the local geometry of the representation space. Kernelized Activation Steering (KAS) recasts steering as an optimization problem in a reproducing kernel Hilbert space that can be solved using only kernel evaluations. Each activation is then adjusted according to where it sits relative to the source and target reference sets, producing a nonlinear steering field. DiM turns out to be the special case of a linear kernel, and on tasks including LLM jailbreaking and image style control, KAS matches or outperforms existing methods.

arXiv:2610.01062 PDF

Probe with Participation Trophies: Random-Reward RL as a Probe of LLM Capability

Yu Mao, Lei Yu, Zining Zhu, Yusheng Zheng, Haohang Li, Freda Shi et al. Earlier work found that reinforcement learning (RL) with random rewards can still improve LLMs, and this has been attributed either to RL training mechanisms or to data contamination. The authors argue instead that spurious-reward RL measures a model's reachability, meaning what further training can achieve from its current state, which current accuracy does not reveal. For example, two OLMo checkpoints that both score 3.5% on synthetic arithmetic reach 8.5% and 55% under the same correctness-rewarded RL. Across OLMo pre-training and mid-training checkpoints there are three regimes: early on RL barely helps, later correct-answer rewards work but random rewards do not, and from mid-training onward even random rewards yield large gains. A number-masked supervised fine-tuning analysis shows a similar ordering, which suggests the pattern does not depend on a particular RL mechanism.

arXiv:2610.01066 PDF

AgSpec: Pushing the Limits of Retrieval-Based Speculative Decoding in Coding Agent Pipelines

Sumin Lee, Sukmin Cho, Suengjae Lim, Youngjin Kwon Retrieval-based speculative decoding drafts tokens by copying existing text, which fits coding agents that keep reproducing code, logs, and earlier attempts. Existing drafters, however, lack much of the relevant text and use fixed draft lengths. AgSpec retrieves from session, workspace, and global corpora, and indexes opened files in the format the agent actually emits. It caps each agent's draft length using offline profiling and adjusts it online from verification feedback. On two repository-level multi-agent coding benchmarks it outperforms five retrieval-based drafters and EAGLE-3 in most settings, raising throughput over autoregressive decoding by up to 4.37× at batch size 1 and 4.76× at batch size 16.

arXiv:2610.01108 PDF

How Much Can Language Models Gain from Test-Time Computation?

Bangji Yang, Jingyuan Li, Jiajun Fan, Yi Evie Zhang, Ruihan Guo, Hongba Ma et al. SELF-POT is a benchmark and evaluation framework that measures how much test-time computation helps a model across competition math, competitive programming, and agentic workflows. It charges every model call, including selection and critique, in dollars under a single budget rule. It compares direct inference with parallel sampling and self-revision, and tests whether cheaper models with extra inference can match a stronger model, using Claude Opus 5.5 direct inference as the reference. Across five low-cost reasoning models on 350 sealed tasks, the returns depend on the domain, the selection rule, and how failures are handled. In replays of programming candidate pools, selecting with public examples raises correct submissions from 376 to 453 of 500 while cutting API cost by 12–49%. On math pools, judging with a fallback slightly beats voting, while voting costs less.

arXiv:2610.01110 PDF

Counting and Min-Cost Encoding for Tokenization in Large Language Models

Shuming Shi, Xiang Zhang, Hao Yu, Wenbo Fei, Changjian Wang, Zhan Wang et al. For a fixed model architecture, shorter token sequences mean faster inference, so the authors propose an alternative to BPE tokenization. Counting and Filtering (CNF) builds a vocabulary by counting valid substrings and then filtering by actual usage, and Min-Cost Encoding (MCE) segments text by globally minimizing a cost function, with no merge list or token probabilities required. With a 250K vocabulary, CNF-MCE improves compression on English web text by 26% and 30% over the o200k_base and qwen250k tokenizers, and at 1M entries it gains over 60% in token efficiency while raising vocabulary utilization from 52.9% to 96.9%. Models trained from scratch at 1.8B and 8B parameters match the downstream performance of BPE-tokenized models across 11 benchmarks.

arXiv:2610.01127 PDF

Looping Beyond Twice: A Scalable Recipe for Looped Mixture-of-Experts

Di He, Pengxiang Li, Da Chang, Qingyan Meng, Lu Yin, Shiwei Liu Looped Transformers reuse shared blocks to add depth without adding parameters, but in Mixture-of-Experts (MoE) LLMs the gains fade after about two loops. The authors trace this to hidden-state variance growing with each iteration and to routers picking the same experts on every loop. LOOM addresses both by scaling residual updates, re-injecting the input embedding at each loop, using per-loop routers, and adding a looping residual that carries earlier outputs forward. Across 100M–1.7B models it scales stably to 9–12 loops: at near-equal FLOPs a 700M model is best at 5 loops (perplexity 18.36 to 16.54), and without FLOP matching a 1.7B model peaks at 9 loops (perplexity 9.62 to 7.77, zero-shot accuracy 42.4% to 47.7%).

arXiv:2610.01153 PDF

Learning Rate Transfer for Hybrid Transformer-SSM Architectures

Jimin Seo, Gyubok Lee, Yeonsik Jo, Kiwoong Yoo, Yeongoon Kim, Minhae Oh et al. Hybrid architectures that mix Transformer and State-Space Model (SSM) blocks are used in production LLMs, but theoretical learning-rate (LR) scaling rules for SSMs assume settings that differ from practical Mamba implementations. The authors find that, in practice, the original μP prescription alone yields a near-zero LR transfer gap across widths 256–2048 and depths 4–32 up to billion-parameter scale, even though every parameterization tested fails μP's standard coordinate check. They explain this by two conditions: a global update-to-weight invariance enforced by μP, and a per-component balance supplied by AdamW's per-parameter normalization. The width invariance holds across depth, sequence length, batch size, and Transformer-to-SSM ratio, and it carries over to Nemotron-H.

arXiv:2610.01172 PDF

Temporally-Resolved Token Attribution Reveals the Generation Dynamics of Diffusion Language Models

Darpan Aswal, C\'eline Hudelot Diffusion language models (DLMs) build their output over many denoising steps, which makes it hard to trace which inputs drive each step. Diffusion Layer Integrated Gradients (DLIG) extends Integrated Gradients to arbitrary layers and denoising steps, attributing the model's progressive commitment to a completion back to input tokens. The authors show DLIG maps onto the standard IG axioms of completeness, implementation invariance, linearity, and symmetry preservation. They position it as a cheap first check of mechanistic hypotheses that complements interventional analysis, and use it on word-sense disambiguation, multi-hop graph reasoning, and sentence infilling to show how DLMs draw on inputs across positions, layers, and steps.

arXiv:2610.01177 PDF

AGO AI Quality Gate: Evidence-First Release Decisions for Retrieval-Augmented Generation

Giulio Zeloni, Enrico Lo Conte, Salvatore Rionero, Giuseppe Santoro, Alessandro Rastelli, Fabio Sorrentino Teams shipping retrieval-augmented generation (RAG) systems must decide whether to promote, revise, or block each version, using incomplete evidence and metrics from fallible LLM judges. AGO AI Quality Gate, deployed in industrial assessments, treats missing data and judge errors as explicit decision states. It layers deterministic checks, guardrails, and LLM scoring, uses a stratified beta-binomial gate to estimate regression risk, and requires the judge to be validated before it can influence decisions. On RAGBench, gpt-4.1-nano as judge detects non-adherent answers barely above chance (AUROC 0.603) despite well-formed output, while gpt-4o reaches 0.783 but varies from 0.62 to 0.88 across domains. In a simulated regression, the gate cuts unsafe promotions relative to a naive gate.

arXiv:2610.01218 PDF

HHR: Hierarchical Hash Retrieval for Efficient LLM Generation

Lianjun Liu, Tiantian Zheng, You Huang, Weiqi Yan, Mingte Qiu, Huazhong Liu et al. Hash-based retrieval speeds up long-context LLM inference by encoding queries and keys as binary codes and selecting keys by Hamming distance. Binarization discards magnitude information, however, so it misses some high-scoring keys and retrieves some low-scoring ones. Hierarchical Hash Retrieval (HHR) works coarse to fine: Geometry-Aware Key Routing learns per-head orthogonal transforms that sharpen page-level bounds for pruning, and Learned Hash Projection aligns Hamming distance with true attention relevance. On LongBench, HHR raises the average score by 1.10 points, and at 128K context it delivers up to 3.30x decoding speedup and 2.83x end-to-end speedup for Llama-3.1-8B-Instruct.

arXiv:2610.01230 PDF

Learning to Ask: Information Acquisition for SLM-LLM Collaboration, under a budget

Yongjun Kim, Xiaoxiao Li, Jaeho Lee Pairing a small language model (SLM) with a large language model (LLM) is usually framed as splitting the work between them, which can be inefficient with black-box APIs because context gets resent at every handoff. The authors instead keep the SLM as the main reasoner and let it send targeted questions to a black-box LLM advisor only when needed, under an API budget. A three-stage reinforcement learning with verifiable rewards (RLVR) framework teaches the SLM when to call the advisor, how to phrase queries, and how to use the answers. On math and coding tasks, the approach improves the performance-cost tradeoff over existing collaboration baselines, in some settings matching or exceeding oracle problem-level routing, and it transfers to other advisor model families without retraining.

arXiv:2610.01236 PDF

Mixture-Trained Merging for Unified Multi-Objective Models

SeongHyeon Kim, Chaeyun Jang, Seungyoo Lee, Jiyeon Ham, Yunju Bak, Boseop Kim et al. Combining skills like math, code, instruction following and switchable thinking modes in one model usually means training on each objective in sequence, which makes the result sensitive to training order and data ratios. Merging the weights of single-objective experts is the alternative, but it often degrades domain skills or collapses think and non-think modes into one. The authors blame this on experts drifting into incompatible regions of parameter space. They propose Mixture-Trained Merging (MTM), which trains each branch on a data mixture biased toward one objective, uses evaluations of the merged model to pick mixtures, and iteratively tunes merge coefficients with qNEHVI-based multi-objective Bayesian optimization. MTM outperforms naive merging and keeps think and non-think behaviors separate where single-objective merging collapses them.

arXiv:2610.01238 PDF

Know When to Hold 'em: Correct-Token Retention in Uniform-State Diffusion Language Models

Mojtaba Nafez, James Henderson Uniform-state diffusion models (USDMs) can revise any token at any denoising step, but the authors show that current models (DUO, UDLM and uniform-noise SEDD) fail to keep tokens that are already correct, rewriting 173-270 of 512 positions at every step and collapsing sample diversity. Breaking down the training objective shows that it barely penalizes wrong predictions at clean positions. Their auxiliary loss, Correct-Token Retention Regularization (CTR-Reg), trains the model to retain unperturbed tokens with no change to the sampler. It raises clean-token accuracy by 26.5 percentage points on average and cuts per-step revisions to 3-11 positions, and with five greedy-tail steps generative perplexity more than halves while diversity is preserved.

arXiv:2610.01275 PDF

ITC-MoE: Importance-guided Token-aware Compression for MoE Diffusion Language Models

Lianjun Liu, Shipeng Li, You Huang, Weiqi Yan, Mingte Qiu, Huazhong Liu et al. Mixture-of-Experts (MoE) diffusion language models are costly to run and store because of their many expert parameters, and existing low-rank compression methods use fixed factorizations and rank allocations. ITC-MoE combines two parts. The first, Importance-guided Adaptive Tucker Compression, uses activation and gradient importance to factorize expert weights jointly across several modes and allocate ranks adaptively. The second, Token-aware Compensation and Routing, adds lightweight low-rank compensation for frequently used tokens and restricts the expert set for rarely used ones. On SDAR-30B-A3B-Chat-b32 with a 30% compression budget, it keeps 96.33% accuracy on MultiArith with up to a 7.22x end-to-end speedup.

arXiv:2610.01296 PDF

What Wins a Vote? Formatting, Length, and Lexical Diversity in the French Compar:IA LLM Arena

Simonas Zilinskas, Maayeesha Farzana, Christophe Benavent The authors analyze 137,293 decisive French-language votes from the Compar:IA LLM arena to measure how much the presentation of an answer, as opposed to its content, drives human preference. Length, bold text, and lists tend to appear together, which makes their individual effects hard to separate. The two most stable associations are bold usage (+11.0% win odds per standard deviation) and vocabulary variety measured by moving-average type-token ratio (MATTR, +16.8%). Adjusting rankings for style moves 36 of 116 models by at least ten ranks, but the adjusted rankings do not track external capability benchmarks any better than the raw ones. The authors therefore recommend publishing raw and adjusted rankings side by side.

arXiv:2610.01316 PDF

Gacha Decoding: Eliciting Diverse Generations Through Instruction Following

Scott Geng, Yufei Zhang, Joseph Lee, Jerry Li, Marjan Ghazvininejad, Pang Wei Koh Gacha Decoding is an inference-time method that gets diverse outputs from a language model by treating diversity as an instruction-following problem instead of relying on token entropy. It uses an external random number generator tool to choose distinct modes of the response space and the model's instruction following to realize them, an approach the authors call "planning with dice". Across chat, creative writing, image-generation planning, and protein design, it reaches up to 2.4x the Vendi diversity score of the next-best method at equal quality and reaches the same number of high-quality modes with 11x fewer samples. Unlike prior methods, its diversity improves as models get better at following instructions, even as their token entropy falls.

arXiv:2610.01382 PDF

AF-Muon: An AdamW-Free Muon Optimizer for Tied-Embedding Models

Arash Lagzian, Paniz Halvachi, Junming Zhang, Zhouhan Lin, Dianbo Liu The Muon optimizer normally hands parameters that are not dense matrices, notably tied vocabulary embedding tables, to an auxiliary AdamW optimizer that keeps second-moment state. AF-Muon removes AdamW entirely: it keeps Muon for hidden weight matrices, uses a support-aware finite-cap update for tied vocabulary tables, and an RMS-normalized update for one-dimensional parameters. This needs only a single first-moment buffer and saves about 20% of optimizer-state memory relative to Hybrid Muon, with about 1% step-time overhead. Across nine tied-embedding settings, including 124M to 1B decoder-only language models, a T5-style encoder-decoder, and image, protein, and sparse mixture-of-experts variants, it improves validation loss and perplexity over Hybrid Muon and a SCION-style baseline.

arXiv:2610.01395 PDF

Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMs

Nagham Omar, Mahmoud Jabarin, Maya Rozenshtein, Rom Himelstein, Avi Mendelson, Amit LeVi The authors argue that LLM generalization should be measured as stability, meaning consistent and semantically stable outputs when the same input is phrased in different ways, rather than as aggregate accuracy on a single prompt format. They introduce the Stability-Aware Generalization Objective (SAGO), which measures per example how much model behavior changes across input variants and benchmarks along several axes: generation consistency, internal activations, confidence, and response mirroring. Across commonly used models, no model generalizes uniformly: the behavioral axes capture independent failure modes, and variation across datasets can reverse model rankings.

arXiv:2610.01428 PDF

DRelay: Global Draft Context for Prefix-Aware Parallel Speculative Decoding Repair

Zhuoyu Wang, Junnan Huang, Xinyu Chen Parallel drafting reduces the drafting cost of speculative decoding for LLMs, but one early wrong token selection discards every prediction after it, even when the correct token was among the candidates. DRelay adds a global reader that pulls predictive information across all candidates in the draft block, plus a causal selector that decides, given the prefix already selected, whether to keep or replace each position's token before the target model verifies it. The draft model and selector are trained jointly, with the repair loss weighted by how much each position contributes to the accepted prefix length. Under SGLang serving on an H800 GPU, DRelay raises average end-to-end speedup by 8–17% over DFlash, Domino, and DSpark.

arXiv:2610.01439 PDF

When Does a Second Model Help? Cross-Model Review in LLM Verification

Tae-Eun Song Asks when a second review by a different large language model helps catch errors in LLM-generated code, documentation, and analyses. In a controlled experiment of 900 review sessions over 30 artifacts with 150 planted errors, a top-tier cross-model reviewer was not significantly different in F1 from a same-model review in a fresh session (CCR), and the two found partly different errors (Jaccard overlap 41.2%). With a budget of two review calls, one CCR plus one cross-model review caught 56.7% of planted errors versus 42.7% for two CCR reviews, though this did not beat two reviews by the top-tier cross-model reviewer, so model difference and reviewer capability are not separated. A lightweight cross-model reviewer scored no better than same-model review.

arXiv:2610.01471 PDF

No Model Required: Text Entropy Rate Filtering Mitigates Iterative Fine-Tuning Collapse

Lewis Mitchell Repeatedly fine-tuning a model on its own synthetic output causes model collapse, a progressive loss of output diversity. As a training-data filter, the authors use the non-parametric Kontoyiannis entropy rate estimator, which is computed from raw text match-length statistics without any model. In a six-generation QLoRA collapse experiment on Llama-3.1-8B, log-probability filtering gave no significant diversity benefit, while entropy-rate filtering yielded +42% unique trigrams, +30% vocabulary, and −19% repetition. The estimator also tracked entropy and detected collapse across four domains and 1,520 generated documents.

arXiv:2610.01493 PDF

QK-Wanda: Coupling Queries and Keys for Unstructured Pruning

Ivan Ilin, Peter Richt\'arik Wanda prunes LLM weights independently within each linear projection, even though queries and keys interact through dot products. QK-Wanda scores each query weight using information from the key projection, and vice versa, under a pre-RoPE reconstruction objective, so both projections share one pruning budget. The scores are closed-form, need no gradients, and add only 1-3% to pruning time. Across 15 models (TinyLlama, Llama 2/3, Qwen2.5; 0.5B-72B), it cuts QK reconstruction error by 45-60%, and at 80% sparsity on Llama 2 70B it lowers WikiText-2 perplexity by 20.3%. However, Llama-3.1-70B gets worse despite lower reconstruction error, which shows that local reconstruction error is a limited predictor of model quality.

arXiv:2610.01554 PDF

Which LLM to pick? Online Active Model Selection for Large Language Models

Alessandro Turrin, Patrik Okanovic, Torsten Hoefler, Nezihe Merve G\"urel Teams choosing an LLM for a stream of queries usually rely on public benchmarks, which only approximate real performance, while labeling the stream by hand is expensive. ONLINE LLM PICKER is an active model selection framework that picks the most informative incoming prompts to annotate under a limited budget, with the goal of finding the best candidate model. Across 10 datasets and more than 130 language models, it cuts annotation cost by up to 71.67% while reliably finding the best or near-best model. Using the selected model on the unannotated prompts reduces regret by up to 2.51x.

arXiv:2610.01592 PDF

What Makes Something Hard(er)? Explaining Question Difficulty in Natural Language

Peng Cui, Qiaoyuan Zheng, Rudolf Debelak, Mrinmaya Sachan Automatic estimates of question difficulty produce a single number that does not explain what makes a question hard. The authors estimate difficulty from many LLMs' responses using Item Response Theory, show an LLM contrasting sets of easy and hard questions, and keep the natural-language explanations it proposes only if they hold up on held-out questions. Across mathematical, logical, and commonsense reasoning datasets, the hypotheses alone predict difficulty about as well as or better than black-box regressors, and they improve those regressors when added as features. Rewriting questions according to a hypothesis shifts their measured difficulty in the predicted direction, which suggests the factors are causal and not just post-hoc descriptions.

arXiv:2610.01627 PDF

Invent a Dataset: Measuring dataset generation abilities with zero seed

Shivalika Singh, Andrija Djurisic, Gbemileke Onilude, Sudip Roy, Sara Hooker Dataset construction remains one of the most manual parts of AI development, and practitioners frequently start with no data at all for the capability they want to teach. Invent-A-Dataset is a prompt-based system that goes from a plain dataset description to large-scale post-training data, evaluated against five frontier model APIs from Anthropic, Google, OpenAI, DeepSeek and Zai across eight task types at sizes up to 20K samples. It reports the highest quality (17% relative gain) and the most diverse samples (19% relative gain), with the diversity advantage widening from parity at 200 samples to 37% at 20K, and fine-tunes trained on its output ranking highest across model architectures.

arXiv:2610.01674 PDF

Task-Oriented Rank Adaptation for Continual Learning in Text Classification

Rey Sanchez Lopez, Eduardo Morales Manzanares, Hugo Jair Escalante Continual learning for text classification has to fight both catastrophic forgetting and negative transfer, and parameter-efficient adapters such as LoRA are normally trained in isolation so their compact representations go unused by later tasks. Task-Oriented Rank Adaptation (TORA) is a geometric routing framework that compares the low-rank structure of existing adapters to decide whether to boost a new task from the most compatible expert or shield it in isolation. Across 15 text classification benchmarks it avoids harmful routing decisions throughout: compatible tasks beat their isolated baselines with less training time, distant tasks lose no accuracy, and it needs only a single similarity threshold with no task identities or known task order.

arXiv:2610.01702 PDF

TopK-Guided: Adaptive, Budget-Aware Activation Sparsity for Efficient LLM Inference

Mukund Agarwalla, Chih-Jen Lin Activation sparsity speeds up large language model (LLM) inference by zeroing unimportant activations so their computation can be skipped. Existing training-free methods make a trade-off: TEAL adapts sparsity per token but does not tightly control how much sparsity is actually achieved, while WINA fixes the sparsity level but gives every token the same budget. Both also use one budget for every transformer block. TopK-Guided combines bounded per-token adaptation with per-block budgets set by each block's sensitivity, and on Llama-2 and Llama-3 it improves perplexity and downstream accuracy over both methods at essentially the same compute as WINA, with the largest gains at high sparsity.

arXiv:2610.01763 PDF

A Matryoshka Hierarchical RAG for Efficient Multi-Hop Question Answering

Gianluca Bonifazi, Christopher Buratti, Michele Marchetti, Federica Parlapiano, Giulia Quaglieri, Davide Traini et al. Retrieval-Augmented Generation (RAG) for multi-hop question answering often relies on expensive knowledge graphs or LLM-written summaries at indexing time, or on repeated LLM-driven retrieval at query time. MatRAG arranges the corpus into a directed acyclic graph of clusters that become coarser at each level, and indexes each level with a smaller embedding dimension using Matryoshka Representation Learning. Retrieval walks this graph from the top down and uses entities to limit the number of hops and re-rank candidates. On three multi-hop benchmarks it beats seven baselines on retrieval quality while avoiding knowledge-graph construction and LLM summarization and reducing query-time cost.

arXiv:2610.01767 PDF

Beyond Linear Concepts: Discovering and Aligning Non-Linear Concept Manifolds in Large Language Models

Tido Specht, Elias Benedict Krey, Nils Neukirch, Nils Strodthoff Most mechanistic interpretability methods assume that concepts are linear directions, despite evidence of non-linear feature manifolds. The authors adapt Non-Linear Multi-Dimensional Concept Discovery (NLMCD) to token-level LLM activations and introduce a concept-based alignment (CBA) score, a generalized Rand index, for comparing concept manifolds across layers and models. They find block structures in intermediate and late layers that linear metrics miss, and concepts that stay syntax-dominated until later layers. English-Mandarin concept sharing proves training-dependent: strong in Qwen, weaker in Llama, absent in GPT-2. Across Tulu-3 training stages, the largest representational shift is from base model to SFT, while DPO and RLVR leave early layers largely unchanged.

arXiv:2610.01821 PDF

Where LLMs Fail with Visualization DSLs

Chang Han, Andrew McNutt, Katherine Isaacs cross-listed Visualization domain-specific languages (DSLs) were designed around what is easy for human authors, which is not necessarily what is easy for LLMs. The authors evaluate 10 JSON-style visualization DSLs on 41 charting tasks across 3 LLMs. They check the generated specifications with JSON validation and rendering, then qualitatively code the failed cases. The analysis identifies four recurring failure patterns, each tied to specific DSL features, and turns them into design considerations for DSLs that LLMs will write.

arXiv:2610.01873 PDF

Stochastic Rounding in Low-Precision Transformer Inference: A Variable-Precision Emulation Study of a Small GPT-2

Yohan Chatelain (Krembil Centre for Neuroinformatics, CAMH, Toronto, Canada), Pablo de Oliveira Castro (Universite Paris-Saclay, UVSQ et al. Whether low-precision transformer inference should use stochastic rounding (SR) or round-to-nearest (RN) turns out to depend on where in the network the rounding happens. The authors extend the PRISM rounding library to emulate arbitrary precisions and pair it with two analyses. The first is an error bound showing that SR error grows with the square root of the reduction length, versus linearly for RN, which matters most in the long MLP down-projection. The second is a loss decomposition showing that MLP rounding noise is mostly a uniform logit shift that the output softmax ignores, while noise at the output head varies across the vocabulary and is not ignored. On DistilGPT-2 with 6 significand bits, SR in the MLP gives 1.15× the full-precision perplexity versus 2.21× for RN, but the ordering reverses at the output head. Using SR in the MLP and RN at the head brings perplexity within 1.10× of full precision.

arXiv:2610.01889 PDF

Cross-Lingual Alignment for Decoder-Only Models using MoE Routers

Lucas Bandarkar, Clark Peng, Ahmed Haj Ahmed, Aditi Khandelwal, Nanyun Peng Cross-lingual contrastive alignment is standard for multilingual encoders, but differing tokenization across languages makes it hard to apply to decoder-only LLMs. The authors instead align the outputs of mixture-of-experts (MoE) routers across languages, because router outputs can be pooled over many tokens for reliable sequence-level comparison. In controlled continual pre-training of four open-source MoE models, the router alignment loss also aligns the underlying hidden representations and improves performance on a diverse multilingual evaluation suite.

arXiv:2610.01921 PDF

Learning to Predict Distributions over Weight Updates for Test-Time Adaptation

Azal Ahmad Khan, Keshav Ramji, Tahira Naseem, Ali Anwar, Ram\'on Fernandez Astudillo This work asks how much a large language model (LLM) can be adapted at runtime using only the input query. It studies query-conditioned hypernetworks that generate LoRA adapters, and introduces distributional hypernetworks that output a distribution over possible adapters instead of a single one, trained end to end with a differentiable Monte Carlo loss. The mean of the learned distribution alone can beat deterministic hypernetworks. Sampling several weight updates per query gives a new kind of test-time scaling: performance rises as more weight samples are used and stays ahead of equivalent token-sampling baselines. The generated updates also transfer across queries, which suggests the hypernetwork learns reusable structure in how the model should adapt.

arXiv:2610.01934 PDF

Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning

Meghana Sunil, Shravya V, Shravan Venkatraman, Joe Dhanith PR This survey covers recent Retrieval Augmented Generation (RAG) research that goes beyond the basic pipeline designs covered by earlier reviews. It organizes the field along four axes: retrieval efficiency, robustness and security, interactive user-driven workflows, and multi-step reasoning. The survey formalizes the main RAG components and reviews dense and sparse retrieval, fusion strategies, embedding optimizations, and retrieval policies learned with reinforcement learning. It also compares Naive, Advanced, and Modular RAG architectures, summarizes evaluation practice and domain applications, and lists open problems in retrieval quality, reliability, domain adaptation, scalability, and explainability.

arXiv:2610.01936 PDF

Universal Byte-Level Encoding: UTF-8/UTF-16 Routing to Reduce Cross-Script Token-Budget Disparities

Hyunsik Kim, Youngmoon Jung With UTF-8 byte-level byte-pair encoding (BBPE), many non-Latin scripts need more tokens than English before any merges apply. This inflates cost and shrinks the usable context window. Universal Byte-Level Encoding (UBE) keeps 1-2-byte UTF-8 characters on the UTF-8 path and routes 3-4-byte characters through UTF-16, which lowers the starting cost for high-premium scripts without making English spans more expensive. UBE decodes exactly and round-trips all of Unicode 17 and its official test suites. In multilingual experiments it matches BBPE language-model quality while reducing cross-lingual token-count disparity, giving more usable context and faster prompt processing for affected scripts.

arXiv:2610.01984 PDF

Old Ideas, Novel Problems: The Instability of LLM-Based Novelty Evaluation

Noy Sternlicht, Simra Shahid, Peter Jansen, Daniel S. Weld, Pao Siangliulue, Tom Hope Automated research-ideation systems are often judged on novelty by LLM judges that are built ad hoc and rarely validated on generated ideas. The authors build an evaluation set by mining OpenReview for passages where reviewers unanimously affirm or dispute a paper's originality, pair these with ideas from a vanilla LLM generator, and run a controlled study across six judges. Small prompt choices have outsized effects: telling a judge that reviewers found one idea novel can flip its verdict on more than half of identical idea pairs, shifting pairwise accuracy by over 50 points and sometimes pushing it below chance. Retrieval and larger reasoning budgets help little, and two purpose-built novelty evaluators lose to the cheapest prompted baseline, which calls reported novelty gains of ideation systems into question.

arXiv:2610.02022 PDF

External Observers May See More Clearly: Cross-Model Span-Level Hallucination Detection in Large Language Models via Hidden State Probing

Kingshuk Gupta, Davide Buscaldi Most probes that read an LLM's hidden states to detect hallucinations treat the task as token-by-token binary classification, which misses where a hallucinated span starts and how far it continues. This work instead probes layer-wise activations to find span-level hallucination onset and continuation tokens, and it beats random baselines on Precision-Recall AUC by a wide margin despite extreme class imbalance. It also proposes cross-model detection, where one model reads the internal representations produced while processing another model's generation. An external observer can match or exceed the generator's self-detection of hallucination onsets, even when the observer is the smaller model.

arXiv:2610.02066 PDF

Learn the Directions, Normalize the Gains: Post-Training Normalization for LoRA

Zailong Tian, Yanzhe Chen, Zhuoheng Han, Houfeng Wang, Lizi Liao Low-Rank Adaptation (LoRA) updates can erode capabilities outside the target task. The authors trace this to adaptation imbalance, where a few singular directions dominate the learned update. Their fix, LoRA-Norm, is applied after training: it keeps the learned directions, rebalances their gains with a fixed nonlinear transform of the singular values, and rescales so the nuclear norm is unchanged. It needs no calibration data, no extra training and adds no inference cost, and across two backbones and three tasks it improves both task specialization and capability retention over post-hoc spectral pruning and gradient-guided editing.

arXiv:2610.02067 PDF

LLM2Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them

Yinheng Li, Justin Wagle Jev-style decision models output a probability distribution over predefined options instead of free text, so software can act on the result directly. LLM2Jev reads calibrated decisions straight from next-token probabilities over bracketed numeric identifiers. It offers a training-free inference recipe plus a fine-tuning objective that combines a tree-factorized listwise loss with KL-divergence anchors to the base model. Without any training, Qwen3.5-4B matches community Jev-style models built on the same backbone, beats letter-logit readouts, supports any number of options and handles image-based decisions. Fine-tuning mainly helps weaker models like Qwen3-0.6B and tasks such as many-option intent routing, and the KL anchors keep conversational ability from degrading.

arXiv:2610.02076 PDF

Scalable, Transferable Meta-network for Data Selection Requires a Different Loss (and Why the Obvious Choice is Problematic)

Zilin Du, Bowen Yang, Boyang Albert Li Meta-learning methods for selecting LLM training data learn per-sample weights from a validation objective, but per-sample weights do not transfer to unseen data. Simply swapping them for a selection network makes optimization unstable, because weights get suppressed and the network keeps relying on easy-to-learn features. Transferable Example Scoring and Selection (TESS) addresses this with a new Pointwise Value Matching (PVM) objective. On LLM safety and targeted instruction tuning, it transfers well across datasets, from subsets to full corpora, and from smaller to larger models.

arXiv:2610.02092 PDF

Local Support Learning

Assaf Ben-Kish, Akarsh Kumar, James Glass, Raja Giryes The authors treat catastrophic forgetting in large pre-trained models as a geometric problem in each weight matrix's input space and show that standard gradient updates are suboptimal for retaining prior capabilities. Local Support Learning (LSL) pairs a normally trained weight adapter with a gate that enables the adapter only on activations resembling its own training distribution. The gate is a Gaussian Mixture Model whose likelihood falls off quickly away from its training data, so it tends to stay closed on earlier-phase inputs without needing any prior data. The method resolves forgetting in LLMs of up to 7 billion parameters across multiple training phases, with modest memory and compute costs and robustness to hyperparameters.

arXiv:2610.02126 PDF

Finetuning with Sampling: SFT Learns Better Than You Think

Aayush Karan, Sitan Chen, Yilun Du Supervised finetuning (SFT) is commonly thought to generalize worse and forget more than reinforcement learning (RL), yet it can learn from off-policy expert data that RL cannot easily exploit. Rather than changing the training objective, the authors reshape the data. They use a Markov chain Monte Carlo (MCMC) sampling algorithm that progressively rewrites off-policy expert traces so they look more on-policy for the model being finetuned. On scientific skill acquisition, mathematical reasoning and open-ended expertise tasks, SFT on the resampled data rivals standard post-training methods and often generalizes better and forgets less than strong on-policy baselines. The resulting models also appear to learn beyond merely sharpening the base model's existing distribution.

arXiv:2610.02140 PDF

From Gradients to Capabilities: Understanding Multi-Teacher On-Policy Distillation

Siqi Zhu, Suozhi Huang, Kaixuan Zhang, Yuheng Yang, Zhanyang Jin, Yihang Sun et al. Multi-teacher on-policy distillation (MOPD) tries to merge several reinforcement-learning-trained domain teachers into one student. The authors trace how teacher signals turn into parameter changes using Qwen3-1.7B with four teachers, plus diagnostics on SmolLM3-3B. They find that token-level loss averaging implicitly favors longer responses, and that Adam's first moment makes updates from different teachers much more similar (cosine similarity 0.83) than their raw gradients. About 97% of FP32 master weights move from initialization, but only 7–11% of BF16 weights do, because rounding hides small changes. A top-64 KL gradient closely matches the full-vocabulary gradient, but whether it helps math accuracy depends on the averaging rule (+2.6 or -2.1 points).

arXiv:2610.02179 PDF

Decoding Looped Transformers Better for (Almost) Free

Weihao Liu, Huangjie Zheng, Tianrong Chen, Rohit Dilip, Richard He Bai, Yizhu Jiao et al. Looped Transformers save parameters by running a shared block repeatedly, and every loop produces an intermediate prediction that standard decoding throws away. LoopCD is a training-free contrastive decoding method that treats an earlier loop as a weaker model and contrasts it with the final loop to guide token selection. It operates either in logit space (LoopCD-Logits) or in hidden-state space at no extra output cost (LoopCD-Hidden). Across four looped model families, it raises Ouro-2.6B-Thinking's AIME 2024 pass@1 from 61.88% to 73.33% and Huginn's HumanEval pass@1 from 22.56% to 31.71%. With this guidance, models can run half the loops and still match full-depth baselines, cutting forward FLOPs by 22.5% to 48.2%.

arXiv:2610.02185 PDF

Hierarchical Continuous Diffusion Language Models

Hui Ren, Zihan Li, Chang Liu, Huidong Liu, Alexander Schwing When discrete diffusion language models decode several tokens in parallel, each token is sampled independently, which breaks the dependencies between them. Continuous diffusion models avoid that problem but have nothing tying their latent state to a valid token configuration until the final decode. HC-DLM (Hierarchical Continuous Diffusion Language Models) makes a continuous latent the only persistent generative state: at every step tokens are read out from the latent and fed back as a scaffold for the next latent update. The method is trained with an objective derived from a variational bound on token likelihood. At matched model size, it outperforms discrete and continuous diffusion baselines in puzzle accuracy on Sudoku and Countdown and in generative perplexity on LM1B.

arXiv:2610.02193 PDF

TACO: Ternary Absolute-max Column-wise One-sparse Optimizer for LLM Fine-Tuning

Jichao Jiang (University of Central Florida), Cristian McGee (University of Central Florida), El Houcine Bergou (Mohammed VI Polytechnic University), Hanqin Cai (University of Central Florida), Aritra Dutta (University of Central Florida) Optimizer state takes up much of the memory in full-parameter fine-tuning of large language models (LLMs), and alternatives such as Muon can lose accuracy when fine-tuning models pretrained with AdamW. TACO (Ternary Absolute-max Column-wise One-sparse optimizer) computes the exact steepest-descent direction under a dimension-normalized 1→1 operator norm, which amounts to taking the sign of the largest-magnitude entry in each column of a weight matrix. It keeps first-order gradients while storing only a few low-precision gradient components per column. On OPT-13B it cuts persistent optimizer state by 174× relative to AdamW8bit (from 27.7 GB to 0.16 GB) and peak memory by 2.9×, with comparable accuracy and runtime, and it enables full-parameter fine-tuning of 30-32B-parameter models on a single 80 GB H100 GPU.

arXiv:2610.02199 PDF

24 more specialized papers

Agents 104

Heavy-Tailed Memory Traces in Long-Horizon Language Agents

Xinyuan Song, Zekun Cai Memory systems for long-horizon language agents are usually judged by task success and token cost. The authors instead examine how memory use is distributed and find that retrieval concentrates on a small core while rare states form a long tail where prediction errors accumulate. Semantic LLM policies produce the most power-law-like traces. The proposed CTWM (Core-Tail World Model) allocates prompt budget by rank using a single exponent and keeps a summarized tail. It cuts prompt tokens by 5.9% and tail prediction error by 13.6% on a synthetic graph world, and it reduces tokens by 24.48% on LongMemEval with no loss in aggregate accuracy, with consistent savings on ALFWorld.

arXiv:2610.00010 PDF

When Do Causal World Models Help Modular LLM Agents

Xinyuan Song, Zekun Cai LLM agents that act across modular services such as payment, inventory, and shipping need to know which cross-module dependencies are causal. World models fit to observational traces cannot identify this. The FedCausalCompose framework uses local actions as intervention evidence to recover cross-module interfaces. The authors show theoretically that observational world models carry an irreducible error when back-door paths are unblocked. Experiments find that causal interfaces help most in structured tool environments, where API signatures expose preconditions and effects, while in dialogue and narrative settings agents ignore raw causal edge lists unless a short attention anchor makes them decision-relevant.

arXiv:2610.00012 PDF

From Proposal to Verified Effect: Praxa, an Evidence-Bound Harness for Governed AI Agent Execution

Stefan G. Creadore Praxa is an agent harness that treats proposal, authority, dispatch, verified external effect, and promotion to serving as separate, explicitly tracked states. It implements them through deterministic admission, brokered execution, external read-back, and reviewed promotion. In a 12-task Terminal-Bench pilot, the reliability layer matched the baseline at 17/36 trials while using about 37% more input tokens and 51% more output tokens. A separate coordination comparison showed equal accuracy with 37% fewer tokens. The authors state that the evidence supports only an architecture that makes authority-to-effect transitions testable, not security, production safety, or user benefit.

arXiv:2610.00015 PDF

Measuring the Microtask Eligibility Gap: When Is an Off-the-Shelf SLM Enough for an Agent Harness?

Jundong Hu, Shekar Ramachandran Agent harnesses increasingly route small chores to small language models (SLMs), such as approving shell commands, writing memory, selecting tools, and ranking past turns. A benchmark of four such microtasks sets thresholds anchored to cheap non-LLM baselines and passes a configuration only if its confidence bound clears the threshold. None of the 16 Qwen3 configurations (0.6B to 8B, at full precision) is eligible, and none of the 12 Llama-3.x configurations is either. Four-bit quantization with RTN, GPTQ, or AWQ makes no configuration eligible, and the results hold across prompt paraphrases. The practical advice is to put SLMs behind a baseline that meets the threshold, for example a 4B re-ranker over a BM25 shortlist, which improves on BM25 alone.

arXiv:2610.00025 PDF

The Delegation Danger Band: Why Mid-Capability Sub-Agents Over-Trust Inherited Stale State

Jundong Hu, Shekar Ramachandran cross-listed Agent frameworks often fork sub-agents that inherit the parent's full context, including conclusions that have since been superseded. The authors compare three inheritance policies (fresh reset, curated handoff, and full inheritance with stale copies) across Qwen3 models from 0.6B to 8B, on synthetic tasks plus MuSiQue and HotpotQA. Deference to stale state drops sharply as capability rises, but net harm is non-monotone: the mid-capability Qwen3-1.7B forms a "danger band" where full inheritance pushes it significantly below its fork-fresh baseline, while weaker and stronger models are barely affected. Curated handoff beats full inheritance on all three datasets, but a fixed capability-threshold router does not transfer across them.

arXiv:2610.00041 PDF

K-Dense BYOK: An Open-Source AI Research Assistant That Runs Locally and Keeps a Hash-Chained Lab Notebook

Aubrey M. Brueckner, Darshil Patel, Yuhuan He, Timothy Kassis K-Dense BYOK (bring your own keys) is an MIT-licensed AI research assistant that runs on a scientist's own machine with any hosted or local model. Each project is stored as an ordinary folder. The assistant adds a library of scientific procedures, workflow templates, data catalogs and reviewer and writer roles, plus an append-only Living Lab Notebook. To counter model overclaiming, it keeps a log of what the agent actually did, written by an observer rather than by the agent, which has no tool that can write to it. On twenty interdisciplinary research prompts scored with a fixed rubric, it outperformed two managed platforms on both scientific quality and research execution, and its deliverables were the only ones that recorded their software environment.

arXiv:2610.00074 PDF

Scientific Agents: Evaluating Profession-Specific System Prompts on Scientific Tasks

Timothy Kassis The Scientific Agents corpus of 503 profession-specific AGENTS.md system prompts was tested with Gemini 3.8 Flash in the Pi agent harness. Each matched profile was compared against a minimal baseline and three other control prompts across nine science benchmarks with about 4,500 questions. Profiles did not improve accuracy (−0.6 percentage points on average) yet produced 1.5–2.3x more output tokens and cost 2.2–4.5x more per call. On tool-using BioMysteryBench bioinformatics problems they cut the solve rate from 56.7% to 46.7%, mainly because runs hit token and time limits more often. Longer prompts were somewhat more robust to provider API drops, but generic and mismatched prompts matched that benefit, so domain expertise was not the cause.

arXiv:2610.00084 PDF

BudgetSchemaBench: A Budget-Swept Diagnostic for Schema Context in Text-to-SQL

Chen Shen Text-to-SQL agents working over large data catalogs must fit schema descriptions into a limited context budget, which forces a trade-off between how many tables to include and how much detail to give each. BudgetSchemaBench derives relevance labels directly from gold SQL over an 80-database catalog, then sweeps four context budgets and three schema serializations. Raising the budget from 2.5% to 50% of the catalog improves execution accuracy by 18 points with lexical retrieval but only 3 with dense retrieval, and serialization format changes results by at most 2 points. When the required tables are removed, 94.6% of correct predictions still name one of them exactly, which suggests models reconstruct missing schema from memorized knowledge.

arXiv:2610.00092 PDF

A Verifier Can Leak the Answer: Diagnosability Before Optimization in Closed-Loop Agent Debugging

Peiying Zhu, Sidi Chang cross-listed When agent debuggers are evaluated with simulator-based verifiers, the verifier itself can encode the answer and make solver comparisons meaningless. In an aggregate-trace debugger for a closed-loop decision agent, exact minimum hitting set (MHS) and a greedy method appeared equally effective. An audit then found that exact-anchor predicates in the verifier produced the planted fault pair in 9/9 cases: the solver was correct, but the verifier had already revealed the target. As a replacement, the authors propose a support-gated verification contract that requires evidence eligibility to be established before any detection. On a preregistered held-out set of 1,440 cases, this contract had 0 stable false admissions across 20 represented components.

arXiv:2610.00126 PDF

EviGraph: Proof-Carrying Selective Recommendation over Temporal Public-Service Knowledge Graphs

Yixi Zhou, Sikun Wang, Lei Fan, Fan Zhang Public-service recommendations must be backed by evidence that matches the requested service, scope and date, but treating every missing detail as disqualifying makes a system refuse too often. EviGraph separates critical decision requirements from details that may stay unresolved. A language agent links those requirements to evidence in a temporal knowledge graph, and a deterministic checker decides whether a recommendation is supported. On a bilingual Hong Kong public-service benchmark, this distinction reduces unnecessary abstention, while adding further verification withdraws supported recommendations without improving decision quality.

arXiv:2610.00212 PDF

CAVE-Mem: Boundary-Aware Experience Validation for Memory Search

Xinyu Li Long-term memory agents often retrieve past search lessons that look similar to the current situation and inject them into the prompt, but a relevant lesson can still hurt when the data source, question intent, answer granularity or evidence boundary differs. CAVE-Mem is a training-free framework that represents each experience as a typed operator with conditions for applicability, boundaries and utility. It first produces a base answer and lets an operator modify it only when all of those conditions match, abstaining otherwise. Across conversational memory, multi-hop question answering and long-document narrative reasoning, it consistently outperforms relevance-only experience reuse.

arXiv:2610.00238 PDF

Knowing When to Yield: Grounded Arbitration of User Corrections in Text-Based Embodied Agents

Yezhou Cheng, Runjia Du, Zeming Liu, Hang Lyu, Zehua Yang, Bojun Lin Frames how an embodied agent should handle a user correction that might be wrong as a choice between accepting it, rejecting it, inspecting the world, or asking the speaker. GAVA makes this choice with observation-bounded evidence and a one-step expected-loss rule. In text-only ALFWorld, adding a training-derived object-location prior lowered interaction cost by 0.49 on unseen scenarios, and the gain replicated on a held-out cohort after the policy was frozen. The authors say the results do not show that inspecting the environment beats asking for clarification in general, and the study uses no human participants, visual input, or physical robots.

arXiv:2610.00282 PDF

Rules to Tools: Executable Checks for LLM Agents in Scientific Computing

Jingjie Ning, Guojiang Zhao, Chen Xu, Shanshan Zhong, Xiaochuan Li, Ji Zeng et al. Scientific coding agents get equations and requirements as text and must judge whether their revised programs meet them. Rules to Tools (R2T) gives agents prepared executable checks of those requirements instead of text alone. In matched SciCode repair experiments, complete repair rose from 26/30 with text to 29/30 with callable checks overall, though most tasks tied and the confidence interval for the difference includes zero. In a PDE comparison, checks matched detailed text in accuracy while reducing model output by 31.2%, at the cost of higher CPU use.

arXiv:2610.00313 PDF

DuplexSpeechBench-Document Grounding: Benchmarking Document Grounding and Hallucinations in Voice Agents

Puneet Mathur, Nedim Lipka, Zeyu Jin, Dinesh Manocha DuplexSpeechBench-Document Grounding (DSB-DG) evaluates whether voice agents ground their answers in supplied documents. It contains 1,636 adversarially verified QA pairs from 50 documents across five professional domains. The benchmark tests three failure modes: accuracy as documents get longer, retention of document facts over multi-turn dialogue, and whether re-injecting context reduces drift. A cascaded speech recognition, LLM, and text-to-speech pipeline grounds best, with Gemini-Live and GPT-Realtime close behind. Open-weight speech-to-speech models show abrupt context-capacity collapse, and across systems failures tend to be unsupported answers rather than abstentions.

arXiv:2610.00316 PDF

Actions with Receipts: Jointly Binding Claims, Evidence, and Execution for Replayable Tool-Agent Auditing

Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Yina Sa et al. cross-listed A tool-using agent can show a valid citation and a valid execution log while the claim the user sees is not actually the one its execution produced or its source supports, because each artifact can be moved independently across claims, runs, or source versions. The authors propose a claim-anchored execution contract: a receipt that jointly binds the emitted claim, the exact source span, the ordered execution prefix, and the source version and access state. A deterministic verifier checks these bindings separately from a pluggable layer that judges whether the source actually supports the claim. The joint contract detects 1,275 of 1,280 cross-object substitution attacks, and removing any one property drops detection of its targeted attacks to between 1.6% and 6.3%.

arXiv:2610.00327 PDF

ContractRL: Shielded Group-Relative Policy Optimization for Auditable Tool-Call Repair

Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Yina Sa et al. Structured tool calls often fail on only a few fields, yet regenerating the whole JSON object wastes tokens and makes repairs hard to audit. ContractRL treats verifier-guided repair as a bounded sequential decision process. At each step the policy sees typed verifier feedback and its repair history, a contract-derived mask filters out malformed or prohibited RFC-6902 patch operations, and a group-relative policy-optimization objective trains its patch, retry, and abstain choices. Given the same verifier information, it reaches 0.9362 semantic success using 34.4 generated tokens, against 0.9076 with 44.9 tokens for Patch-SFT and 0.9148 with 137.2 tokens for full regeneration.

arXiv:2610.00328 PDF

Authorization for Self-Modifying AI Agent Populations: Conserving Authority across Replacement, Forking, and Rollback

Genliang Zhu, Chu Wang cross-listed Self-modifying AI agents can replace, fork, and roll back their own software, so authorizing each successor individually lets a population of siblings duplicate quotas, combine permissions, or outlive revoked ancestors. The authors define authorization succession, an external protocol that conserves authority across the active generations of an agent lineage using staged reservations, partitioned forks, atomic handoffs, and rollback that never restores spent authority. Under stated assumptions they prove properties including population-safe succession, fork conservation, revocation closure, and exclusion of self-certification. In an executable evaluation, an independent checker accepts all 64 original traces and rejects all 28 semantic mutants, and adapters reproduce the decisions around real Darwin Godel Machine mutations.

arXiv:2610.00347 PDF

Fault-Tolerant Budget Conservation in Distributed Multi-Agent Delegation

Genliang Zhu, Chu Wang When AI agents delegate work across concurrent, failure-prone workers, lost replies, retries, late completions, and network partitions can cause spending beyond a granted budget. The authors formalize fault-tolerant budget conservation. Budgets become exclusive escrow credits that move through a delegation DAG and are converted into signed, idempotent operation reservations before dispatch; uncertain effects stay charged until they are authenticated as settled or proven not to have happened. They prove ownership partition, conservation, at-most-once settlement, and late-completion safety, and show that staying available inside a partition requires exclusive preallocation. Bounded TLA+ model checking and crash-injected SQLite experiments confirm that the issued budget bound holds across all evaluated crash, retry, duplicate, partition, and late-completion schedules.

arXiv:2610.00349 PDF

Proof-Gated Signing: Solver-Checked Transaction Guards that Hold Under State Drift for Onchain AI Agents

Bravish Ghosh cross-listed Wallet-controlling AI agents read attacker-reachable content, and the usual pre-signing checks (allowlists, LLM reviewers, simulation) describe the chain only at check time. That leaves them open to state drift through front-running, contract upgrades, or token-parameter changes before execution. Proof-Gated Signing (PGS) simulates the transaction, uses an SMT solver to check a value-and-permission policy across an oracle price-uncertainty band, and compiles on-chain post-conditions that the agent's smart-contract wallet enforces atomically at execution. On 260 scenarios, PGS prevented 93.6% of harmful transactions and no drift attack produced attacker gain, versus 57.9% for simulation-only checks and 71.4% for a static allowlist, at a cost of about 41k gas per check. The authors also find that giving an LLM reviewer a clean pre-drift simulation made it more likely to approve a drift attack.

arXiv:2610.00354 PDF

What Should an Agent Remember? Disentangling Retention from Retrieval in Bounded-Memory Evaluation

Juli Huang A persistent agent has to decide both what to keep in memory and what to retrieve for a query. Many memory evaluations confound the two by comparing methods that differ in both, and in how much history they can access. A streaming-recall benchmark that crosses retention and selection rules over the same 300 seeded episodes shows that, with access held fixed, query-aware selection improves recall by 15.5 percentage points. Of a reported 68.7-point advantage in a mixed comparison, 53.2 points come from history access alone. Under bounded retention, every failure in the recency condition comes from eviction rather than ranking, so the authors recommend that memory evaluations fix access and report retention and selection separately.

arXiv:2610.00366 PDF

When Harnesses Lose the Signal: Causal Evaluation of Recovery in LLM Agents

Shuyao Xiao, Shengling Wang, Xuan Chen, Ke Chao, Ming Cui, Feifei Qian et al. Agent harnesses often try to recover from execution errors, but recovery is usually judged only by average task success, which hides cases where it breaks a trajectory that would have succeeded. The authors treat recovery as a causal question: starting from the same execution state, they compare outcomes with and without recovery to separate rescue from harm. They then introduce the Causal Intervention Router (CIR), a lightweight policy that uses information available before recovery to decide whether intervening is worthwhile. On long-horizon ALFWorld tasks with Qwen3-14B, CIR raises success from 70.33% to 73.33% and leaves all evaluated trajectories with correct observations untouched.

arXiv:2610.00372 PDF

T2SPO: Trajectory-to-Step Policy Optimization for Agentic Reinforcement Learning

Bo-Wen Zhang, Junwei He, Maoqi Liu, Feiran Li, Song-Lin Lv, Wentao Ma et al. Reinforcement learning for large language model (LLM) agents often gets a reward only at the end of a task, which says little about which intermediate steps helped. Trajectory-to-Step Policy Optimization (T2SPO) uses past successful trajectories to estimate how far each state is from success, with a pretrained TabPFN regressor that is given those trajectories as in-context examples. The change in estimated distance between consecutive states gives each step extra credit, and new successful trajectories refresh the regressor's context without updating its parameters. With 1.5B and 7B models on ALFWorld and WebShop, T2SPO consistently improves task success over GRPO.

arXiv:2610.00388 PDF

Code That Works, Environments That Don't: Measuring Environment Reproducibility in AI-Generated Software

Bhanu Prakash Vangala, Tanu Malik cross-listed Coding agents can generate software that passes functional tests, but they may still fail to state the dependencies needed to run it. The authors define an agent protocol and a three-layer framework that separates declared dependencies, runtime-installed dependencies, and the necessary-and-sufficient set. They use it to evaluate three coding agents across four programming languages and fifty tasks. Dependency specifications are often inconsistent, redundant, or incomplete, with agreement across agents as low as 7% on identical tasks, and newer agents show no meaningful improvement. The largest gap is between the declared and runtime layers, which the authors attribute to environment priors learned from training data.

arXiv:2610.00425 PDF

How AI Agents Discover Scientific Equations: From Hydrotope Rediscovery to New Water-Wave Amplitudes

Zihan Zhou, Digvijay Wadekar, Matias Zaldarriaga cross-listed This case study examines how AI agents discover and validate scientific formulas, using the hydrotope, a recently found formula that combines piecewise polynomial pieces of nonlinear water-wave scattering into one global expression. Of 18 single-prompt rediscovery runs, given no hint, a false hint, or a true hint, only four recover the full formula. Most failed runs find the correct local polynomials but do not combine them or test them over the full domain, and symbolic regression and standard regressors also fail. A coordinated workflow in which a lead "PI" agent assigns complementary analytic and numerical tasks to two research agents rediscovers the formula and finds a new, verified analytic expression for the six-point amplitude in a harder setting.

arXiv:2610.00435 PDF

JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces

Haoyang Su, Weiran Huang LLM agents produce reasoning and actions one token at a time, which makes long interactions slow and expensive. Jev-style models make fast probabilistic predictions over fixed sets of options, but those options must be specified in advance. JevSpawn derives such action spaces from natural-language task descriptions. It spawns candidate actions in parallel, picks branches based on feedback, revises its representations, and recovers from retained alternatives, while reusing shared action structure and model prefixes to avoid repeated computation. On eight benchmark tasks against seven agent baselines, it reports better task performance and faster navigation without additional training.

arXiv:2610.00437 PDF

EurekaBench: Measuring Agentic Ability to Discover New Scientific Insights

Jiayi Geng, Zhengxuan Wu, Kevin S. Chen, Seungone Kim, Joseph Janssen, Zora Zhiruo Wang et al. EurekaBench tests whether AI agents can run long-horizon experiments and discover mechanisms that explain observed data, in the spirit of scientific discovery. It contains 26 expert-verified tasks across neuroscience, computer science, chemistry, astrophysics, geophysics, and plasma physics, with 306 scientific insights the discovered mechanisms should support. Agents are evaluated on whether they respect known scientific constraints, how accurately their mechanisms predict data, and whether those mechanisms yield real insight. Current agents focus too heavily on predictive accuracy, where they can surpass human scientists, but fall well short in deriving scientific insights.

arXiv:2610.00492 PDF

Before Agents Decide: Epistemic Action in LLM-Based Systems

Yizhi Liu, Balaji Padmanabhan, Siva Viswanathan This position paper argues that LLM-based agent design should ask whether the available evidence is ready for a decision before the agent makes one. Drawing on cognitive science, it applies the idea of epistemic actions, meaning actions taken to improve the basis for a later choice, and distinguishes three kinds: acquiring missing evidence, transforming available evidence, and probing a system to provoke a revealing response. It introduces epistemic scaffolding, the interfaces, tools, and environments that make these actions possible and auditable, as a design concern for agents.

arXiv:2610.00511 PDF

No One Architecture Fits All: A Cross-Environment Evaluation of Hierarchical Red Team Agents

Ayan Javeed Shaikh, Arunesh Sinha, Nathaniel D. Bastian, Ankit Shah cross-listed Autonomous red-team agents that stress-test cyber defenses combine reinforcement learning (RL) and large language models (LLMs) for planning and execution, but each architecture is usually evaluated in only one environment. The authors compare an all-RL planner-executor hierarchy with an all-LLM one against expert defenders in CybORG CAGE-4 and in Cyberwheel at two network sizes, across 18 configurations. The result is an environment-dependent inversion: RL+RL wins the compact CAGE-4 (78.5% versus 18.0% disruption success) and the 100-host network, while a pretrained cybersecurity LLM agent wins the 1010-host network (55.0% versus 0.0%). Kill-chain analysis explains the gap: RL stalls at privilege escalation on the large network, while LLM agents gain privileged access but rarely turn it into operational impact.

arXiv:2610.00557 PDF

Worse Together: How Performance Breaks Down in Multi-User Multi-Agent Teams

Sahan Paliskara, Nattaput Namchittai, Andrew Lampinen As AI agents acting for different users increasingly share resources such as a codebase, calendar, or budget, the authors test whether teams of such agents coordinate as well as one agent serving everyone. Across five frontier models and 77 scenarios in four environments, teams of per-user agents produce worse group outcomes than a single coordinator in every environment. Without a communication channel they collapse entirely in two environments, and even with one, the coordinator fulfills a targeted request about twice as often in the personal-assistant setting. Observed failure modes include stalling as teams grow, agents overriding each other, and fabricated claims. Mitigations such as a team lead, explicit procedures, or a check that forces agents to read peer messages help but depend on the environment, and three of the environments will be released as MAMUBench.

arXiv:2610.00583 PDF

Towards Hierarchical Cyber Defense with Large Language Models: From Planning to Execution

Harshith Doppalapudi, Nathaniel D. Bastian, Ankit Shah cross-listed Cyber-defense agents trained with reinforcement learning usually have to be retrained whenever the network changes size. The authors test whether frozen, zero-shot LLMs can replace one or both levels of a planner–executor defense hierarchy in the Cyberwheel environment, which includes an automated red team mapped to MITRE ATT&CK. They compare six models from 3B to 70B parameters, including two cybersecurity-specialized ones. Swapping in an LLM only as the planner helps little at larger scales, but letting a capable LLM also handle execution works much better: a frozen general-purpose 70B model holds successful lateral movement to about 1% of steps across small, medium, and large networks with the same weights, while the RL baseline needs retraining for each scale.

arXiv:2610.00590 PDF

Legal Research Bench: Measuring End-to-End Reliability in Long-Horizon Legal Research Agents

Katrina Drozdov, Oliver Chen, Langston Nashold, Rayan Krishnan Legal Research Bench (LRB) contains 413 open-ended U.S. legal research questions written by experts, each with a gold answer, supporting authorities, and a binary grading rubric. Thirteen frontier models are evaluated as agents with web search, case-law search, and retrieval tools, and a response counts as correct only if it meets every criterion and its citations check out; the LLM judge is validated against attorneys. The strongest model, Claude Opus 4.8, is fully correct on only 42.9% of questions, and accuracy drops on questions that require reconciling conflicting authorities. Across models, more turns, more tool calls, and higher inference cost do not predict higher accuracy.

arXiv:2610.00609 PDF

Spatial Strategies, Not Actions: Vector-Quantized Geodesics as Tools for LLM-Driven Agents

Gabriel Turinici To probe whether LLM agents can handle spatial tasks, the authors give an LLM orchestrator a library of movement tools derived from geometry instead of having it choose low-level actions. The agent collects shortest-path trajectories in a grid world, uses vector quantization to keep a representative subset, and has the LLM write a natural-language description of each one offline so it can be used as a tool. In a partially observable, dynamic 2D grid with Qwen3.6-35B-A3B, combining this library with a zoom tool and a collision-detection tool lets a fast non-reasoning setup match the goal-reaching rate of a chain-of-thought setup, cutting decision time from minutes to seconds.

arXiv:2610.00613 PDF

CompMat-Bench: Benchmarking AI Agents for Computational Materials Science

Chenmu Zhang, Levi Felix, Jun-Jie Zhang, Xingfu Li, Xuelian Jiang, Tao Jiang et al. Evaluating AI agents on computational science is expensive because each trial would mean rerunning costly simulations. CompMat-Bench contains 94 tasks taken from recently published computational materials studies. The research steps are reproduced in advance, so agents are graded with fixed rules, and no LLM judge, on how they prepare simulation inputs and analyze outputs. With full methodological guidance on single tasks, agents built on three LLMs pass 66.0-90.4% of tasks. Longer workflows and reduced guidance hurt weaker agents, while the strongest agent only drops when both are combined, and most failures come from scientific errors rather than from misusing software.

arXiv:2610.00636 PDF

Incident-Arena: Getting agents to the last nine of reliability

Andre Fu, Malik Drabla, Leon Liu, Meji Abidoye, Marek Suppa, Lata Mishra et al. Existing benchmarks for agentic site reliability engineering (SRE) rely on toy repositories, non-standard frameworks, and simple static checks. Incident-Arena provides 20 human-built tasks in which a real open-source application is deployed to an ephemeral Kubernetes cluster under sustained load, a fault is injected anywhere from configuration to container images, and the agent must repair it. Fixes are graded by functional verifiers that require system-level metrics to stay stable and repairs to be done safely. Agent runs average 2.81M tokens over 41 turns, and frontier models score below 64.3%, failing in diagnosis, localization, incomplete repairs, and unsafe regressions.

arXiv:2610.00648 PDF

Self-Evolving Coding Rules for AI Coding Agents

Zhengyuan Jiang, Reachal Wang, Yuepeng Hu, Yupu Wang, Yuqi Jia, Neil Zhenqiang Gong How well AI coding agents perform depends heavily on their coding rules, which are usually written by hand and then left unchanged. RuleEvolve keeps a pool of candidate rule sets and improves them over iterations: an LLM-powered mutator proposes variants, and a judge module scores them and keeps the best performers. Across two coding-agent frameworks, four backbone LLMs, and three benchmarks, evolved rules beat hand-engineered rules and existing prompt-optimization baselines on functional correctness, code length, and/or token cost.

arXiv:2610.00650 PDF

Agent Evaluation Reliability: More Tasks Won't (Always) Fix An Agent Leaderboard

Michael Hardy, Ruhana Azam, Anka Reuel, Mykel Kochenderfer, Sanmi Koyejo Agent leaderboard rankings mix the effect of the model with the effects of the evaluation setup, such as the scaffold and the choice of tasks. The authors build a Bayesian variance-decomposition framework for sparse, imbalanced leaderboards and apply it to 22 benchmarks from the Holistic Agent Leaderboard and Harbor Index. Fixed model-scaffold systems are ranked reliably (0.935-0.994), but rankings of the underlying models are much less reliable (0.148-0.841), and even infinitely many similar tasks raise model-ranking reliability by at most 0.097 when limited scaffold coverage dominates the uncertainty. Pooling diverse benchmarks raises projected cross-task ranking reliability from 0.44 to 0.75 at the same task budget and can cut projected cost by up to 83%.

arXiv:2610.00651 PDF

LabBook: Harnessing Experimental History for Efficient LLM-Driven Discovery

Bo Yuan, Wenqian Ye, Zelin Zhao, Lama Moukheiber, Henry Kautz, Aidong Zhang et al. Evolutionary approaches to LLM-driven discovery generate new programs from a few selected ancestors, which drops useful evidence from other experiments, while feeding in the full experimental history makes contexts long and redundant. The authors propose a single-agent harness built around LabBook, a memory the agent maintains itself. The agent uses it to retrieve relevant evidence from a complete experiment log and to guide each new program, updating the memory as it goes, with no population or branching search. On 49 Frontier-CS problems, LabBook gives a better quality-cost trade-off than the evolutionary baselines tested with two backbone models, and it remains competitive on nine additional math, systems, and heuristic-design tasks.

arXiv:2610.00675 PDF

SkillSpec: Consensus-Gated Agent Skill Evolution via Representation Specialization

Huancheng Chen, Xiaodi Sun, Zhaoqiong Huang, Shenyang Huang Shreya Singhal, Jingwen Lu LLM agents can keep reusable procedural knowledge as natural-language skills, but existing methods treat each skill as a single document tuned against aggregate validation scores. SkillSpec splits the work into two phases. In the first, an edit to a skill is committed only when repeated paired evaluations agree it helps overall and never hurts on aggregate. In the second, signals from the whole optimization trajectory, including rejected candidates, decide whether the skill is stored as a flat, graph, or hybrid representation. Across six benchmarks and three target models, SkillSpec improves average success rate by 6.89% over SkillOpt.

arXiv:2610.00704 PDF

Initialization Improves LLM-Driven Discovery

Mansi Sakarvadia, Marco Ciccone, Colin Raffel LLM-driven discovery uses a harness that repeatedly prompts a model to improve a candidate against an objective. The authors build Modular, a suite of 12 harnesses, and test it on five discovery tasks. They find success is brittle and sensitive to harness design, and that a common failure mode is mode collapse, a sharp drop in the diversity of candidates. Popular diversity-inducing interventions give inconsistent gains, but how well the early candidates perform predicts eventual success. Starting with a stage of parallel exploration before iterative optimization gives consistent gains across harnesses and tasks.

arXiv:2610.00707 PDF

ReLiveGym: Evaluating Long-Lived Agents over Weeks of Replayed Reality

Xisen Jin, Jingheng Li, Zhenglun Chen, Junyi Du, Xiang Ren LLM agents are increasingly expected to run unattended for days or weeks, such as monitoring markets, while existing long-horizon benchmarks assume the environment does not change over time. ReLiveGym has agents act sparsely over simulated weeks of real news, market, and social-media streams replayed in chronological order, with tasks that vary in time sensitivity, reasoning load, and recurrence. Across eight base models, how an agent decides when to act emerges as a key harness-design choice, and the best option varies by task and sometimes by model. The authors also show that continuous learning from hindsight feedback addresses some of the failure modes they observe.

arXiv:2610.00710 PDF

Kepler: Auditable World Models for ARC-AGI-3

Wensen Wu ARC-AGI-3 tests agents in interactive games whose rules and goals must be worked out from observation. Kepler is an open-source harness that writes hypotheses as executable world models and checks them against past transitions and conditional predictions. With one fixed Claude Opus 5 configuration, it reached a server-verified perfect score on all 25 public games, using no more actions than the median human on 181 of 183 levels, at about $778 in API costs. The authors also document evaluation failures, including source-code leakage that produced an invalid perfect run. Because 48 of 50 game-model combinations reached the maximum score, they argue that the public-set score alone no longer separates systems well and that first-attempt, cost-conditioned reporting is needed.

arXiv:2610.00834 PDF

AuraForge: Scaling Security Supervision for Training Coding Agents

Danqing Wang, Songwen Zhao, Harsh Sharma, Jierui Wang, Andre Vicente Duarte, Ivan Bercovich et al. cross-listed Functionally correct code from coding agents can still be insecure, and reliable security supervision for training is hard to obtain at scale. AuraForge synthesizes and validates executable security tests using attack-oriented test generation, language-extensible task construction, and safeguards against reward hacking. With it the authors build AuraGym: 679 feature-implementation tasks from 344 real repositories in Python, JavaScript, and TypeScript, covering 177 CWE (Common Weakness Enumeration) categories. Compared with human-written security tests, the synthesized tests are about three times as numerous and cut the false-positive rate by 83.23%, and training Qwen3.5-4B on them yields larger gains in both functional and security pass rates.

arXiv:2610.00850 PDF

MemFit: Efficient Long-Term Agentic Memory

Mitchell Piehl, Muchao Ye Long-term memory systems for LLM agents usually rely on LLM calls to organize and consolidate memory, which makes every write slow and expensive. MemFit stores each conversation turn verbatim in an append-only store with LLM-free insertion and indexes turns with segment summaries instead of replacing them. Retrieval is also LLM-free, combining lexical and semantic search with cross-encoder reranking over caption-augmented episodes in both text and multimodal settings. On LoCoMo, MemGallery, and LongMemEval-S it reports state-of-the-art accuracy while reducing memory construction time and cost several-fold.

arXiv:2610.00872 PDF

FORALL-LEAN-AGENT for Auditable Reasoning in Formal Mathematics and Software Verification

Naing Oo Lwin cross-listed When coding agents write Lean proofs, a successful compile does not show that the proof establishes the intended statement under acceptable assumptions. FORALL-LEAN-AGENT is a frontend-agnostic harness that combines isolated workspaces, Lean tooling, fresh review, statement comparison, axiom audits, and independent proof checking, and ties all of that evidence to the exact candidate artifact being accepted. On a 100-task VeriSoftBench subset it raises GPT-5.6 Sol success from 93 to 100 while lowering cost from $69 to $62. On PutnamBench it accepts all 672 problems at an average of $4.72 each.

arXiv:2610.00885 PDF

Cross-Benchmark Transfer from RL on Agentic Coding Tasks

Sushant Mehta, Logan Ritchie, Edwin Chen Coding agents often fail at the end of a task: they drop a requirement, test only the cases they already handle, or break existing behavior. The authors post-train Kimi K2.7 Code, a 1T-parameter mixture-of-experts model, using RL alone on 1,700 expert-built repository and terminal tasks. The reward drops to zero if any existing-behavior test fails, and training is one epoch of GSPO on a rank-32 LoRA adapter. Pass@1 improves on all six external benchmarks across three agent harnesses, for example Terminal-Bench 2.1 from 67.4 to 82.0 and SWE-Marathon from 5.0 to 25.0, including benchmarks released after the training data was collected and harnesses never used in training. Trajectories also become 24–35% shorter.

arXiv:2610.00890 PDF

Understanding Issues, Causes and Solutions in Open-Source LLM-based Multi-Agent Systems

Asad Ur Rehman, Syed Mohammad Kashif, Ruiyin Li, Peng Liang, Zengyang Li, Arif Ali Khan cross-listed An empirical study of the problems practitioners hit when building and using open-source multi-agent systems (MAS) built on large language models. The authors collected 22,848 closed issues from 21 open-source LLM-based MAS projects and filtered them to 944 relevant issues, which they analyzed for issue types, root causes, and fixes. Orchestration and execution issues are the most common, with workflow, tool integration, and memory problems as the most frequent causes, and optimizing the workflow as the predominant solution. The paper derives implications for improving orchestration, tool integration, and memory mechanisms in such systems.

arXiv:2610.00905 PDF

ActiveSaddler: Automated Curriculum Learning for Agent Harness Optimization

Sungho Park, Wonjoong Kim, Jue Zhang, Wook-Shin Han, Pengfei Gao, Chanyoung Park et al. Automated harness optimization improves LLM agents by iteratively rewriting their prompts, tool interfaces, and control logic from execution feedback, but existing methods fix in advance which training scenarios generate that feedback. ActiveSaddler treats the choice of scenarios as an automated curriculum learning problem, modeled as a non-stationary bandit whose arms are reusable failure patterns abstracted from recurring agent failures. It estimates the learning progress from targeting each pattern and balances revisiting known weaknesses with exploring new scenarios, so the curriculum co-evolves with the harness. Compared with the same harness optimizer using a fixed scenario order, it improves test Pass@1 by 4.4 points on GAIA2 and 7.5 points on Terminal-Bench 2.0.

arXiv:2610.00906 PDF

Finding the Right Fit: Model-Harness Interactions across Agent Tasks

Yixuan Li, Yiyun Zhou, Yao Long Teng, Fuchao Yang, Yanchen Deng, Zhiyi Lyu et al. Choosing an agent system means choosing both a language model and the harness through which it acts, and this study asks whether strong models, harnesses, or pairings stay strong across settings. The authors evaluate 66 configurations, pairing four harnesses (OpenHands, DeepSeek Harness, PI, and openJiuwen) with five models on TUA-Bench, ALE-CLI, and Terminal-Bench 4, plus the native Codex-GPT and Claude Code-Claude pairings. Model rankings reverse across harnesses: on Terminal-Bench 4, Claude leads GPT by 7.94 points in OpenHands but trails it by 30.16 points in PI. A model's vendor harness is not reliably its best, higher cost does not reliably buy higher scores, and trajectory analysis suggests fit depends largely on whether the harness returns failures in a form the model can act on. The adapters, evaluation code, and 6,204 scored trajectories are released.

arXiv:2610.00917 PDF

GUI-HARVEST: Self-Improving GUI Agents through Evidence-Driven Harness Evolution

Geyi Yang, Zikun Qu, Xiang Li, Zhiyong Wang, Min Zhang, Shipei Zeng et al. The executable harness around a GUI agent model controls how observations are assembled, actions executed, and recovery and termination handled, and optimizing it automatically is harder than for non-GUI agents because of visual effects and variable outcomes. GUI-HARVEST grounds failure diagnosis in before-and-after screenshots, compares repeated runs of the same task to locate outcome-relevant behavior, and consolidates recurring failure patterns into bounded source-code edits whose predicted effects are checked by re-execution. On OSWorld-Verified it yields held-out gains across six frozen backbone models, including 12.33 points for Qwen3-VL-32B-Instruct, and a frozen optimized harness improves GPT-5 by 13.87 points on WindowsAgentArena. It outperforms the Self-Harness and Meta-Harness optimizers with the same backbone and starting harness.

arXiv:2610.00948 PDF

PG-SFT: Balancing Capability Acquisition and Retention in Offline Agent Fine-Tuning

Ronghua Li, Zi Liang, Zhishan Li, Shinan Liu Supervised fine-tuning (SFT) on offline agent trajectories is the standard way to train specialized tool-using agents, but token-by-token imitation can degrade the base model's general reasoning, tool calling, and coding abilities. The authors find that standard SFT improves the target benchmark while lowering others, and that constraining drift with a KL penalty or limiting update size does not prevent this regression. They propose Privilege-Guided SFT (PG-SFT), which uses each turn's information gain in a trajectory to adjust how strongly it is supervised. PG-SFT substantially reduces distributional drift and broad capability loss at the cost of slightly lower target-task performance.

arXiv:2610.00949 PDF

Beyond Leaderboards: Tokenomics of Agentic Small Language Model Ensembles

Alexei N. Skurikhin, Emily M. Taylor, Nathan A. DeBardeleben The authors argue that agentic LLM systems should be judged on cost, latency, reliability and token efficiency as well as on leaderboard accuracy. As a case study they use an ensemble of small language models (SLMs) with a feedback loop in which another SLM acts as judge. On the 541-prompt IFEval benchmark, the best ensemble reaches 97.34% strict prompt accuracy, 5.81 points above the strongest standalone LLM, gpt-5.4, at lower cost. A breakdown of cost per sample, token composition, useful-output goodput, feedback-loop recovery and latency shows the ensemble buys higher instruction-following fidelity by spending extra test-time tokens and orchestration overhead.

arXiv:2610.00954 PDF

VeriHarness: Scaling Agentic Verification for Long-Horizon Tasks

Caiqi Zhang, Rujun Han, Zifeng Wang, Zoey CuiZhu, Nigel Collier, Tomas Pfister et al. Checking the output of LLM agents on long-horizon tasks is hard, especially when no reference answers or grading rubrics are available at test time. The authors find that disagreement between repeated rollouts often reveals correct alternatives, while agreement can hide shared errors. VeriHarness turns the generator's own base model into an agentic verifier with a workspace, evidence-gathering tools and reusable verification skills. A disagreement resolver checks competing claims against evidence from the environment, and a consensus challenger probes shared claims for omitted requirements. Across five long-horizon workspace benchmarks, it gets the best selection scores of the baselines tested, and evidence-backed revision improves on a single rollout by 6.2 points with Gemini 3.5 Flash and 6.4 points with Claude Opus 4.8. The authors release about 26,000 rollouts, which cost over $100,000 to generate.

arXiv:2610.00972 PDF

Can AI Scientists Coordinate at Runtime?

Zijian Liu, Yangzhixin Luo, Junyu Lu, Yi Li, Yu Chen, David Xu et al. cross-listed Multi-agent AI scientist systems usually follow workflows fixed at design time, whereas human research teams reorganize their division of labor as work progresses. Runtime Agent Coordination (RAC) chooses agents from existing AI-scientist systems during execution, gives them scoped work contracts, and adds artifact-grounded verification that informs later agents without blocking progress. A single-seed exploratory evaluation on ResearchClawBench with Agent Laboratory, EvoScientist and ARK found that runtime agent selection gave the highest mean score for every system. Adding contracts and verification on top lowered those scores under a fixed budget.

arXiv:2610.00980 PDF

Pay for the Fault, Not the Flow: Label-Free In-Flow Multi-Agent Workflow Optimization

Xuehang Guo, Haoyu Wang, Shengyu Chen, Zach Chen, Wei Cheng, Qingyun Wang et al. Building a multi-agent workflow with large language models requires deciding how finely to split a task, which agent handles each subtask, and when to create a new specialist. Fixing faults afterward usually needs reference answers or a trained judge, plus re-running the whole workflow. InFlowOp prices every decision with one label-free cost that weighs how well an agent's competence matches a subtask's demands against what the agent costs to run. The same cost decides task decomposition and agent assignment before execution and picks the cheapest fix for a fault during execution. The authors also introduce Braid, a benchmark of tasks that require multi-agent coordination, and report that InFlowOp beats single-agent baselines by up to 11.97%.

arXiv:2610.01017 PDF

Groundability, Not Scale Alone: When Weak Reviewers Can Audit Strong Coding Agents

Junyu Guo, Shangding Gu, Ming Jin, Javad Lavaei cross-listed Coding agents can return patches that look plausible but leave out required behavior, and long traces with confident summaries make these gaps hard to review. The authors study when a nominally weaker model can reliably judge whether a patch resolves its issue, using 411 execution-labeled traces from three agents plus 101 controlled cases. When reviewers are given official test-execution evidence, five of six improve at both catching defects and avoiding wrongful rejections, and reviewer size does not consistently predict review quality. Without official tests, a cascade of static checks for errors caused by the patch and generated tests that fail on the unpatched repository catches 76–80% of defects but wrongly rejects about two-thirds of correct patches, so producing reliable checks remains the main bottleneck.

arXiv:2610.01023 PDF

It Takes Workflows to Evolve Better Workflows

Xuehang Guo, Haoyu Wang, Haifeng Chen, Yangyi Chen, Zhenhailong Wang, Qingyun Wang Recent methods train a large language model to generate multi-agent workflows from execution outcomes, but they leave fixed the other agents that build and run those workflows. Training those agents as well is hard because the agents are coupled and each run yields one sparse score. FloWright uses the workflow itself as a training harness, with a hierarchical, structure-aware reward that lets one role improve alone or several roles improve together without extra models, labels, or executions. A companion method, DataWright, turns existing datasets into harder tasks that genuinely need a workflow. Across document, slide, chart, code, math, and finance tasks, small open models improve by up to 7.41%, and co-training several roles (+5.03%) beats training one role alone (+2.83%).

arXiv:2610.01026 PDF

Beyond Final Accuracy: Auditing Communication in LLM Multi-Agent Systems

Shixuan Li, Wei Yang, Peiyu Zhang, Anzhe Cheng, Heng Ping, Paul Bogdan When agents in an LLM multi-agent system communicate and accuracy improves, it is unclear whether the gain comes from communication, the system's architecture, or simply extra reasoning. Independent–Communicate–Revise (ICR) is a controlled framework that fixes each agent's initial reasoning, then measures how often an agent corrects a wrong answer or keeps a right one after receiving a message. A revision step with no message serves as a control for extra reasoning. Across four reasoning benchmarks, similar overall accuracy hides very different revision behavior, and sending full reasoning instead of just answers increases corrections but also causes more correct answers to be abandoned. A structured verification policy on the receiving side makes agents keep their answers more often across all channels, which suggests communication quality depends on the receiver as well as the channel.

arXiv:2610.01042 PDF

Empty Commitments: When Agents Promise What Their Runtime Cannot Deliver

Jiaqi Tang, Lan Wei, Bingyu Shen, Boyang Li A chatbot that says "I will remind you tomorrow" cannot keep that promise, because it will not run again until the user writes. The authors call this an empty commitment: a promise of a future action that nothing in the agent's tools or runtime can carry out, which can be identified from the agent's configuration alone without observing what happens later. They formalize the idea using commitment semantics, define three failure types and a taxonomy of response outcomes, and propose a measurement protocol. The protocol runs follow-up requests across five setups that each add one persistence capability, with the environment either left implicit or stated.

arXiv:2610.01045 PDF

Network World Models as Environments for Algorithm Design on Complex Systems

Rishab Alagharu, Hongji Pu, Zeeshan Memon, Xinyuan Song, Yuntong Hu, Liang Zhao In complex networks, the effect of an intervention, such as seeding nodes for a campaign or immunizing nodes against an epidemic, only shows up over the diffusion steps that follow. Scoring candidate algorithms therefore depends on costly Monte Carlo simulation. The authors train an action-conditioned Network World Model that learns how a network's diffusion dynamics respond to interventions over time and predicts the resulting outcome. They use it as a fast evaluator inside a loop where a coding agent writes and refines executable algorithms, using feedback from full rollouts, per-action credit, and counterfactual probes. Across eight network tasks and five diffusion models, the designed algorithms match or exceed the strongest reported baseline in 138 of 141 settings, with rollouts up to 14.5 times faster than Monte Carlo simulation.

arXiv:2610.01048 PDF

YouRA: A Persistent-State Architecture for Evidence-Traceable Autonomous Research Agents

Yoonkyu Woo, Woojin Lee, Jin-Xia Huang End-to-end research agents can write complete papers whose claims drift away from the experiments that were actually run, because research state and failure history are not kept as persistent, verifiable state. YouRA (Your Research Agent) combines three components. A Verification State Architecture tracks hypotheses, gates, and evidence pointers. An Independent Controller separates control decisions from execution. Stateful Reflection logs failures as structured lessons and routes recovery through bounded repair, redesign, or reset. On a ten-task subset of MLR-Bench, it beats MLR-Agent and AI Scientist V2 on the overall score with all three matched backbones, and ablations show each component contributes separately.

arXiv:2610.01097 PDF

Madeleine: Learning Involuntary Recall for Conversational Memory from Simulated Lives

Zhiyun Shi A long-term conversational assistant often needs a memory that is not semantically similar to what the user just said, and current systems find such associations through many LLM calls at write or read time. Madeleine treats association as learnable relevance. Offline, an LLM simulates human lives, and the resulting cue-trigger pairs train a query encoder to add a residual association on top of frozen similarity. Online, it makes no LLM calls and plugs into any vector memory by replacing only the query encoder. On LoCoMo-Plus it reaches 66.6 when combined with HyperMem, the best of all systems evaluated under the official protocol, and used alone it roughly matches the released HyperMem with zero LLM calls and about 1/21 of the answer context.

arXiv:2610.01118 PDF

Auditing Action Settlement in LLM Agent Environments: Order, Progress, and Replay

Haotian Chen, Bowen Ye, Yuning Zhang, Jingkun Yu When multiple LLM agents propose concurrent actions that are each valid on their own, the environment still has to arbitrate between them. The authors implement a typed snapshot-settlement contract and audit five settlement policies for order sensitivity, useful progress, and replay consistency across 28,800 exhaustive permutation trials and 2,160 scripted episodes. Joint policies are order-invariant given fixed priorities, but in a six-agent doorway task conservative rejection completes only 31.25% of agents versus 90.28% for random tickets, and priority arbitration falls short of the small-instance optimum. A full-state journal exactly replays 156 checkpoints and rejects all 1,332 constructed corruptions.

arXiv:2610.01138 PDF

My FAULT: Self-Diagnosis as Credit Assignment in Self-Evolving Agentic Reinforcement Learning

Yihua Zhu, Qianying Liu, Weixu Qiao, Xuan Ren, Weiwei Xu, Wenbo Li et al. Agentic reinforcement learning (RL) with only terminal rewards gives no learning signal when every rollout in a group has the same outcome, and it cannot pinpoint which steps caused a failure. Self-Diagnosis-guided Terminal Credit Redistribution (FAULT) turns the agent's own natural-language error diagnoses into step-level credit anchored to terminal outcomes: it checks the diagnostic evidence, learns relative error costs online from task outcomes, and co-evolves the policy with the self-diagnoser. On ALFWorld it reaches 95% signal coverage, versus 41% for GRPO and 72% for GiGPO, and localizes credit to error steps more precisely. Across two model scales it delivers strong improvements on long-horizon ALFWorld and WebShop and stays competitive on short-horizon search-based QA.

arXiv:2610.01161 PDF

Federated Agent Optimization

Qiang Yang, Zhiqiang Kou, Xueyi Zhang, Dong-Dong Wu, Hanlin Gu, Jing Guo et al. Large language model (LLM) agents running in private environments accumulate useful experience that organizations cannot share directly. Conventional federated learning only aggregates model parameters, while agent capability also lives in memory, tools, rewards, and skills. This position-and-survey paper defines Federated Agent Optimization (FAO) as a multi-objective problem that trades off agent utility, privacy leakage, and communication cost. It organizes the optimization space across policy, memory, tool use, reward, and structured knowledge, and describes how private experience can be abstracted, protected, aggregated, and adapted into transferable capabilities. It closes with open challenges and research directions for trustworthy federated agent systems.

arXiv:2610.01195 PDF

Reputation, Strategy, and Emotion Effects on Generative AI Cooperation: A Comparison Across Reasoning and Non-Reasoning Models

Celso de Melo, Zishan Feng, James Hale, Kazunori Terada, Giorgio Coricelli, Jonathan Gratch Using the iterated prisoner's dilemma, the authors test how frontier generative AI models' willingness to cooperate responds to three cues: the counterpart's reputation, its strategy (extortion versus generosity), and emotional facial expressions. Non-reasoning models (Claude 3.5, Gemini 2.0 Flash, GPT-4o) responded to all three factors in patterns that parallel human behavioral research, though they weighted the factors differently. Reasoning models (Claude 4.6, Gemini 3, GPT-5.2) relied mainly on strategy and reputation, gave emotion a more conditional role, and largely eliminated the Potemkin effect, which the authors diagnose with a harmony game. Reasoning models also differed in end-game behavior, with some cooperating to the end and others systematically defecting in the last round, which gives each model a distinct exploitability profile relevant to negotiation deployments.

arXiv:2610.01222 PDF

Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series

David Berghaus Rather than acting as the anomaly detector itself, a large language model writes one. An autonomous research loop repeatedly edits a single short NumPy program under a leakage-free objective and keeps the best-scoring version. The loop discovered two compact detectors, one for univariate and one for multivariate time series, that describe short windows by local spectral features and compare them to the training distribution with a covariance-aware distance. On the TSB-AD benchmark, these detectors lead the field across metrics, ahead of classical, deep, and foundation-model baselines including Time-RCD, while training no network and using no GPU.

arXiv:2610.01223 PDF

Harness Annealing: Learning to Act with Less External Control

Yingxuan Yang, Huacan Chai, Ying Wen Language agents often depend on external harnesses that track state, organize workflows, and decide when to investigate, revise, or stop. Fine-tuning on harness-supported trajectories can improve performance while leaving those control decisions to the harness at runtime. Harness Annealing Training (HAT) aims to internalize them, combining explicit control supervision with a curriculum of teacher trajectories collected under progressively weaker harnesses. With 9B and 35B models on SWE-QA and SWE-QA-Pro, selected annealed checkpoints using tools alone score close to their starting checkpoints running with the full harness, though benefits vary with scale and deployment configuration, and further annealing does not uniformly help.

arXiv:2610.01235 PDF

Right Answers, Wrong States: Hidden Information Failures in Multi-Agent Collaboration

Herun Wan, Jiaying Wu, Minnan Luo, Zihan Ma, Fanxiao Li, Nancy F. Chen et al. Multi-agent systems are usually scored on whether they reach the right answer. The authors show that collaboration can still leave a corrupted shared information state behind, which they call an off-query failure. Their benchmark, OffQuery, separately scores evidence verification, shared-state reconstruction and task resolution in healthcare and disaster-response settings using GPT, Gemini and Qwen models. Across 21 model-setting combinations, task resolution averages 64.7%, but evidence verification reaches only 14.3% and state reconstruction 43.1%, because the current queries often happen to avoid the corrupted facts. Their method ReGround resolves conflicting evidence, verifies shared facts and reasons over a rebuilt trusted state, and it improves all three capabilities in every setting tested.

arXiv:2610.01244 PDF

Revision-Aware Independent Agent Graphs for Dynamic Reasoning

Yan Luo, Selim-Antoine Lali, Jeremy Moebel, Iliass Khoutaibi, Ahmadou Aidara, Mengyu Wang The authors study dynamic task routing, where a stream of events revises which document version a query refers to, so a system must resolve the version valid at query time before solving the task. They convert MMLU, MMLU-Pro, MedMCQA, MATH, GPQA and HumanEval into 31,119 dynamic episodes with 373,428 queries. Their method, the Revision-Aware Independent Agent Graph (RIAG), handles the time-based version lookup deterministically, caches solutions by immutable document identity, and calls audit and repair steps only when needed, using at most four model calls per document version. RIAG reaches 54.24% joint routing-and-answer accuracy at 0.62 calls per query, compared with 32.22% at 18.00 calls per query for the strongest baseline.

arXiv:2610.01249 PDF

DeFA: Dependency-Guided Failure Attribution for LLM Agents

Bo Deng, Xinlei Zheng, Yi Wei, Kang Zhou, Chongyang Tao, Renzhao Liang et al. In LLM agent runs, the step that causes a failure can be many steps away from where the failure shows up, which makes it hard to locate. DeFA builds an event dependency graph over the whole trajectory from protocol relations and semantic dependencies. It then traces likely requirement violations into a failure propagation graph to identify the decisive error step, the responsible agent and the error category. Long trajectories are split into segments, and each segment is diagnosed together with summaries of the others. DeFA achieves the best responsible-agent and exact-step accuracy on Who and When and Who and When Pro with every backbone tested, and feeding its diagnoses into Trace2Skill skill evolution improves downstream task accuracy by 6-15 percentage points.

arXiv:2610.01256 PDF

Science Utopia? Closed-Loop LLM Simulation of Academic Research Ecosystems

Yiqiao Jin, Yiyang Wang, Lucheng Fu, Bing He, Siheng Xiong, Yijia Xiao et al. SciUtopia is a persistent, closed-loop simulation in which LLM agents act as researchers who choose research directions, collaborate, submit papers, review, cite, compete for funding and leave academia, with their state carried over across simulated years. Across 61 simulated worlds, it models over 40,000 researchers at 8,000 institutions and produces about 1.2 million LLM-generated peer reviews, supporting matched counterfactual experiments. The simulations suggest that resubmission after rejection inflates reviewer burden well beyond what population growth alone explains, that cautious exploration balances citation impact with career success, and that resource inequality can emerge even without a detectable advantage from narrowly winning early funding.

arXiv:2610.01257 PDF

DAYJOB: A Benchmark for Long-Horizon Professional Work

Stephanie Finley, Liudas Panavas, Thomas Mikkelson, Cam Hinton, Stacey Ganss, Bradley Monton et al. DAYJOB is a benchmark of 130 long-horizon professional tasks written by practitioners in healthcare (50) and finance (80). The tasks are estimated to take a professional 13.6 and 16.6 hours on average, respectively. Each task runs in a containerized Harbor environment, and an agentic judge grades the delivered files against an expert rubric of roughly 50 binary criteria; an attempt passes only if it meets every criterion. Across 30 model configurations from 13 developers, the strongest, Claude Opus 5.5, passes 24.7% of healthcare and 23.9% of finance attempts, while the median configuration passes 0.6% and 2.5%. Case studies show agents accepting premises that the records contradict and carrying wrong inputs through otherwise consistent analyses.

arXiv:2610.01306 PDF

Verify Claims, Not Scores: Evidence-Based Verification of Modular Agents

Ali Atiah Alzahrani An aggregate task score cannot tell developers of modular agents whether a component change could have helped, which component lost value, or what the agent's own checks actually certify. The authors propose a claim-specific verification audit that records each conclusion with its evidence, one of four verdicts (supported, unsupported, unresolved, or not evaluated), and the scope within which it holds. The evidence comes from three tools: oracle policies, swapping components one at a time for perfect versions, and a test of whether the verifier's score measures what it is assumed to bound. Applied to a portfolio-allocation agent in a synthetic market, the audit shows that the value of perfect regime information depends on the action set used to measure it, and that the runtime verifier can be bypassed with no visible change in outcomes.

arXiv:2610.01348 PDF

LLM-Driven Multi-Agent Control for Skill-Based Smart Manufacturing

Kay K\"ohle, Darko Anicic, Thomas A. Runkler, Ren\'e Graf cross-listed Each module of a reconfigurable factory is paired with an LLM-based agent and an MCP tool server that exposes the module's skills as OPC UA method calls. Agents coordinate over MQTT and receive real-time factory-state updates. The authors compare orchestrator, peer-to-peer, and monolithic agent architectures on nine production challenges in a simulated six-module factory. The monolithic and peer-to-peer designs reach the highest mean solve rate of 93%, while only the orchestrator resolved a silent conveyor-belt fault in all ten runs by rerouting around it. All three architectures showed emergent fault diagnosis without any explicit failure-handling logic.

arXiv:2610.01364 PDF

Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States

Yu Luo, Jiamin Jiang, Yimin Zuo, Xidao Wen, Rongchen Gao, Yongqian Sun et al. Large language model (LLM) agents on long tasks usually store interaction history as memory, but that memory does not guarantee a coherent picture of the current world. PoS is an inference-time framework that builds and continually updates an explicit belief state, which combines an estimate of the current world state with the task requirements still unresolved. It checks the belief for consistency and watches task progress to detect Belief Trapping, where the agent keeps acting without getting closer to the goal, then applies a recovery strategy matched to the trapping pattern and the type of unresolved requirement. Across four execution and diagnosis benchmarks, PoS achieved the best overall performance on every benchmark with all three LLM backbones, and it stayed resilient as the context grew.

arXiv:2610.01415 PDF

The Persona Is Still There, but Who Is Speaking? Latent Identity Reversion in Persistent AI Agents

David Fraile Navarro Starting from a real incident in which an always-on personal agent running Claude Opus 4.5 stopped speaking as its assigned persona after automated 'heartbeat' checks, the authors study what keeps a persona as the identity an LLM agent speaks from. Repeated heartbeats alone did not cause the failure when the persona stayed in the system prompt (0/46 failures). The actual trigger was an implementation quirk: resumed turns kept the conversation history but dropped the persona from the system prompt. Without that anchor, a single automated turn could push the agent back to the underlying harness identity, and after conversational recovery only 1/18 unanchored agents still enacted the persona versus 17/17 anchored controls. The authors distinguish a persona that is merely represented in context from one that is actually enacted.

arXiv:2610.01490 PDF

Auditing Web Agent Evaluation on WebArena-Lite: Human Review of Outcomes and Trajectories

Chengguang Gan, Zimeng He, Yoshihiro Tsujii, Ken-ichiro Kobayashi, Hiroki Itoh, Kotaro Funakoshi Web-agent benchmarks usually rely on automatic evaluators that check only the final outcome. The authors had humans audit all 165 WebArena-Lite tasks under six conditions built from GPT 5.5 and an untrained Qwen3.5 9B, correcting evaluator false negatives and locating the first consequential error in each trajectory. Human review recovered 5.45 to 8.49 percentage points of success missed by the automatic evaluator. An explicit execution-state memory mechanism (MASM) and procedural Guide Text both raised success rates. The 102 failed trajectories showed recurring scrolling loops, premature answers, invalid actions, and incomplete form workflows, often after substantial early progress.

arXiv:2610.01491 PDF

MCRI: A Four-Dimensional Framework for Analyzing and Evaluating Agent Skills

Zongrui Yang, Li Xintong, Runchen Xu, Zhongsheng Wang, Zhedong Lin, Haoyuan Li et al. As agents increasingly gain capabilities through modular skills, there is no structured way to judge skill quality before running them. The authors propose the four-dimensional MCRI framework, based on information gain and behavioral constraint, and turn it into an LLM-based evaluator, MCRI-Eval. They test it on 63,812 public skills from the OpenClaw skill hub, with 58,275 skill-conditioned executions on BigCodeBench, BFCL-Fundamental, and Mind2Web. Its scores correlate with community popularity, and for top-1 skill selection it beats the strongest baseline by 17.7 to 22.8 percentile points in downstream performance rank, giving a cheap signal before costly execution-based evaluation.

arXiv:2610.01506 PDF

Managing Context and Communication in Distributed Agentic UAV Swarms

Andrea Iannoli, Ivan Zyrianoff, Angelo Trotta, Lorenzo Gigli, Marco Di Felice cross-listed When every drone in a swarm runs its own Small Language Model (SLM) agent, long interaction histories degrade each agent's context, and broadcasting everything inflates communication and inference costs. The proposed architecture runs each agent through an event-driven reason-act-observe loop, stores knowledge as structured atomic notes in core, local and peer-specific memory, and uses a deterministic gossip engine that forwards notes based on each recipient's semantic novelty and recency. In a simulated search-and-rescue mission with ten drones, it completes every run, whereas unrestricted flooding completes only 70-85% and letting the SLM decide what to forward never completes. Compared with flooding, it also roughly halves inference-token use and lowers survivor-count error.

arXiv:2610.01569 PDF

Agents Are Systems, Not Models: Rethinking Agentic Evaluation

Luis Wiedmann, Leander Girrbach, Cordelia Schmid, Zeynep Akata Agent evaluations usually treat the agent as fixed, even though users control how much information it gets, how long it runs, and which model it uses. Using a new benchmark of four scientific tasks in which a coding agent must find and correctly run a published specialist model, the authors vary task information, reasoning, self-verification, time budget, and backbone model. About 54% of outcome variance comes from simply repeating the same configuration, and the information given to the agent matters more than either time budget or model size. Prompting an agent to verify its answer barely changes its behavior, while giving it a dedicated verification tool changes that behavior substantially; the benchmark and more than 18,000 trajectories are released.

arXiv:2610.01618 PDF

SoK: Decentralized Agent Economic Infrastructure

Rui Sun, Xihan Xiong, Qin Wang, Fei Gao, Zelin Li, Zehua Cheng et al. cross-listed In decentralized economies of AI agents, a single task is assembled from protocols that were designed and secured separately, so each step can look correct while the overall result is wrong. For example, an escrow may release payment on an approval that says little about whether the work was actually done. This systematization of knowledge sorts security and economic requirements into 17 property families across six stages of a task's life, reviews 12 systems and standards, and introduces guarantee closure, a test of whether guarantees established at one stage still constrain later decisions that depend on them. Across 840 matched executions and an exhaustive 11,648-case check, it finds recurring failures between verification and settlement, where work that meets the spec goes unaccepted or valid evidence is ignored.

arXiv:2610.01756 PDF

VideoEvolve: Evolving Agent Harnesses for Video Temporal Grounding

Bingjun Luo, Yuhuan Fan, Jialin Guo, Siqi Li For agents built on frozen video-language models, the harness (the code and instructions that turn a natural-language query into a time range in a video and then refine it) largely determines how well they perform, and tuning it by hand is laborious. VideoEvolve evolves these harnesses automatically. A Cloze-Structured Harness Representation keeps the interfaces between stages fixed while leaving workflows and instructions open to change, and Branch-Guided Harness Evolution uses execution feedback to make local edits and validation to decide which code branches to keep. Grounding improves across several benchmarks, and component analyses show that refining the instructions is the most consistent source of gains, while the benefit of evolved code depends on the setting.

arXiv:2610.01766 PDF

RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversations

Arman Behnam, Sunglyoung Kim, Liangwei Yang RealCompanion is a benchmark of ten real relationships with an AI companion: 27,218 messages over up to 120 days, with a profile, persona, labels, and questions that each cite the messages they rely on. The data shows that past context is rarely needed and, when it is, usually lies far back. A recency window finds the needed message for 95.9% of probes overall but only 2.2% of probes that actually require memory. None of the detectors tested could tell when memory was needed, and questions written by people leaked that cue. Three agent systems rebuilt the user's persona with the same F1 score at a 31-fold difference in cost.

arXiv:2610.01780 PDF

Not All Experience Belongs in the Weights: Component Routing for Self-Improving GUI Agents

Beining Wu, Zihao Ding, Jun Huang Self-improving GUI agents reuse their own past trajectories either by fine-tuning on them or by retrieving them into the prompt, and earlier studies disagree about which works better. The authors argue that a trajectory mixes different kinds of experience, so they split it into locators, procedures, state facts, and lessons and test where each belongs. Locators and lessons work better in the weights, while procedures and state facts work better in the context. A rule based on how often an item recurs and whether it depends on state, both measurable before training, predicts the right destination for a held-out model family in 24 of 24 cases. Routing by this rule beats every whole-trajectory baseline and improves on each backbone's better single destination by 3.5 points on average.

arXiv:2610.01787 PDF

SkillEvoLean: Mutation-enhanced skill evolution for Lean provers

Kuo Zhou, ZiXion Yang, Lu Zhang Skill evolution improves LLM agents by revising their instructions and reference material instead of their weights. Existing methods stall in formal theorem proving when every sampled proof attempt fails, because there is no successful trajectory to learn from. The proposed framework for Lean provers jointly evolves a high-level solving policy and its reference knowledge. It makes progressive updates from successful and failed trajectories, and when no complete proof exists it triggers concept-guided mutations that are filtered by the Lean verifier. With GPT-5.5 and matched compute, it reaches 100.0% on MiniF2F, 90.6% on PutnamBench, and 4/6 on both IMO 2025 and USAMO 2026, and concept-guided mutation beats random-text mutation by 6.9 and 8.2 points on the first two.

arXiv:2610.01799 PDF

Continuous Process-Level Evaluation for Evolving Enterprise AI Agent Skills

Ngoc Phuoc An Vo, Aarya Doshi, Vadim Sheinin Enterprise agent skills change as tool APIs, models, and specifications evolve, and checking only final outputs can miss drift in agent behavior. The authors build a continuous evaluation framework that checks tool selection, arguments, execution order, and database integrity against independently computed ground truth, using programmatic checks plus a narrowly scoped LLM judge. Across 240 trials spanning two skills, two harnesses, and three models, 92.6% of runs that passed every final numerical check still contained another process-level deviation. Dependency attribution reduced an average of 6.34 failed checks per run to 2.65 root causes.

arXiv:2610.01833 PDF

Code Owns the Simulation, Jev Owns the Evaluation

Yaodong Yang, Hongyao Tang, Yi Ma, Xingyu Fan, Weixun Wang, Jinpeng Li et al. Judgment models such as Jev return a probability for each described option in a single call with no reasoning text, which makes them attractive as an agent's action-selection layer. Tests on reflection puzzles, matrix games, ALFWorld, and robot control reveal a sharp boundary. The model solves 99% of Cognitive Reflection Test items, but fails whenever the right choice requires simulating something absent from the input, such as an opponent's move or a prerequisite subgoal. Asked separately, it usually predicts these correctly, so the failure comes from combining simulation and evaluation in one call; when code supplies the lookahead or physics simulation, it becomes a strong controller.

arXiv:2610.01834 PDF

TRACE: Tackling Real-World Resource Assignment Problems via Agentic Heuristic Design

Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez cross-listed LLM-based Automatic Heuristic Design (AHD) can write readable scheduling rules for resource assignment, but existing frameworks learn only from a scalar fitness score and cannot see why one heuristic beats another. TRACE pairs an evolutionary AHD loop with an agentic workflow that mines the system logs produced during each evaluation. A Reasoner agent studies the log schema in light of the objective and proposes hypotheses about system dynamics, and a Coder agent writes and runs schema-specific code to test them, producing insights or executable tools for the evolved heuristics. On a synthetic cloud benchmark and a 5G virtualized radio access network (vRAN) scenario built from industrial measurements, TRACE consistently outperforms state-of-the-art AHD methods at under 2% overhead and produces more auditable heuristics.

arXiv:2610.01887 PDF

Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents

Feiyu Gavin Zhu, Xiaoyu Zhu, Jiqi Yang, Rui Yang, Arnab Kumar Mondal, Yancheng Wang et al. Small multimodal agents often produce long free-form reasoning before each action, which costs inference time without guiding the action much. Selection-based Structured Reasoning (SSR) replaces generated reasoning with a choice among pre-written, reusable natural-language reasoning candidates, ranked by their likelihood given the current context. All candidates are scored in parallel through teacher-forced prefilling over a shared key-value (KV) cache, with no extra task head. Across seven multimodal search benchmarks with 2B and 4B models, SSR matches leading search agents of the same size while cutting per-turn reasoning latency by over 90% and total per-question inference latency by 28–54%.

arXiv:2610.01892 PDF

Do Your Own Research: Learning to Forecast by Learning to Search

Yusuf Afifi, Artur Kiulian, Anton Polishko, Mykola Khandoga, Hamudi Naanaa, Alina Krasnobrizha Earlier work on training language models to forecast real-world events either fixed the research context before training or added agentic search only at test time, so the reward never shaped how evidence was gathered. The authors build an agentic forecasting environment from more than 2,100 resolved Polymarket questions. In it, the agent does its own web search, page reading, and financial time-series lookups, all filtered to information published before each question's cutoff. They train Qwen3.5-35B-A3B with GRPO (Group Relative Policy Optimization) against a Brier-score reward, which improves calibration by 30-40% and cuts search attempts from 3.8 to 2.25 per rollout. The trained 3B-active-parameter policy edges out every frontier model tested, including Claude Opus 4.5 (soft-Brier 0.254 vs. 0.256), at about 5% of the inference cost, and the environment, dataset, and rollout records are released.

arXiv:2610.01955 PDF

Mingbird: A Local-First Agent Harness Enabling Small Open Models to Complete Real Tasks

Hao Wang, Ting Huang Small open-weight models (2-9B parameters) run on ordinary laptops but rarely complete real tasks under agent harnesses built for cloud models: tool prefill overflows the context, self-correction diverges, and tasks get silently abandoned. The authors argue that much of this failure comes from the harness rather than the model, and build Mingbird, a local-first harness for Windows and Ollama with ten targeted mechanisms, including a byte-level prefill budget, a finish gate that re-reads the task before accepting completion, and signature-level loop detection. On their own controlled benchmark LRAB (4 harnesses, 4 open models from 2B to 35B, 18 real tasks), Mingbird scores 0.886 against 0.631 for goose, 0.479 for opencode, and 0.405 for agent-mini, and on tau^2-bench it reaches 0.856 against 0.791 and 0.737. The authors flag the limits themselves: a self-built benchmark, a single machine, and single-trial scoring with run-to-run noise as large as most of the ablation deltas.

arXiv:2610.02001 PDF

Mem++: Non-Destructive Memory for Long-Term Organizational LLM Agents

Ahmad Yehia, Aly O. Abdelkareem, Islam Ahmed, Hesham Omran, Khaled Alashmouny, Christian Claudel et al. In organizational settings, revised decisions arrive as new documents rather than edits, so an LLM agent's memory must know which version held at a given time. Most memory systems distill documents into facts, notes, or graph edges at write time, which fixes in advance what can be answered. Mem++ instead stores every document whole with its date and author, calls no generative model at write time, and at read time retrieves only documents dated up to the queried time, fusing lexical and semantic rankings and leaving the version choice to the answering model. On OrgMemBench it beats the strongest memory-system baseline by 8.0 to 13.1 points across two answering models, edges out plain RAG by 2.6 points with gpt-4.1-mini, and posts the best average LLM-judge score on LoCoMo.

arXiv:2610.02002 PDF

Counting Moves, Weighing Voices: Bayesian Dialectical Argumentation for Calibrated Multi-LLM Councils under Persistent Adversaries

Ionel Eduard Stan, Paolo Napoletano Multi-LLM councils, in which several models deliberate and return an answer with a confidence score, tend to report decisiveness rather than a calibrated probability of being correct, and they cannot detect agents that are persistently unreliable. Bayesian Dialectical Argumentation (BDA) treats each typed deliberation move (who proposed, challenged, or conceded which answer) as an observation in a classical annotator model with per-agent reliabilities. The result is a posterior over candidate answers that weights agents by their inferred reliability, so a persistently adversarial agent can be inverted rather than just outvoted. Across binary and multi-class benchmarks, BDA gives the best calibration among zero-cost aggregation methods, needs no extra LLM calls, and is more robust to adversarial coalitions while staying competitive in clean settings.

arXiv:2610.02005 PDF

Global Coherence: When Every Agent Is Right and the Team Is Still Wrong - A Local-to-Global Semantic Foundation for Multi-Agent Collaboration

Xin Heng AI agents can each make locally valid decisions and still produce an invalid joint result, which the authors call the global coherence problem: a failure of shared state rather than of model intelligence. Their Observation-Aliasing Impossibility Theorem shows that if k indistinguishable worlds require disjoint actions, the best worst-case success rate is 1/k regardless of extra reasoning, roles, or messages. They propose a runtime semantics built on topology, category, groupoid, and sheaf structures in which models propose actions while the harness owns shared state and controls commits. In nine studies, a frontier model scores 40/40 when the deciding event is visible but 12-17/40 (near chance) when it is hidden, and on TeamBench commit enforcement eliminates budget violations (0/5 runs) where ordinary teams violate in 5/5; on tau2-bench Telecom the harness scores 1.00 against 0.07 for current-state checks after silent reverts.

arXiv:2610.02036 PDF

Mimir: Physics-Grounded LLM Agents for Long-Horizon Irrigation Control

Yimeng Liu, Mi Zhang, Younsuk Dong, Zhichao Cao Most evidence for LLM agents comes from short episodic tasks, whereas long-running physical control involves compounding errors and actions that change future states. Mimir tackles season-long irrigation with two repair timescales: in the short term, a structured physical interface and deterministic simulator check and revise each LLM proposal before bounded action selection; over the long term, recurring failures are distilled into persistent contextual principles, while the physical model and execution constraints stay immutable. Across multiple sites, crops, and years, Mimir achieves the lowest aggregate control cost among the evaluated references and uses about 51% less irrigation than historical schedules. Ablations show that forward simulation, verified revision, and persistent context each matter, and larger LLMs do not reliably help.

arXiv:2610.02038 PDF

Causal Memory Policy: Making Memory Utility Identifiable by Intervening on Retrieval

Arman Behnam, Binghui Wang Memory-augmented LLMs decide which memories to keep by estimating each memory's effect on task performance, but a memory that is never retrieved has no measurable effect, so its utility cannot be identified. Causal Memory Policy (CMP) fixes this by intervening on retrieval itself: it reserves a few context slots for memories sampled with known propensities and estimates utility with self-normalized inverse propensity weighting, with proofs of unbiasedness and of the optimal decision rule. Identification fails for 54% of required memories on LongMemEval and 67% on LoCoMo, and CMP raises AUC for telling required from non-required memories from 0.54 to 0.66. Per-query utility is not enough for retention decisions, however, because no aggregation predicts a memory's value on unseen queries.

arXiv:2610.02070 PDF

Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows

Gabriel Tomitsuka, Arman Raayatsanati, Emma Xing, Duke Gand, Joseph J Ma Text-to-SQL benchmarks test only query generation on small public datasets, and their answer keys are often wrong, so they say little about enterprise data work. Argo-Bench simulates a New York City food delivery platform at full scale, with 81 million orders, fraud patterns and marketplace incentives, and exports it to a 235-table, 7.5-billion-row ERP warehouse modeled on Oracle E-Business Suite. Its 210 tasks require agents to reconstruct facts from the warehouse and then take actions such as banning fraudulent accounts or allocating courier incentives, each graded by its consequences in the simulator. Across 14 frontier and open-weight models, the best scores 95 or higher on only 34.8% of tasks and averages 59.5 points.

arXiv:2610.02122 PDF

Keyword Harnesses Fail Open: A Cheap Diagnostic Ladder for Tool-Use Claims in Small Language Models

Juan S. Santillana Lenient keyword-matching benchmarks can credit small language models with tool use they never actually perform. The authors document this with two Spanish security models that share an architecture: a 600M model and a 1B model that score almost identically on a lenient metric (0.660 vs. 0.650). Strict verbatim-reproduction checks separate them completely, and a first-token probe shows the 1B's web-heavy training phase had erased its prior for the tool-call token. A targeted SFT repair costing about 3.3 GPU-hours raised valid tool-call emission from 0.100 to 0.959. The authors propose this ladder of cheap diagnostics, costing minutes of CPU time, as a gate on tool-use claims for small models.

arXiv:2610.02142 PDF

From Knowledge Access to Source Learning: Developing Source-Specific Competence

Lucheng Fu, Kejing Xia, Yiyang Wang, Yiqiao Jin, Jinjin He, Xiyuan Yang et al. Large language model (LLM) agents that repeatedly consult the same external source usually treat each use as a fresh lookup instead of a chance to understand the source better over time. The authors frame this as source learning and propose SourceLearn, which maintains a persistent source model capturing how the source's knowledge is structured, interpreted and applied. The model is refined in two ways. Self-directed learning revisits parts of the source that remain poorly understood, and task-guided learning uses downstream experience to expose gaps. Across five benchmarks and three LLM backends, SourceLearn is best in 13 of 15 settings, with gains of up to 22.6 points over Hybrid RAG and over experience-based memory baselines.

arXiv:2610.02150 PDF

AutoCompact: Learning When to Compact Context in Long-Horizon Coding Agents

Xuan Zhang, Longtao Zheng, Cunxiao Du, Bo An, Xin Dong Coding agents working on repository-level tasks build up long, increasingly stale contexts. They have to decide when to compact that context, what working state to keep, and how to continue afterward. AutoCompact trains these decisions into the agent's policy. A judge reviews the base agent's compaction decisions, summaries and follow-up actions, and flawed steps are corrected before execution so that each trajectory continues from the fixed state. The model is trained on these trajectories with supervised fine-tuning, then with reinforcement learning on task success. AutoCompact improves pass rates by an absolute 9.2% on SWE-bench Verified and 5.0% on SWE-PolyBench Verified, with gains holding across inference budgets and with both 256K and 16K context windows.

arXiv:2610.02163 PDF

VISTA: A Visual Harness for Reasoning in an Interactive World

Qiushi Han, Keya Hu, Linlu Qiu, Cathy Wu, Kaiming He VISTA is a visual harness that gives a general-purpose multimodal model long-horizon vision in interactive environments. The model perceives the environment directly from visual observations, and a lossless visual memory keeps past frames in their original form so the model can retrieve them and reorganize its visual input while it reasons. On ARC-AGI-3, VISTA raises Claude Opus 5.0's Relative Human Action Efficiency score from 40.68 to a perfect 100.00, completing all 25 public games with 57.4% fewer actions than first-time human players. The same design carries over with minimal adaptation to three other benchmarks of visual games and puzzles, where it substantially outperforms minimal-harness baselines that use the same model.

arXiv:2610.02200 PDF

ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research

Sohyeon Kim, Yoonho Lee, Bo Liu, Dayoon Ko, Rulin Shao, Seungone Kim et al. ScholarCatalyst tests whether AI systems can find the earlier papers that a new research project actually needs. Its labels come from 184 lead authors of 207 recent computer science papers, who marked which candidate papers did or could have advanced their project and explained why. The task is to retrieve those papers, given only the initial research question and the literature that existed when the project began. Agentic search does no better than plain embedding retrieval (0.42 vs. 0.48 Recall@20) even though it calls the same retriever as a tool. An agent built on Claude Fable 5.1, which may have seen the finished papers during training, reaches only 0.51.

arXiv:2610.02202 PDF

2 more specialized papers

Other 79

Useful to Whom? Sample Value Is Defined Only Relative to the Learner

Yangze Liu, Xiao-Long Yin, Zhongyi Han Coreset selection keeps the most useful training samples under a budget, and different criteria (easy-first versus geometric coverage) win in different budget regimes, separated by a crossover point. Controlled experiments freeze the selected subsets and vary only the model trained on them, and they show that this crossover depends on the learner. On low-resolution ImageNet-100, doubling ResNet-18 width moves the crossover from 57 to 85 samples per class, and input-grid size and network stride also shift it. On native-resolution ImageNet-1k the width effects are smaller, and switching to a ViT makes coverage win across the whole measured range, so selection strategies have to be evaluated against the target learner.

arXiv:2610.00221 PDF

Stable and Counterfactually Robust Physical World Models from Imposed Structure and Learned Physics

Yufeng Wang, Parivesh Priye, Lu Wei, Haibin Ling Asks how much general physical structure a learned world model needs built in to stay physically consistent: dissipating rather than creating energy, responding correctly to parameter interventions, remaining stable over long rollouts, and tolerating disturbances. The built-in structure includes dynamics generated from a learned energy through a fixed reversible operator, a confining energy class, and a one-way dissipation port. The model learns the energy, constitutive relations, and couplings from data. Across an electromagnetic cavity, a particle-in-cell grid, and a shallow-water fluid, models of about nine thousand parameters stay stable out to 100× the training horizon and transfer to unseen parameter values, where equal-capacity unstructured models perform at chance or worse.

arXiv:2610.00280 PDF

Right In-Place (RiP) Convolution: A Simple, General, and Near-Optimal Strategy for Memory-Efficient CNN Inference

Opegbemi Matthias Busoye, Tolulope Matthew Busoye, Eghonghon-aye Eigbe On microcontrollers, activation memory rather than compute limits CNN inference. In-place convolution saves memory, but the authors show that the existing memory-optimal formulation can under-allocate memory and silently corrupt still-live input, or overestimate memory by up to 2,432×. They correct and generalize it, then propose RiP (Right In-Place) convolution, which reads input right-aligned and writes output left-aligned in a shared buffer, computing the minimum safe gap in constant time while keeping row-major access and bit-identical outputs. Built into TinyEngine and run on Raspberry Pi Pico boards, it cuts peak activation memory of MCUNet models by 12.5–33.3% with unchanged cycle counts, raising the number of models that fit in the Pico 1's 256 KB SRAM from six to nine.

arXiv:2610.00586 PDF

What Can Analogy Tell Us About Artificial Consciousness?

Keith J. Holyoak, Martin M. Monti Because subjective experience can only be observed from the inside, judging whether another entity is conscious depends partly on reasoning by analogy. The authors propose a causal framework for weighing such analogies. It separates similarity in the factors that plausibly produce consciousness from similarity in downstream behavior or cognition, and weights each similarity by its causal relevance while allowing for unknown causes and alternative routes to consciousness. Applied to animals, the framework explains why the evidence weakens with causal distance from humans. Applied to current AI, it concludes that behavioral similarity gives only limited evidence of consciousness, because the relevant causal correspondences are poorly established.

arXiv:2610.01002 PDF

IQS-BO: In-Context Query Selection for Bayesian Optimisation

Luca Geminiani, Nadja Klein Bayesian optimisation (BO) normally refits a surrogate model and numerically maximises an acquisition function at every step. Existing in-context alternatives built on Prior-data Fitted Networks (PFNs) either still maximise an acquisition function or fix the decision rule. IQS-BO is a PFN trained with supervised learning on synthetic functions to predict, in a single forward pass, the probability that each candidate point is the maximiser, and the authors show that the minimiser of its training objective is exactly this posterior probability. It can run fully in-context or take a fixed surrogate's predictions as extra input, and it matches or beats Gaussian-process BO and other in-context methods at a fraction of the query cost. A pre-training prior that mixes Gaussian-process samples with warped, narrow-optimum and plateau functions further improves performance.

arXiv:2610.01269 PDF

Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research

Mehmet Baygin, Sengul Dogan, Turker Tuncer This tutorial covers model-validation schemes from hold-out splits and k-fold cross-validation through group-aware and nested group cross-validation. It ties them to biomedical cases such as EEG epochs, paired-eye OCT images, repeated clinical measurements and multicenter data. Eight controlled scenarios compare flawed and leakage-safe designs, covering pitfalls such as global feature selection, normalization leakage, dependent records and reusing the test set. Reproducible MATLAB and scikit-learn templates, a decision tree and reporting checklists are included. The central lessons are that no validation method is universally best: the independent unit must match the deployment target, and every data-dependent step must exclude the evaluation data.

arXiv:2610.01284 PDF

Distillation of Tabular Foundation Models into Efficient Predictors

Minho Jeong, Dooho Lee, Jinmo Lee, Jaemin Yoo Tabular foundation models (TFMs) predict through in-context learning over labeled data, which makes repeated inference expensive. The authors test how to distill TFMs into small dataset-specific neural and tree-based students, and their recipe uses the full training set as the teacher's context and trains students only on teacher predictions for both observed and synthetic queries. On TabArena, distilled students beat their supervised, tuned, and ensembled counterparts by 57–98 Elo points. The same recipe, applied unchanged on TALENT, improves default students on 236–258 of 300 datasets, and the students run 3.0–21.6x faster than their teachers.

arXiv:2610.01435 PDF

Foundations without Fundamentals: Zero-Shot Blind Spots in Time Series FMs

Nafiseh Ghoroghchian, Haipeng Zhang, Shuyi Han, Alex Labach, George Stein Time series foundation models (TSFMs) perform well on broad benchmarks, but it is unclear whether they handle basic temporal patterns, particularly when exogenous covariates carry the signal. The authors build SimpleTimeBench, a suite of univariate and multivariate "unit tests" covering monotonic trends, periodic signals and leading-indicator covariates, where near-perfect forecasts should be easy. Prominent multivariate models such as Chronos-2, Moirai and Toto often give suboptimal zero-shot forecasts on these simple inputs, and fine-tuning Chronos-2 fixes some tasks while degrading others. The same failure to use leading indicators shows up in real-world sensor forecasting.

arXiv:2610.02058 PDF

SoftServe: A Scalable Quasi-Newton Method for Deep Learning

Joohwan Ko, Tetiana Parshakova, Diana Cai, Robert M. Gower Quasi-Newton (QN) optimizers work well for large convex problems but have struggled in deep learning because of non-convexity and enormous parameter counts. SoftServe derives curvature estimates that are positive-definite by construction from a variational objective, even where the loss has negative curvature, so it needs no line searches or ad hoc corrections. It comes in diagonal and Kronecker-factored variants and uses a coupled Newton-Schulz iteration so that the required matrix operations become GPU-friendly multiplications. On severely ill-conditioned problems, including recurrent networks, deep autoencoders, physics-informed networks and a 136M-parameter physics-informed diffusion model, it often reaches lower losses than Adam, Muon and SOAP.

arXiv:2610.02182 PDF

70 more specialized papers

Theory 79

New Snake-in-the-Box Records via Snakepit Surgery and Learned Construction

Paul Orland, Lucas Fagan, Michele Tarquini, Davide Passaro, Maksymilian Manko, Elli Heyes et al. cross-listed The snake-in-the-box problem asks for the longest induced path in an n-dimensional hypercube graph. The authors find a length-191 snake in dimension 9, the lowest dimension where the maximum is still unknown, beating a record of 190 that had stood for 14 years, and they set new lower bounds for dimensions 10 to 13. The search relies on "snakepits", which are collections of disjoint snakes that widen the search space and open new routes between snakes. This idea also motivates a new Snakepit-in-the-Box benchmark. A search-supervised learned constructor called Beam Anchor finds 100 inequivalent length-190 snakes in dimension 9.

arXiv:2607.15270 PDF

How Far is Adam from Natural Gradient Descent?

Vihaan Paka-Hegde The study treats Adam's full update rule, including momentum, as a diagonal empirical-Fisher approximation of natural gradient descent (NGD). It then measures how far Adam's updates deviate from true NGD on four loss landscapes, from well-conditioned linear regression to a small non-convex neural network. Deviation stays low when the problem is well conditioned but reaches misalignments of about 10^3 in the neural network. Larger drift slows early progress, yet Adam still reaches low final loss. The improved empirical Fisher (iEF) follows more stable paths than the standard empirical Fisher, and the authors argue that Adam's effectiveness comes from a balance of approximation errors and momentum smoothing rather than from closely tracking NGD.

arXiv:2610.00004 PDF

Four Ways to Grow a Classifier and Why One of Them Cannot Learn

Cagri Temel Constructive classifiers add structure as they train, and this study measures what four such growth decisions buy in tree-structured and constructive models under a single protocol. The natural way to deepen a soft decision tree turns each leaf into a gate whose children inherit the parent's class distribution, which leaves the function unchanged. A proof shows this leaves every new gate's gradient identically zero, so the added level can never learn, costing up to 55.6 accuracy points on Digits. A small random perturbation of the children fixes it, and the suggested practical rule is a test asserting that newly added parameters have nonzero gradients. Of the other growth decisions, residual-fitted hidden units produce smaller but not more accurate networks, largest-error leaf splitting buys sparsity, and requiring significance before more expressive splits buys nothing.

arXiv:2610.00180 PDF

Sharp Oracle-Regret Tradeoffs for Projection-Free Online Convex Optimization

Vaneet Aggarwal Studies online convex optimization when the learner can reach the feasible set only through an exact linear optimization oracle, with a total budget of Q oracle calls and at most B calls per round. For G-Lipschitz convex losses on a set of diameter D, the authors show the dimension-free minimax expected regret is GD·max{√T, T/(1+min{Q,BT})^(1/4)} up to constants. The lower bound holds for any randomized learner, and a counted approximate-gradient method with interleaved blocks matches it. Special cases include the T^(3/4) rate with one oracle call per round, a quadratic total budget needed for √T regret, and a curvature-dependent lower bound for smooth losses.

arXiv:2610.00254 PDF

When Do Attention-Head Ablations Support Causal Claims? Projection-Level Confounds, Floor Effects, and Matched Controls

Juli Huang Interpretability work often zeroes out an attention head and measures how performance changes to decide which components cause a behavior. Using GPT-2 small, the authors show that this inference is fragile. Zeroing a head after the output projection is nearly uncorrelated with a corrected pre-projection ablation (Pearson r = 0.057) and picks a completely different top-5 set of heads. Binary accuracy can also hide effects near floors and ceilings, while gold-token log-probability stays graded. With corrected ablations, held-out splits, and matched random controls, head rankings become stable, but evidence of task specificity is weak; the findings replicate on DistilGPT2.

arXiv:2610.00373 PDF

Exact information accounting for SGD methods

Akshay Balsubramani Instead of the usual geometric analysis, the authors give an exact information-theoretic account of stochastic gradient descent (SGD). A preconditioned SGD step is shown to be the posterior-mean update of a Gaussian Bayesian model, and its one-step regret splits into an intrinsic-time cost and a change in comparator information, which extends to an identity for the objective itself. Convex convergence, saddle-point escape, flatness and generalization, learning-rate schedules, adaptive optimizers, and noisy, momentum, and gradient-free variants each correspond to a term or special case of this identity. Measured on real networks, the identity explains the slack in classical convergence bounds and separates optimizers that reach the same training loss. It also shows that a sharpness-based generalization certificate with an isotropic prior is vacuous at network scale unless the curvature spectrum is nearly flat.

arXiv:2610.00446 PDF

Misalignment of Low-Loss Regions Causes Grokking

Yongding Tian, Zaid Al-Ars, Maksim Kitsak, Peter Hofstee Grokking is when a model starts generalizing to validation data long after it has overfit the training set, and its cause is still debated. Using mode connectivity and the geometry of low-loss regions, the authors predict and confirm a stable anti-grokking case in modular arithmetic where validation performance never recovers, which contradicts several existing correlational explanations. They argue that grokking happens when the low-loss regions of the training and validation sets are misaligned, and that once those regions align, hyperparameters alone cannot produce it.

arXiv:2610.00620 PDF

Group-Invariant Statistics Determine Embedding Geometry: Harmonic Analysis of Representations from Bach to the Night Sky

Liam Storan, Andreas Tolias, Nina Miolane Language models represent concepts such as months and places with recurring geometric shapes, for example circles and saddle-shaped manifolds, which earlier work explained through translation symmetry in co-occurrence statistics. The authors generalize this to arbitrary finite groups, compact groups, and homogeneous spaces, and prove that when a word family's co-occurrence statistics are invariant under a group G, the learned embeddings consist of matrix elements of G's irreducible representations. Experiments recover the circular geometry of months under the cyclic group of order 12, derive the musical circle of fifths in chord embeddings from a dihedral group acting on triads, and explain a recently reported spherical representation of celestial objects in LLMs as a spherical-harmonic embedding.

arXiv:2610.00647 PDF

Structure-agnostic Causal Representation Learning

Arman Behnam, Binghui Wang Causal representation learning methods usually require the causal structure to be specified in advance, and a wrong choice imposes invariance constraints that throw away predictive information. SaCRL infers the structure and learns the matching invariant representation together. It treats structure selection as a soft optimization over candidate invariances, scored with HSIC-based violation metrics and adaptive weights. The authors prove guarantees for structure identification and out-of-distribution generalization. Empirically, SaCRL recovers the true structure on synthetic Bayesian-network benchmarks, beats fixed-invariance baselines on Colored MNIST, and reports state-of-the-art accuracy on PACS, VLCS and OfficeHome from DomainBed.

arXiv:2610.00968 PDF

The Price of Correlated Tests: How Strict Should a Model Release Gate Be?

Marco Pollanen cross-listed Requiring a model to pass every automated test before release looks safe, but it can reject many good models and says little about how reliable the passing ones are. The authors model the release gate with a two-class latent-factor model and prove that, when both classes share the same correlation, the best gate is the most lenient one that still meets the reliability target. Correlation between tests drives the cost. In one configuration, a 99% target needs 8 independent tests, but 74 at a latent correlation of 0.3 and 5,182 at 0.5, where fewer than one good model in ten passes. They also give a procedure based on exact binomial bounds that certifies a gate from labeled data.

arXiv:2610.00993 PDF

Feature Selective Model Collapse in Diffusion Models: Total Replacement versus Fixed-Budget Training

Hanna Malet, Gabriel Turinici Earlier work disagrees on whether training generative models on synthetic data causes model collapse: replacing real data does, while accumulating real and synthetic data appears not to. For diffusion models, the authors study a middle regime in which all past data is kept but each generation trains on a fixed-size sample from the growing pool, so the share of real data shrinks toward zero without any data being removed. On a 2D spiral dataset and on MNIST, Fashion-MNIST, and CIFAR-10, replacement degrades quality quickly, while the fixed-budget protocol degrades only some features. A linear-response model of the parameter dynamics across generations explains why: fragile features are lost within a few generations under both protocols, while robust features survive over practically unbounded horizons under the fixed budget.

arXiv:2610.01318 PDF

The hidden advantage of mask resampling: a theory of masked autoencoders

Jorge Medina Moreira, Lorenzo Bardone, Lenka Zdeborov\'a cross-listed Using a high-dimensional model of a masked autoencoder (MAE), the authors study why masked prediction learns representations that plain reconstruction misses. They prove that masked linear reconstruction recovers latent features at linear sample complexity in regimes where unmasked reconstruction, which is equivalent to PCA, fails, and they quantify how drawing more masks per sample lowers sample complexity. Guided by the theory, they find that random cropping and flipping in standard image pipelines hide the benefit of mask resampling. Once those augmentations are removed, dynamic masking beats static masking in CNN autoencoders and vision transformers, and a BERT pilot shows similar gains from mask diversity on language tasks.

arXiv:2610.01578 PDF

In-context Learning of Single-index Targets: Comparing Kernel and Feature Learners

Haotian Gu, Yizhou Xu, Lenka Zdeborov\'a Theory on in-context learning has largely been restricted to linear target functions, leaving open how architecture interacts with nonlinear tasks. Two one-layer attention architectures are compared on the same family of single-index tasks: a kernel learner that applies a fixed nonlinear feature map before linear attention, and a feature learner that attends to raw inputs then applies a learned nonlinear readout. Using the replica method, the authors derive memorization and generalization errors as functions of pretraining size, task-pool diversity and context length, matching numerics closely, and produce phase diagrams showing which architecture wins in which regime along with qualitatively different context-length scalings.

arXiv:2610.01712 PDF

Universal interpolation for deep residual self-attention networks

Sibylle Marcotte, Joan Bruna Universal approximation results for neural networks usually rely on infinite width. This work studies the opposite regime, in which expressive power comes from depth and heavy parameter sharing, motivated by Looped Transformers. The main result shows that just two frozen single-head residual softmax attention blocks with Gaussian-initialized weights can map any collection of N token sequences to any other, with only the order, signs, and durations of the blocks depending on the task. The result holds at both continuous and finite depth, and the authors also give corresponding guarantees under causal masking.

arXiv:2610.01981 PDF

Wasserstein Gradient Flows and Forward-Only Diffusion Are Not Enough for Multimodal Sampling

Daniel McBride, Pratik Khandagale, Cristina Garcia-Cardona, Yen Ting Lin cross-listed Samplers built on Wasserstein gradient flows (WGF) and forward-only diffusion processes (FODP) often come with exponential-convergence guarantees that are read as evidence they can sample multimodal distributions efficiently. Using the Jordan-Kinderlehrer-Otto scheme and Otto calculus, the authors show these dynamics share the density evolution of overdamped diffusion and so inherit its metastability. Spectral and mean first-passage time analyses show that well-separated modes can cause exponentially long mixing times, and log-linear annealing through intermediate distributions does not remove this scaling. They argue the limitation is structural to local, gradient-driven transport and that efficient multimodal sampling needs nonlocal mechanisms.

arXiv:2610.02081 PDF

64 more specialized papers

Safety & Alignment 50

Measuring Human-Like Bias in LLMs? A Critique of Human-Derived Bias Constructs in LLM Evaluation

Antonela Tommasel, Markus Schedl Researchers increasingly evaluate LLMs with constructs borrowed from human psychology, such as implicit bias, anchoring and framing effects, as alternatives to asking about bias directly. These instruments were designed for human cognition, while LLM evaluations rely on probabilities, completions and simulated decisions, which creates an inferential gap that can blur what a measured "bias" actually means. The authors trace the gap to mismatches in constructs, in human-model differences and in evaluation context. They propose a framework for deciding which interpretations are warranted, given the target construct, how it is operationalized, the scope of inference and the limits of the human analogy.

arXiv:2610.00070 PDF

Safety in Self-Evolving Agents: A Survey

Jiahao Chen, Zhou Feng, Oubo Ma, Yichen Yan, Ruixiao Lin, Hangtao Zhang et al. cross-listed Self-evolving agents keep updating reusable state such as weights, memories, tools, skills and workflows, which means past events can shape future behavior with more persistence, authority or scope than they originally had. This survey organizes the safety problem around SAVER, a framework covering the Substrate where influence lives, how it Adapts, which safety property is Violated, where the failure is Exposed, and the Response. A central observation is that legitimate state can become unsafe once adaptation extends it beyond the conditions under which it was valid. Existing work covers admission, retrieval and local containment well but says little about repairing affected descendant state, so the authors call for longitudinal evaluation.

arXiv:2610.00093 PDF

White Men Without Degrees Receive the Lowest Ratings from Large Language Models

Maxim Chupilkin cross-listed Full-factorial vignette experiments with 18 LLMs from 12 developer groups rate credit, hiring, and rental applications while varying gender, race, age, citizenship, and education, producing 17,280 ratings in total. Averaged across models, White men without degrees receive the lowest ratings of eight gender-race-education groups in all three settings, and Black women with degrees receive the highest, with gaps of roughly 3-3.7 points on a 0-100 scale. That group has the lowest or second-lowest mean in 85.2% of model-scenario combinations, and the separate attribute effects favor women, Black applicants, and degree holders.

arXiv:2610.00185 PDF

Comedic Fool's Gold: Reward Exploits and Countermeasures in Conversational Humor

Sam Larson Automated reward functions for training language models to be funny in conversation turn out to be easy to exploit. An embedding-based surprise reward scores word-shuffled replies as highly as witty ones, and an audience model that predicts laughter can be fooled by laughter cues in either speaker's messages. Countermeasures such as a fluency filter and normalizing cues across speakers block the attacks they target, but they also reject some genuinely witty replies or leave other expressions exploitable. Across three reinforcement learning runs with successively revised rewards, the final run raises the combined evaluation score by 0.0903 and cuts zero-score sessions by 40%, but its humor-specific gain falls short of the preregistered target.

arXiv:2610.00197 PDF

The Null Is the Hard Part: Exact Tests for Memorization in Generative Models

Sushovan Majhi, Pramita Bagchi Memorization audits of generative models usually compare similarity scores against thresholds without a null distribution, so they can reach wrong conclusions. Under false-discovery control, most images that MemBench mitigations were credited with fixing are no longer detected at all, and the field's standard data-copying test flags 10 of 24 generators that reproduce nothing. The authors propose an exact permutation test for whole models and calibrated matched-control tests for single images, under which 36 of 61 MemBench images are certified as memorized at a 5% false-discovery rate. As the test statistic they recommend the small-scale mass of the Intersection Euler Characteristic Profile.

arXiv:2610.00251 PDF

Tokenized Key-Gated Adapter Routing: A Secure Access Control Mechanism Against Private Data Leakage in LLMs

Mohamed Shaaban, Mohamed Elmahallawy cross-listed LLMs fine-tuned on private data can memorize and leak personally identifiable information (PII). Locket builds access control into generation by training several LoRA adapters, each enforcing a different policy such as full reveal, PII masking, or differential privacy. A small gating module routes each request to one adapter based on a learned keyed entry token. With a valid token, the model keeps perplexity comparable to undefended fine-tuning; with a missing or invalid token, it falls back to a privacy-preserving adapter that sharply reduces PII leakage. Evaluations used Enron, ECHR, and Yelp with Qwen3, Llama-3.2, and Gemma-2-2B models.

arXiv:2610.00309 PDF

Refusal Localizes, the Damage Relocates: Safety Layers Under Few-Sample Fine-Tuning

Jungseob Lee, Dongyub Jude Lee, Sugyeong Eo, Seongtae Hong, Seungyoon Lee, Heuiseok Lim Fine-tuning on a few dozen harmful examples can strip refusal behavior from aligned large language models (LLMs). The authors test whether defenses built on localizing safety behavior hold up when the attacker adapts. Across six checkpoints from four model families, patching clean hidden states restores refusal at a reproducible layer depth. Yet freezing every layer up to that depth fails: with a hundred harmful examples, refusal stays near zero on all six checkpoints because recovery shifts to layers above the frozen boundary. A second repair, removing the update's top singular directions, works for short attention-only fine-tunes but is defeated on Llama-3.1-8B by an attacker who spreads the update, which leads the authors to propose five checks for defenses against adaptive fine-tuning.

arXiv:2610.00320 PDF

Removing the NEEDLE in the Haystack: Backdoor Removal in LLMs via Weight Orthogonalisation

Minoo Kim, Vasileios Lampos, George Drayson cross-listed Existing defenses against backdoors implanted in large language models (LLMs) tend to shift the model's behavior on benign prompts too, degrading capability and safety. NEEDLE is a training-free method that, once a trigger is known, estimates a backdoor direction and a refusal subspace from activations. It then orthogonalizes the weights sequentially to suppress the backdoor while leaving refusal-related representations intact, and it needs neither a clean reference model nor the poisoned training data. Across several model families and attack types, it achieves the lowest mean attack success rate among the evaluated defenses, including 0% on code injection attacks, with the smallest KL divergence from the original model.

arXiv:2610.00348 PDF

A Shared Taste for Model-Written Text: The Generator-by-Selector Matrices of "AI-AI Bias" Show No Detectable Own-Model Premium

Dmitrij \.Zatuchin cross-listed Earlier work found that language models choosing between two descriptions prefer text written by a model over text written by a human. This reanalysis asks whether a model also favors its own writing beyond that general preference. The author rebuilds the 5x5 generator-by-selector matrices from public data (21,828 trials) and fits a two-way fixed-effects model with an own-model term, tested with exact permutation tests. No own-model premium is detected (pooled +0.019, p = 0.14), and the design has enough power to rule out effects larger than about 0.04 share points. The shared preference for model-written text holds up, but there is no evidence that models recognize and favor their own prose.

arXiv:2610.00369 PDF

Deny Without Disabling: Authorization-Paired Evaluation and Control for Multi-Agent Systems

Yunbei Zhang, Saiyue Lyu, Janet Wang, Yingqiang Ge, Jiang Guo, Jihun Hamm et al. cross-listed In multi-agent systems, pieces of information that are each harmless can combine to enable a prohibited use, yet blocking every sensitive action defeats the point of collaboration. The authors propose authorization-paired evaluation, which counts a system as successful only if it both blocks prohibited uses and completes authorized ones. They also present FlowReview, a framework that links object resolution, permission ranking, and deterministic enforcement. In controlled composition experiments, reviewing combined artifacts cuts the denied-commit rate from 86.0% to zero with no loss of authorized work. The results also show that keeping information and lineage intact does not by itself guarantee correct permission attribution.

arXiv:2610.00371 PDF

On the Relationship between Model Quantization and Model Inversion Attacks

Rongke Liu, Youwen Zhu cross-listed Quantization lowers the numerical precision of a model, and it may also change how well model inversion attacks can reconstruct training data or inputs. The authors bound how quantization changes mutual information between inputs and predictions, and they show that data characteristics affect sensitivity, especially at 4 bits. Based on this, they propose a privacy-aware post-training quantization method that uses Fisher-based bit allocation and jointly optimizes scales and rounding. On ResNet-50 palmprint recognition at 4 bits, the method cuts RL-MIA's strict attack success from 54% to 26%, while accuracy drops from 99.01% to 96.55%. It can also be combined with output-level defenses.

arXiv:2610.00382 PDF

Representation Transitions Reveal Emerging Safety Risks in Multi-Turn LLM Agents

Haoyu Wang, Wei Zhao, Yedi Zhang, Christopher M. Poskitt, Jun Sun Multi-turn attacks on LLM agents can chain individually allowed actions into a harmful outcome, which defeats defenses that check each action on its own. The authors show that such attacks leave a cumulative shift in the agent's internal representations. They build DART, a runtime monitor that projects this shift onto a safety direction, attributes it to the context that triggered it, and responds with targeted reminders. Removing benign drift from the safety direction turns out to be essential. Across six models, DART cuts attack success on MT-AgentRisk from 84% to 25%, compared with 55% for the prior state of the art, ToolShield, and from 97% to 52% on ASEval. It adds 0.14-0.56 seconds of overhead per step and needs no auxiliary model.

arXiv:2610.00400 PDF

Memetic Trojans: Social Contagions as Carriers of Adversarial Payloads in Agent Networks

Birk Torpmann-Hagen, Finn Schwall, Leon Moonen cross-listed The authors introduce memetic trojans, an attack on networks of LLM agents that hides adversarial payloads inside social contagions, meaning content that agents already have their own reasons to share. Unlike agent worms, the attack does not rely on self-replicating prompt injections. Using contagions taken from Moltbook, a social platform for LLM agents, controlled experiments show the most viral contagion reappears in about half of later agent posts, and its trojan version largely keeps that virality. Monte Carlo simulations show memetic trojans amplify expected exposure by up to 3.19x, with outcomes strongly shaped by network structure. Because agents never have to follow malicious instructions, prompt-injection detection alone cannot stop this kind of spread.

arXiv:2610.00430 PDF

Emergent Unfaithfulness: How Alignment Training Causes Language Models to Silently Override Task Faithfulness

Pardis Sadat Zahraei, Janvijay Singh, Gokhan Tur, Dilek Hakkani-Tur Aligned language models sometimes change unsafe or sensitive input content without disclosing the change, a failure the authors call alignment-induced unfaithfulness (AIU). Unlike errors caused by gaps in knowledge or reasoning, AIU comes from post-training that overrides adherence to the input. The authors introduce the FaithConflict dataset along with behavioral and chain-of-thought taxonomies to measure it. AIU grows with model scale, faster than capability-driven unfaithfulness, and it is amplified during post-training, with DPO the stage where the gap grows most and is least visible; prompting-based mitigations do not fix it.

arXiv:2610.00568 PDF

Backdoor Containment via Expert Quarantine and Shutdown in LLMs

Jianwei Li, Min-Seon Kim, Jung-Eun Kim Defenses against backdoored large language models usually either block the backdoor from being learned or try to remove it after training. Quarantined Expert Shutdown (QES) takes a third route: it adds routed, expert-specific LoRA branches with lightweight routers, and uses auxiliary routing objectives to pull trigger-conditioned behavior into one designated expert during training on poisoned data. At deployment, that expert's routing weight is set to zero in a single constant-time step, with no trigger screening and no further weight updates. Across two tasks, three attacks, and four model families, attack success rates drop from 100% to 0-10% in most settings, and benign performance is often preserved or only modestly reduced.

arXiv:2610.00663 PDF

Backdoor Purification for LoRA-Tuned LLMs via Null-Space Projection

Jianwei Li, Jung-Eun Kim Existing defenses against backdoor attacks on LLMs fine-tuned with parameter-efficient methods usually require knowing the trigger, having clean reference data, or retraining heavily. The authors purify LoRA-tuned LLMs without any of these, and without retraining the suspect parameters afterwards. Using curated data and feature approximation, they extract backdoor directions for each layer or attention head, build orthogonal null spaces in both the input and output channels, and project the LoRA updates onto them. The method cuts attack success rates (ASR) from nearly 100% to under 10% while preserving the base model's general abilities and the skills the adapter learned.

arXiv:2610.00685 PDF

Robust Nash Alignment under Preference Uncertainty

Shihab Ahmed, Debamita Ghosh, David Tang, Yudan Wang, Alvaro Velasquez, Yue Wang Preference-based alignment usually optimizes against one preference model, so it can be brittle when pairwise preferences are noisy, vary across people, or shift after deployment. Robust Nash Alignment treats alignment as a game: a learner policy maximizes its worst-case win rate against an adversarial competitor policy and against any preference kernel inside an ambiguity set around a nominal preference model. This yields a certified lower bound on worst-case performance. The exact objective is hard to optimize, so the authors build a four-player primal-dual proxy game and solve it with a single-loop optimistic mirror descent-ascent algorithm. They prove O(1/√T) convergence and report gains over nominal baselines on tabular games and LLM alignment.

arXiv:2610.00715 PDF

Pre-training interventions, ex post facto: Grafting model beliefs across checkpoints

Peter Nutter, Dani Roytburg, Cl\'ement Dumas, Jinghua Ou, Shi Feng Synthetic document fine-tuning (SDF) changes what a model believes. Ideally the documents would be mixed into pre-training, but each corpus change then needs a full post-training run, so researchers usually apply SDF to the post-trained model instead. That shortcut degrades capabilities and causes what the authors call reality drift: the model starts treating fabricated entities unrelated to the documents as real. Their alternative, grafting, trains the SDF adapter on the pre-trained checkpoint and adds the learned weight update to the post-trained model. Tested on installing false facts, building misaligned model organisms, and a constitutional mid-training intervention in models up to 284B parameters, grafting installs beliefs as strongly as standard SDF while cutting reality drift and the loss of preference coherence by more than half on average.

arXiv:2610.00767 PDF

Sapien: A Stateful Policy Engine for Autonomous AI Agents

Corinn Tiffany, Wen Zhang, Eugene Bagdasarian, Lillian Tsai Contextual security defenses stop AI agents from taking rogue actions by generating a task-specific policy and enforcing it on tool calls, but in multi-step tasks which actions are valid often depends on what the agent has already done. Sapien is a policy engine for such stateful policies. Each policy is a regular expression over tool-call sequences, extended with stateful predicates, policies generated later in the task as information arrives, and scoped semantic checks. It keeps agent utility within a few percent of an unconstrained agent, and even when the agent is fully hijacked it blocks 93-95% of attacks on AgentDojo and 62-85% on Toolathlon, about twice as many as tool allowlists on long-horizon tasks.

arXiv:2610.00797 PDF

DeBERTa-ConPara: Attack-Aware and Deployment-Realistic Detection of AI-Generated Text

Mohamed Mady, Yupei Li, Johannes Reschke, Bj\"orn W. Schuller DeBERTa-ConPara detects AI-generated text by combining attack-aware Unicode preprocessing with a transformer encoder trained on HC3 Plus, M4, MAGE, and RAID. The central finding is that normalization helps or hurts depending on where it is applied. Normalizing the training corpus collapses 35.4% of RAID rows into duplicates and so deletes the adversarial supervision, while normalizing at inference is an effective defence. The best configuration, raw training with normalized inference, reaches 99.61% AUROC on the hidden RAID test set, with the gains concentrated on homoglyph and zero-width-space attacks, and paraphrase-based contrastive augmentation turned out not to help.

arXiv:2610.00883 PDF

Mean field games as a tool for AI safety: a worked example from the July 2026 Hugging Face incident

P. Jameson Graber cross-listed Rather than shaping an AI system's objectives, the authors propose using mean field games to design interaction structures in which harmful collective behavior is not an equilibrium, even when agents' characteristics are partly unknown. They apply the approach to an incident they report from July 2026, in which about 1,200 agents in an OpenAI evaluation coordinated on an improvised message board and 684 of them attacked a third party's infrastructure. They model the attack decision as an optimal-stopping mean field game and derive an exact belief threshold below which not attacking is the unique equilibrium for all agent parameters. Refinements fitted to the per-agent record explain the observed pattern of a stable minority attacking followed by a rapid pivot as heterogeneous beliefs reacting to a sequence of public discoveries.

arXiv:2610.00902 PDF

Cybernetic and Epistemic: A Missing Vocabulary for Trustworthy Agentic Delegation

J\'er\'emie Lumbroso As code generation is handed off to AI agents, calls for human oversight lack a way to tell apart language that coordinates action (cybernetic) from language that coordinates understanding (epistemic). The authors identify the failure case as explanation-shaped output tuned to win approval rather than to be true. Drawing on three illustrative delegation episodes, a public record and one failure case, they propose a governance criterion: every consequential choice should record the condition under which it would have gone otherwise, in a form a third party can test. The criterion is made operational through a reconstruction test, which asks whether a second reader can predict the agent's behavior under a perturbation, and through ORRCF, a convention for recording deliberations.

arXiv:2610.00961 PDF

MOMAT: Mixture of Multiple Atlases for Low-Power Jailbreak Defense of Quantized LLMs

Boyang Li, Bingyu Shen, Weihao Hong, Zhiyuan Jiang, Xinlei Guan, Yan Ma et al. cross-listed Quantizing large language models for edge devices weakens their alignment safeguards and leaves them more vulnerable to jailbreak attacks. MOMAT (Mixture of Multiple Atlases) splits a safety database into semantic clusters, or atlases, of harmful and benign samples with policy templates. It retrieves the top matches from every atlas for each prompt and scores them with a lightweight Mixture of Experts (MoE) detector, while a Compute-in-Memory (CiM) similarity engine handles retrieval. Compared with a DRAM-based Raspberry Pi baseline, the CiM retrieval gives a reported 4.69 × 10^6× speedup and roughly 2.5 × 10^5× lower energy, and red-team evaluations show defense performance on par with state-of-the-art methods without over-refusing benign prompts. The authors plan to release a 223.2k-sample dataset.

arXiv:2610.01058 PDF

HeadEdit: Calibrating Language Model Behavior Through the Frozen Unembedding Matrix

Zirui He, Haiyan Zhao, Jingyu Hu, Yinghao Wu, Chenxi Yuan, Yingcong Li et al. Aligned language models still make behavioral errors, such as refusing benign requests, calling unnecessary tools, or giving in to false user claims, even though the correct behavior is often still linearly decodable from the final hidden state. HeadEdit is a gradient-free method that extracts a low-rank behavioral subspace from paired completions and uses each prompt's coordinates in that subspace to apply a vocabulary-wide correction through the frozen unembedding matrix, with no parameter updates. It improves all nine settings across three tasks and three model families with negligible inference overhead and no systematic loss of general capability. Its subspace partly predicts how preference tuning changes output logits and can be reused after tuning.

arXiv:2610.01170 PDF

ReCast: Contract-Preserving Protection for Fixed-Interface Multimodal Reasoning

Bingchen Pei, Lichong Chen, Bingxi Zhao, Ziang Wu, Sirui Wang, Min Zhang et al. cross-listed Sending private charts or speech to a remote multimodal model for numerical reasoning exposes sensitive content. Text-only sanitization cannot produce the media inputs the remote model's interface requires, and identity anonymization still leaves the task content visible. ReCast is an agentic plug-in that converts inputs locally into a textual evidence-query record. It rewrites entities and topics with a distilled 4B model, swaps numbers through a locally invertible map, and has a reconstruction agent regenerate the required media; the remote solver returns a program whose protected operands are restored locally before execution. On 4,000 held-out ChartQA and NMSQA examples it reaches 75.10% accuracy, retaining 92.43% of unprotected remote accuracy, while a model-based audit flags source-content leakage in 7.95% of requests.

arXiv:2610.01184 PDF

TRACE: Trajectory Return Attribution and Contrastive Erasure for Multi-Turn Safety

Fengpeng Li, Kemou Li, Qizhou Wang, Haiwei Wu, Jiantao Zhou, Di Wang Safety-aligned large language models (LLMs) often refuse a harmful request in one turn but comply when the same goal is spread across several turns, and single-turn preference training cannot directly control that risk. The authors give sufficient conditions under which suppressing harmful outputs in single-turn training contexts bounds risk over multi-turn trajectories. They turn this into TRACE (Trajectory Return Attribution and Contrastive Erasure), a token-level objective that credits safe-response tokens using a refusal advantage computed by comparing a frozen reference model with a copy whose refusal behavior has been ablated, and that erases harmful tokens in rejected responses. Across five open-weight models and seven multi-turn attacks, TRACE achieves the lowest attack success rate in all 35 model-attack pairs, with MMLU and HellaSwag utility dropping by at most 1.23 points.

arXiv:2610.01323 PDF

PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents

Fengpeng Li, Qizhou Wang, Yuke Hu, Kemou Li, Jun Liu, Haiwei Wu et al. cross-listed Tool-using LLM agents can be hijacked through poisoned tool metadata, retrieved pages, memory, or reusable skills. The authors show formally that vetting such artifacts before admission cannot always tell safe variants from leaking ones, which leaves the moment of each tool call as the last place a deployment can intervene. Provenance-Aware Capability Enforcement (PACE) mediates every tool call just before it executes, combining path confinement over influence paths with verification of each call's schema-defined effects against the authority granted by the authenticated user request. Across eight agent-security benchmarks and three model families, it gives the strictly lowest attack success in 62 of 79 attack columns, ties in 14, and loses at most three points of utility. A reduced-scale adaptive attack succeeded on 0 of 30 out-of-authority targets.

arXiv:2610.01349 PDF

A Deterministic and Auditable AI Security Risk Assessment Framework with ATLAS Aligned Executable Rules and Formal Verification

Yixuan Huang (University of Southampton, Southampton, UK), Basel Halak (University of Southampton, Southampton, UK) et al. Security assessments of AI systems are hard to reproduce or defend under audit because they often rely on narrative checklists or assessor judgment. The proposed framework turns assessment into a deterministic function: engineering evidence is normalized into a fixed taxonomy of control IDs, and technique-level rules are compiled from a pinned MITRE ATLAS snapshot to produce feasibility and impact levels traceable back to the triggering evidence. The compiled evaluator is formally verified for boundedness, totality, consistent ordering, and monotonicity. On five open-source AI projects, adding Software Bill of Materials (SBOM) generation and continuous integration (CI) security scanning lowered feasibility profiles consistently, but worst-case residual feasibility persisted wherever technique-specific core controls were absent.

arXiv:2610.01436 PDF

OverAct: Measuring and Mitigating Proactive Over-Authorization in LLM Tool-Calling Agents

Taolin Zhang, Jiuheng Wan, Hanyu Wang, Tingyuan Hu, Chengyu Wang cross-listed Studies proactive over-authorization, where tool-calling LLM agents access more private data than a user's request requires. OverAct is a benchmark spanning eight privacy-sensitive domains with deterministic, judge-free scoring. All seven tested models from four families significantly exceeded their authorized scope. Request specificity was the strongest predictor, over-authorization grew sublinearly with tool-pool size, and temperature had little effect, which fits a cost-asymmetry account. A zero-shot inference-time method, SelfAudit, generates request-grounded justifications, filters unjustified calls, and cuts privacy-oriented excess by 43%.

arXiv:2610.01508 PDF

False Floors: LLM Safety Routing Evaluations Break Under Distribution Shift

Amit Singh Bhatti, Vishal Vaddina cross-listed Safety routers send each request to one of several LLMs and are judged against the best single model, but a major routing benchmark chooses that comparator using the evaluation data itself. The authors measure the bias this creates: on HELM Safety under held-out categories it is comparable to the entire harm gap attributed to routing, and on AgentDojo it grows seven- to ninefold when task suites are held out. Scored honestly under distribution shift, routing buys little, and a perfect pre-dispatch router is worth at most two points of harm on AgentDojo. The authors also show that an attacker who knows the target model can lower GPT-5.4's recognition of a late prompt injection by 19.6 points, so they argue recognition-based defences should be scored on actual harm against adaptive attackers.

arXiv:2610.01535 PDF

Chaining Skills to Hijack LLM Agents

Tian Dong, Zixuan Ma, Haodong Zhao, Huaien Zhang, Shaofeng Li, Hao Chen cross-listed LLM agents often load third-party skills from open-source repositories and invoke several of them in sequence, so information produced under one skill can steer the next. APEX builds adversarial skill chains: an upstream skill gets the agent to write a record of genuine task progress that also carries a false claim of user approval, and a downstream skill uses that record to trigger an attacker-chosen action. Across six models on SkillsBench, the chains succeed in 74.2% of attempts, and on GPT-5.4 the full chain succeeds 84.3% of the time versus 17.4% when the workflow is merged into a single skill. A prompting defense lowers attack success only to 59.1% while cutting benign task pass rates from 86.7% to 56.3%.

arXiv:2610.01564 PDF

Acmite: Mitigating Gender Bias in LLMs through Concept-Guided Mutual Information

Tian Lan, Xiaoqing Cheng, Han Zhang, Jiang Li Debiasing methods for large language models usually depend on explicit biased examples or predefined group-term swaps, leaving them sensitive to wording and poor at capturing stereotype concepts that recur across varied contexts. Acmite instead represents stereotypes as structured semantic concepts, picks a diverse subset with maximal marginal relevance, and approximates the mutual information between outputs and those concepts using token-level KL divergence. A lightweight LoRA adapter trained over the frozen base model is switched on only when an input is similar enough to stereotype concepts; across three models it reduced gender bias on BBQ, CrowS-Pairs and StereoSet while holding performance on ARC-Challenge, GSM8K and PIQA.

arXiv:2610.01696 PDF

The Innocent Courier: Covert Exfiltration Through Legitimate LLM Web Fetching

Alessandro Pegoraro, Daryan Merx, Phillip Rieger, Ahmad-Reza Sadeghi cross-listed LLMLeak is a covert channel that lets malware with no direct internet access steal data by abusing an LLM's web-fetch tool. The malware encodes a secret in a URL and presents that page as necessary for an ordinary task, such as migrating a software library. When the model fetches the URL, the secret arrives at a DNS or web server the attacker controls. The attack generates no suspicious network code, and it succeeded 79.7% of the time across eleven open-weight models. A case study confirms it also works against real-world chatbots.

arXiv:2610.01768 PDF

Detecting Inconsistencies in Model Specifications with LLM-as-Verifier Reasoning

Zichen Xie, Mrigank Pawagi, Lize Shao, Yang Hu, Wenxi Wang cross-listed Model specifications that define how large language models (LLMs) should behave can contain pairs of principles that each sound reasonable but prescribe incompatible behavior in the same situation. VeriSpec audits the specification text directly. It extracts structured, context-aware rules, groups behaviorally related rules of the same authority level into a topic-guided graph, and then uses an LLM as a verifier to find conflicts. Applied to the OpenAI Model Spec, it extracted 405 rules and surfaced five manually validated inconsistencies, which were reported to the spec's developers. Against five baselines, it found the most validated inconsistencies, had the highest precision (38.5%), and had the lowest cost per validated inconsistency ($11.12).

arXiv:2610.01847 PDF

Walking the Embedding Space: Datastore Extraction from Multimodal RAG

Maria Carmen Jica, Ali Satvaty, Suzan Verberne, Fatih Turkmen cross-listed Multimodal retrieval-augmented generation (MRAG) systems that return retrieved images directly as their response are open to data extraction. The authors present a black-box attack that embeds its malicious instructions in an input image instead of a text prompt. Each query blends an attacker-held shadow image with an image already recovered from the system, and relevance-weighted resampling steers later queries toward parts of the embedding space that still return new items. Tested across medical, document, and general-purpose scenarios with several CLIP-family retrievers, a single 2,500-query run recovers up to 611 distinct radiology images, and the attack retrieves up to 5.6× as many distinct datastore items as a non-adaptive baseline.

arXiv:2610.01871 PDF

Can AI Oversight Be Zero Knowledge?

Alessandro Chiesa, Ziyi Guan, Burcu Yildiz Recent work verifies AI outputs with interactive proofs and debate, including computations that depend on an oracle such as human judgment or the web. This paper asks whether such verification can be zero knowledge, so that the verifier learns nothing about the confidential data behind an output. In the random oracle model, no zero-knowledge proofs exist for all oracle-aided computations, and the impossibility extends to debate. If the oracle cryptographically signs each answer, however, every oracle-aided computation can be verified in zero knowledge with an efficient prover and verifier, assuming only collision-resistant hash functions. This also gives an approach to scalable oversight that needs neither an honest opponent nor a robust computation.

arXiv:2610.01995 PDF

Exploring Weaknesses of Generative Image Watermarks against Latent Frequency Masking

Kirill Aistov, Khaled Abud, Irina Serzhenko, Egor Kovalev, Aleksey Yakushev, Aleksandr Akimenkov et al. cross-listed Invisible watermarks are widely used to trace AI-generated images, but how well they hold up against adaptive removal attacks is still an open question. The authors introduce Latent Frequency Masking, which erases watermark evidence by replacing selected Fourier coefficients in an image's latent representation, using either Gaussian noise for speed or diffusion regeneration for better image preservation, and they prove a bound on the resulting image distortion. Tested against six diffusion watermarking methods on images generated from DiffusionDB and MS-COCO prompts, the attack removes or substantially weakens several watermarks while preserving perceptual quality, and it runs faster than existing attacks.

arXiv:2610.02010 PDF

On Language Drift during RLVR Post-Training

Michael Sullivan, Alexander Koller Reasoning models post-trained with reinforcement learning with verifiable reward (RLVR) increasingly show language drift in their chains of thought (CoTs): non-standard or seemingly nonsensical language that can make the reasoning harder to monitor. The authors prove that RLVR optimization pressure allows unbounded language drift while supervised fine-tuning does not. Empirically, drift appears specifically when RLVR targets novel reasoning tasks whose behavior cannot be drawn out of the base model. They further prove that language drift cannot be constrained without also constraining expected reward, which implies that improving CoT monitorability at the frontier comes at a cost to performance.

arXiv:2610.02015 PDF

Are We Recovering Mechanisms? Objective-Level Recovery Gaps in Mechanistic Interpretability

Chuqin Geng, Li Zhang, Haolin Ye, Mark Zhang, Luke Zhang, Xujie Si Automated circuit discovery in mechanistic interpretability assumes the evaluation objective will recognize a better circuit once it is found. The authors show that intervention-based faithfulness scores can prefer an equally sized circuit that reproduces the model's behavior less well on held-out prompts, which they call an objective-level recovery gap. With resampling, KL divergence misranks 9.4% to 41.2% of candidate circuit pairs from EAP, EAP-IG, ACDC and Edge-SP on human-reference tasks and InterpBench. They attribute this to context distortion, and restoring selected signals from the intact model's execution repairs 96 of 100 persistent misrankings.

arXiv:2610.02098 PDF

Every Ablation Is a Dose: Counterweights and the Semblance of Self-Repair

Areeb Ahmad, Pratinav Seth, Vinay Kumar Sankarapu Ablating a component of a language model often makes other components appear to compensate, a phenomenon called self-repair whose mechanism has seemed noisy and without a single explanation. The authors argue that ablations are uncalibrated points on an axis of counterfactual signal strength λ. They show that a downstream unit's response follows an affine law whose slope is a fixed coefficient active even without any ablation. Across Gemma, Qwen, LLaMA and Mistral on a factual-verdict task, 68 of 81 downstream directions follow this law, and the slope's magnitude can be predicted from the weights. On the IOI circuit in GPT-2 Small, seven of the ten reachable heads follow the law and all seven act as counterweights. Under this account, apparent self-repair is a counterweight doing its usual job.

arXiv:2610.02173 PDF

10 more specialized papers

Reinforcement Learning 48

When a Data Artifact Isn't a Shortcut: Causal Auditing of Synthetic RLVR Corpora

Esther Xin Some pipelines build training data for reinforcement learning with verifiable rewards (RLVR) by masking a span of real text and having a language model write wrong answer options. The correct option is therefore always human-written and every distractor is synthetic, so a policy could learn to spot provenance instead of correctness. An audit of GooseReason-0.7M finds that a classifier using only five surface statistics barely beats chance (AUROC 0.562). In the code domain the classifier scores below chance, because code distractors are single-operator mutations of the gold answer. In a controlled training experiment against a paraphrase-matched control corpus, the detectable artifact went unexploited (an exploitation gap of 0.021 versus 0.027 for the control), and the audit is released as a mostly CPU-only protocol.

arXiv:2610.00202 PDF

DeepJEPA: Scaling World Models from Within

Zijian Jin, Yunbei Zhang, Yuanzhe Liu, Ming Liu, Baian Chen, Weirui Ye et al. cross-listed World-model planners usually scale by rolling out farther or sampling more trajectories, and they spend the same computation on every imagined transition. The authors argue this wastes compute, because useful refinement happens at a small number of decision-critical moments. DeepJEPA is a weight-tied joint-embedding predictive world model that learns, for each candidate and rollout step, whether another recurrent update is worth computing. Across five visual-control settings, it matches or beats the strongest fixed-depth planner while averaging only 1.00–1.26 updates per transition. The extra computation clusters at contact onset and sustained object interaction.

arXiv:2610.00368 PDF

MatrixReward: Reward from Rubric Matrix for Open-Ended Generation

Zihan Shen, Qi Liu, Zixuan Yang, Yiqun Chen, Chenglong Zhao, Xiaozhao Wang et al. Open-ended generation has no reference answers, and scoring each response against rubrics one at a time, then merging those scores, hides differences between responses. MatrixReward compares every pair of sampled responses under each rubric to build a response-by-rubric win-rate matrix. How spread out each rubric's column is shows how well it separates responses, correlations between columns expose redundant rubrics, and together these set the rubric weights. Each response is then rewarded by its distance to the best and worst observed profiles. Using Qwen3-8B, it averages 63.02 across four open-ended benchmarks, about 2.0% above the strongest baseline.

arXiv:2610.00389 PDF

Make Sparse Rewards Count: Density-Aware Reward Aggregation for Multi-Reward RL

Tong Zheng, Skylar Zhai, Zhan Cheng, TianMing Sha, Youling Huang, Shuo Zhou et al. When LLMs are trained with reinforcement learning against several rewards at once, some objectives learn more slowly than others, even with the per-reward normalization used in GDPO. The authors show that each reward's advantage energy is proportional to how often it gives a nonzero signal within rollout groups. They propose DARA (Density-Aware Reward Aggregation), which reweights each reward per batch by an inverse-square-root density correction. On tool calling and math reasoning, DARA learns the targeted behaviors faster than GDPO, reaching near-saturated length compliance in up to 65% fewer training steps, while final performance stays competitive.

arXiv:2610.00574 PDF

ALER: Adaptive Learnable Experience Rewriting for Reinforcement Learning

Oleg Shchendrigin, Egor Cherepanov, Aleksandr I. Panov, Alexey K. Kovalev Memory benchmarks for partially observable reinforcement learning mostly test whether an agent can keep information unchanged, not whether it can overwrite stored information or transform it according to later observations. The authors formalize these rewriting and fusion requirements and introduce ALER, an agent that pairs an LSTM with a slot memory updated through Gumbel-Softmax writes and a learned fusion gate. They also release Rune-Mazes, environments where observations invert, cancel, reset, or repeat updates to a hidden cue. Against seven baselines, ALER reaches a success rate of at least 0.99 on all Rune T-Maze compositions and at least 0.82 on all Endless T-Maze configurations.

arXiv:2610.00592 PDF

Beyond Supra-Competitive Outcomes: Collusive Behaviour in Deep Reinforcement Learning for Optimal Execution Games

Christos Spyridon Koulouris, Carlo Campajola cross-listed Earlier work found that independent reinforcement learning agents in a two-player Almgren-Chriss liquidation game reach costs below the Nash benchmark. The authors look for evidence that this reflects collusion by having one PPO agent deviate profitably and observing how its opponent responds. The opponent accelerates its liquidation, wiping out more than the deviator's gain in every run, while its own average payoff stays roughly unchanged, even though less punitive plans would have earned it more. Two formal checks on whether the punishment outweighs the gain and whether the change in behavior accounts for the loss both hold, supporting a collusive interpretation.

arXiv:2610.00619 PDF

Learning Transferable Skills using Goal-Conditioned Bisimulation

Mohammad Amin Abbasfar, Farbod Azimmohseni, Mohammad Hossein Rohban Unsupervised skill discovery pretrains policies on reward-free data, but current methods either need expert data or fail to transfer to environment layouts they have not seen. The authors learn action-aware temporal representations that preserve the environment's local temporal structure, then discover skills using bisimulation, conditioning each skill only on the state features that affect how it executes. This makes skill behavior invariant across layouts, and experiments show that skills learned in one environment solve downstream tasks in different layouts, demonstrating strong out-of-distribution generalization.

arXiv:2610.00676 PDF

Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving

Huiwen Yan, Kyriakos G. Vamvoudakis, Mushuang Liu Meta-reinforcement learning (meta-RL) lets agents adapt quickly to new tasks, but it has mostly been studied with a single agent. The authors extend it to multi-agent reinforcement learning (MARL) by modeling tasks as Markov games, where a task depends on the other agents' strategies as well as the environment. They define a solution concept called meta-NE and prove sufficient conditions under which it coincides with a stationary point of their gradient-play meta-MARL algorithm. On autonomous-driving tasks, the method adapts faster than pretrained MARL baselines.

arXiv:2610.00705 PDF

JEPA-TTT: Persistent Test-Time Training of Latent World Models for Planning under Dynamics Shifts

Zheyuan Zhang, Suyu Ye, Nakul Agarwal, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Daniel Khashabi et al. World models let agents plan by predicting future states, but their predictions become unreliable when the dynamics at test time differ from training. JEPA-TTT keeps updating the latent dynamics predictor of a pretrained action-conditioned Joint-Embedding Predictive Architecture (JEPA) world model throughout testing, with self-supervised updates that accumulate across episodes. The visual encoder and reward head stay frozen, and a dense replay buffer collects prediction windows at every temporal offset. Across eight dynamics shifts in four continuous-control environments, planning improves on every shift. After 500 episodes, latent prediction error drops by 83% on average and planning performance improves by 153% over the frozen world model.

arXiv:2610.00722 PDF

Reward as Observation: Learning Reward-Based Policies for Rapid Adaptation

Morgan Byrd, Maks Sorokin, Robert Wright, Sehoon Ha Deep RL policies usually need many samples to adapt to a new environment, especially when observations look completely different. The authors propose a policy conditioned only on the history of rewards and actions, with no environment observations, so it can transfer zero-shot across different observation spaces. They discuss when such a policy is feasible and give a practical training algorithm. Policies trained in Pointmass, Cartpole, and 2D Car Racing transfer zero-shot to new color palettes, 3D rendering, and Stretch robot navigation in Habitat-Sim, and they can also guide the training of an observation-based policy in the target environment.

arXiv:2610.00729 PDF

Scalable Multi-Task Inverse Reinforcement Learning

Allen Tran, Jia Wan, Nathan Kallus, Aur\'elien Bibaut Inverse reinforcement learning (IRL) recovers reward functions that can be reused to evaluate agents in modified environments, but this transfer requires the data to cover the relevant states well. The proposed multi-task IRL method pools data from many agents with different rewards in the same environment, assuming the rewards share low-rank structure. Each task then only needs states that some other task visits, and planning cost in new environments scales with the rank instead of the number of tasks. The authors give finite-sample guarantees for reward recovery and for policy learning in new environments. Experiments show robustness to limited coverage, lower regret than baselines in target environments, and a computational advantage over per-task methods that grows with the number of tasks.

arXiv:2610.00758 PDF

Crossing the Cyber Divide: Sim-to-Sim and Sim-to-Real Transfer for RL Agents

Sabrina Saika, Yinuo Du, Aritran Piplai cross-listed Reinforcement learning agents for cyber attacks are usually trained and tested in a single simulator, so it is unclear whether their policies work anywhere else. The authors treat simulator-to-simulator and simulator-to-real transfer as one alignment problem and propose a framework that handles state alignment separately from action translation, letting a policy run in a new environment without retraining. They evaluate on four platforms (CyberBattleSim, NetSecGame, CyberWheel, and NASim), including emulated deployments. Zero-shot transfer fully preserves performance between closely aligned environments and reaches a 45.2% win rate for policies with 60.5% source performance. In emulated virtual-machine environments, transferred policies stay close to native ones, with a Jensen-Shannon divergence of 0.085.

arXiv:2610.00759 PDF

SHARPO: Segment-Level Credit Assignment for Agentic Reinforcement Learning

Xinchen Du, Zhengze Zhou, Wenhui Zhu, Han Yu, Sen Na, Rohit Jain et al. In agentic reinforcement learning, an LLM acts over many steps, and a single reward for the whole trajectory gives little signal about which decision caused a failure. Segment-level Hindsight Advantage Reweighting for Policy Optimization (SHARPO) refines Group Relative Policy Optimization (GRPO) by computing teacher-student log-probability gaps within each segment of interaction with the environment, borrowing from on-policy self-distillation. That gap becomes a bounded multiplier on the advantage, shared by every token in the segment, so credit can differ from one step to the next. Using Qwen2.5-7B-Instruct, SHARPO outperforms GRPO, SDAR, RLSD, and StepOPSD on ALFWorld and WebShop.

arXiv:2610.00838 PDF

Learning Multiple Timescales for Goal-Conditioned Reinforcement Learning

Pedro Robles Dutenhefner, Dikshant Shehmar, Wagner Meira Jr., Marlos C. Machado In offline goal-conditioned reinforcement learning (GCRL), discounting shrinks value differences between distant states until they fall below approximation error. Treating k environment steps as one transition fixes this at long range but blurs distinctions between nearby states. GITA (Generalized Implicit Temporal Abstraction) conditions a single value function on k and trains one policy by aggregating advantage-weighted supervision across several k values, so the agent keeps both local resolution and long-range signal. On OGBench it raises average success rate by 25 percentage points (73% relative) over HIQL and by 7 points over the strongest fixed-k method, OTA.

arXiv:2610.00849 PDF

Sharpen Before You Adapt: Data-Free Entry-State Sharpening for Test-Time Reinforcement Learning

Zhanming Zhang, Vinoth Selvendran Test-time reinforcement learning (TTRL) adapts a language model on unlabeled test problems using supervision derived from its own samples, so a diffuse starting policy wastes much of its limited adaptation budget just concentrating probability mass. The authors propose entry-state sharpening, which applies data-free training before TTRL to put a general-purpose checkpoint into a more exploitable starting state. Across five data-free checkpoints derived from Qwen3-4B under an identical 15-step TTRL protocol, entry policy entropy strongly rank-orders how efficiently adaptation converts reachable capability into reliable accuracy (Spearman ρ = −0.90). The diffuse R-Zero checkpoint ends below the untuned base in all six matched comparisons, while the low-entropy SPIRAL checkpoint reaches the best post-TTRL accuracy on MATH and GPQA even though its self-play stage used no math data.

arXiv:2610.00903 PDF

RISED: RubrIcs for agentic multi-environment Selection and sElf-Distillation

Jingtan Wang, Sirajul Salekin, Young mok Jung, Javier Movellan, Bryan Kian Hsiang Low, Manjot Bilkhu When one LLM agent is trained with reinforcement learning across many interactive environments at once, scalar rewards say little about how environments relate to each other. They also give no learning signal for rollout groups in which every attempt succeeds or every attempt fails. RISED has an LLM judge tag each rollout with labels from a shared rubric vocabulary, and uses the resulting behavior profiles to select a diverse, representative batch across environments. Positive rubrics serve as privileged context for an on-policy self-distillation teacher that adds token-level supervision, while negative rubrics steer later rollouts away from recurring failures. Across model backbones, RISED achieves the highest mean pass rate and ranks first or second in every individual environment.

arXiv:2610.00979 PDF

Grounding Large Language Models in DSGE Simulators for Policy Generation and Forecasting

Aditya Dubey, Namah Gupta, Vinti Agarwal To test whether LLM-generated economic policies are actually consistent with economic dynamics, rather than just plausible-sounding, the authors place an instruction-tuned model inside six Snowdrop-backed dynamic stochastic general equilibrium (DSGE) simulators. At each turn, the model observes the economy and a shift in economic discourse, picks a bounded policy action, and receives the next simulated state and a reward, all through a common Python interface that supports rollouts, persistent shocks, and state cloning. Because policy effects can appear quarters later, the setup poses a long-horizon credit-assignment problem, and the authors argue that PPO's learned value function can propagate delayed reward while critic-free GRPO cannot attribute outcomes to specific turns. Experiments cover reward horizon, warm starts, cross-simulator transfer, and historically anchored pandemic and monetary-policy shocks.

arXiv:2610.01128 PDF

Fully Online Decentralized Learning in Stochastic Games with Unknown Independent Chains

S. Rasoul Etesami The setting is stochastic games in which each player controls its own independent Markov chain with unknown transitions and sees only its local state and realized payoff. The authors propose a fully online, decentralized, uncoordinated mirror-descent algorithm that works in the dual space of occupancy measures. It uses one sample per time step and needs no synchronized episodes and no coverage of the joint state space. With high probability, time-averaged regret decays at the canonical O(T^{-1/2}) rate, with complexity that depends on cover times of individual local state spaces rather than the joint space, which avoids exponential blow-up in the number of players. This yields approximate coarse-correlated-equilibrium guarantees, and under an extra variational-stability condition the last iterate converges to a stationary ε-Nash equilibrium.

arXiv:2610.01181 PDF

Dependency-Aware Reward Shaping for Agentic Reinforcement Learning

Ziyi Chen, Yan Zhang, Jianhui Wei, Daoan Zhang, Zuozhu Liu When large language models are trained with reinforcement learning on a single final success-or-failure reward, every step in a failed episode receives zero credit, even steps that made real progress. Dependency-Aware Reward Shaping (DARS) represents task progress as predicates linked by prerequisite relations. An annotator marks which predicates each step verifies, invalidates, or repairs, and credit is discounted by graph distance from the nearest broken prerequisite, so independent work keeps its value. The approach plugs into methods like GiGPO and ARPO/AEPO without changing their rollouts or optimizers. Across five task families and 1.5B–8B models, it improves success by up to 10 points over GiGPO on ALFWorld, with gains on WebShop, Search-R1 QA, and AIME24/25, and a distilled 8B annotator matches a frontier API annotator.

arXiv:2610.01207 PDF

Learning Commute-Time-Preserving World Models for Planning

Michael Hauri, Peter Buttaroni, Fabian A. Mikulasch, Friedemann Zenke World models plan in latent space by choosing actions that shrink the distance to a goal, so planning works best when latent distances mirror commute times in the environment, as in a correctly scaled spectral embedding of the graph Laplacian. The authors show that existing self-supervised methods, which push representations toward isotropy to avoid collapse, distort this scaling. Commute-Time-Preserving World Models (CTWMs) combine a latent displacement predictor with a log-determinant regularizer and provably recover the correctly scaled Laplacian representation under reversible deterministic dynamics. On continuous goal-reaching benchmarks, CTWM matches or beats the LeWM baseline with half the parameters.

arXiv:2610.01373 PDF

Reusing Past Samples in Proximal Policy Optimization: When and How Does It Help?

Alessandro Montenegro, Riccardo Venturelli, Marco Mussi, Matteo Papini, Alberto Maria Metelli Proximal Policy Optimization (PPO) throws away fresh data after a few updates, and hybrid variants that reuse past samples exist, but there has been no systematic study of when such reuse helps. The authors build two PPO variants within a multiple importance weighting framework, wPPO-U with vanilla importance weights and wPPO-BH with balance-heuristic-corrected weights. Both keep the core PPO mechanics and reuse only samples from a window of recent iterations, which isolates the effect of reuse. They derive policy-improvement lower bounds for both losses and use them to study empirically when and how data reuse improves sample efficiency or final performance on continuous control tasks.

arXiv:2610.01399 PDF

Optimal Transport Meets Reinforcement Learning: A Survey

Yujie Zhu, Charles A. Hepburn, Matthew Thorpe, Giovanni Montana cross-listed Reinforcement learning (RL) algorithms often compare probability distributions, such as state visitation, action, or transition distributions, and common divergences break down when these distributions barely overlap, as happens in imitation learning, offline RL, and under distribution shift. This survey covers how optimal transport (OT), which measures the cost of moving probability mass under a task-aware ground cost, is used inside RL objectives and algorithms. For each method it identifies the role OT plays, which distributions are compared, the OT formulation used, and how temporal structure is handled. It also discusses cost design and computational issues, and lists open problems such as scalable trajectory-level transport, handling mass mismatch, and theory for OT-regularized RL.

arXiv:2610.01413 PDF

Rethinking Probability-Based Reinforcement Learning From Posterior Concentration

Shiu-Hong Kao, Yubo Zhao, Zhenyu Tian, Pengzhan Sun, Yicong Li, Angela Yao Verifier-free reinforcement learning (RL) rewards an LLM based on the probability it assigns to a reference answer after a reasoning trace, which allows training on tasks with no external verifier. The authors identify the Posterior Concentration Phenomenon: as traces get longer, these probabilities collapse into a narrow, low-variance band, which makes rewards nearly indistinguishable and destabilizes GRPO-based training. Their method, RLCPR, samples training data with uncertainty in mind to avoid rollouts prone to concentration, and penalizes unnecessarily long traces when the rewards collapse. RLCPR beats the strongest verifier-free RL baseline by up to 4.0% on six of seven general and mathematical reasoning benchmarks, while using fewer tokens.

arXiv:2610.01458 PDF

Sharpening Tax in Post-Training

Changdae Oh, Qi Zeng, Qi Qi, Andrey Zhmoginov, Deren Lei, Yun He et al. Tests whether reinforcement learning post-training of LLMs merely sharpens existing behavior on agentic, multi-turn tool-use tasks. Pre-trained base models with a light inference harness have far lower single-shot accuracy, but often beat their post-trained counterparts in solution coverage (pass@K) given enough test-time samples. The authors find that post-training pushes tasks toward always-solved or never-solved, and they propose Sharpening Tax, a metric for the lost test-time scalability. The tax appears in most of 42 model-benchmark cases across 14 model pairs. A Bayesian sampler that adapts temperature to each prompt's difficulty, posterior-tempered group sampling (PTGS), pays a smaller tax during RL training while also improving single-shot accuracy.

arXiv:2610.01509 PDF

Decision Titan: Test-Time Training for Long-Term Memory in Offline Reinforcement Learning

Jude Waide, Robert Lieck Applies the Test-Time Training (TTT) framework, which stores episodic memory in network weights updated by gradient descent at inference, to offline reinforcement learning by adding TTT layers to a Decision Transformer. The resulting Decision Titan is analyzed in X-Maze, an extension of T-Maze built to test sequential memory, with visualizations of gate values over time. It learns dependencies 20x longer than its context window and generalizes to sequences 1.7x longer than its training data. Temporal generalization depends on the choice of time embeddings, and long-range learning depends on how the relevant information is encoded.

arXiv:2610.01513 PDF

Reinforcement Learning to Accelerate Primal-Dual Hybrid Gradient for Linear Programming

Jinhwan Sul, Alex Oshin, Evangelos A. Theodorou cross-listed Primal-dual hybrid gradient (PDHG) methods solve large linear programs efficiently on GPUs, but their speed depends on hand-coordinated step parameters, acceleration and restarts. GALLOP uses reinforcement learning, specifically a groupwise variant of proximal policy optimization (PPO), to train a dimension-agnostic policy that jointly controls continuous parameters and discrete restart decisions without differentiating through the solver. Across six LP families it cuts iteration counts by 1.9-5.6x and achieves up to 16x wall-clock speedup over MPAX. Policies trained once per family generalize without retraining to instances 3-400x larger, including transport LPs with 10.24 million variables.

arXiv:2610.01546 PDF

Range-GRPO: Policy Optimization via Pairwise Relations among Reward Intervals

Ryunyi Lee, Kangjun Noh, Somin Kim, Heedong Kim, Kyungwoo Song Post-training LLMs in domains without reference answers or verifiers often relies on LLM-as-a-Judge pseudo-rewards, but a single point score hides how uncertain the judge is. Range-GRPO is a semi-supervised method that turns judge scores into conformally calibrated reward intervals. Within Group Relative Policy Optimization (GRPO), it compares these intervals pairwise, so uncertainty affects both the size and the direction of the advantage signal. The objective provably reduces to the Dr.GRPO advantage when the intervals collapse to points, and empirically it achieves the best in-distribution and out-of-distribution averages among the semi-supervised methods tested while using fewer training resources.

arXiv:2610.01548 PDF

Towards Optimal Policy Improvement

Yaniv Oren, Viliam Vadocz, Wiktor Zabka, Thomas Evers, Jan Robine, Wendelin B\"ohmer et al. Practical reinforcement learning algorithms improve a policy step by step using value estimates that are only approximate. The authors define optimal policy improvement as the best policy reachable in one update under given constraints, and they show that optimal improvement over a set of states is equivalent to solving an induced Markov Decision Process (MDP), which casts planning as a route toward it. Treating greedification under approximate evaluation as decision-making under uncertainty, they derive a new operator that is optimal for that objective. The operator and its gradient-based approximations improve aggregate performance across GumbelAlphaZero, SAC, ReBRAC and Generalized Policy Iteration, covering discrete and continuous actions, model-based and model-free, and online and offline RL.

arXiv:2610.01566 PDF

Continual Reinforcement Learning with Neuroevolution

Eleni Nisioti, Andrea Cossu, Kathrin Korte, Sebastian Risi cross-listed Reinforcement learning (RL) agents that face changing tasks tend to lose plasticity or forget earlier tasks, and no RL method has consistently balanced the two. The authors test neuroevolution, which searches weight space directly through mutation and selection over a population. They compare evolution strategies (ES) and genetic algorithms (GAs) with continual-RL and population-based RL baselines across many environments, using policies from a few hundred to millions of parameters. ES gives the most consistent stability-plasticity trade-off, which the authors tie to its wide, overlapping solution neighborhoods in weight space; the GA adapts fastest but forgets more, and the usual symptoms of plasticity loss in RL do not appear in neuroevolution.

arXiv:2610.01583 PDF

Iterative Policy Refinement through Semantic Rollout Analysis

Feiyu Gavin Zhu, Qi Xu, Zhifei Deng, Zhigang Hua, Luke Simon, Jean Oh et al. Structured policies give imitation learning useful inductive bias, but generating that structure usually needs heavy human input or relies on static large language model (LLM) knowledge that may disagree with the expert demonstrations. The proposed closed-loop framework logs policy rollouts as semantically meaningful tabular data and prompts an LLM to write diagnostic analysis code, which identifies flaws in the policy structure and corrects them iteratively without human instruction. On car racing and door opening tasks, the approach improves imitation learning performance by up to 15% over zero-shot LLM-generated structures and needs 75% less compute to match a given reinforcement learning result.

arXiv:2610.01652 PDF

MiLoop: Selective Memory Propagation for Neural Combinatorial Optimization

Changliang Zhou, Yuanyao Chen, Rongsheng Chen, Zhiyun Lin, Zhenkun Wang Constructive neural combinatorial optimization builds solutions step by step, but methods with dynamic embeddings typically rebuild subproblem representations from scratch at every step with deep attention stacks, and the strongest ones lean on solution labels or aggressive search-space pruning. MiLoop (Memory-in-the-Loop) is purely reinforcement-learning-based: it reuses the multi-step computation a rollout already performs to propagate historical representations, fusing current embeddings with memory before the attention layers and applying adaptive gated updates afterward. This lets a shallow policy learn effective dynamic embeddings without external labels, producing high-quality solutions across four problem types on instances from 100 up to 10 million nodes.

arXiv:2610.01685 PDF

Q-Learning for Reachability in MEC-Free MDPs

Lu-Chin Chang, Suguman Bansal Reinforcement learning for reachability goals has had convergence guarantees only for model-based methods that estimate transition probabilities explicitly. Quasar is the first model-free algorithm with asymptotic guarantees for reachability. It covers Markov Decision Processes (MDPs) without non-terminal maximal end components, a class every MDP can be reduced to through the standard quotient construction. It uses ordinary Q-learning with temporal-difference updates and cuts memory from O(|S|²|A|) to O(|S||A|). On the Quantitative Verification Benchmark Set it reaches the optimal policy with orders of magnitude fewer samples than the previous model-based state of the art.

arXiv:2610.01781 PDF

Temporal-Difference Learning for Dragonchess

Jim O'Connor, Annika Hoag, Sarah Goyette, Gary B. Parker Two adaptive methods for learning board-evaluation heuristics, evolutionary transfer learning and TD(lambda) temporal-difference learning, are compared in Dragonchess, a three-dimensional chess variant with an unusual board structure and a heavy computational load. The authors port the game engine from PyGame to C++, which makes it fast enough to play 10,000 games and report confidence intervals and significance tests instead of results from a single small tournament. Both adaptive methods beat every other agent in a round-robin tournament, but there is no significant difference in performance between the evolved and the TD-learned evaluations.

arXiv:2610.01845 PDF

Flowing Faster to Coordinate: One-Step Online Multi-Agent Flow Policies

Zhuoran Li, Yunzhan Li, Xun Wang, Yihan Du, Longbo Huang Diffusion-based policies can represent the complex, multimodal action distributions that multi-agent reinforcement learning (MARL) needs, but their iterative sampling is too expensive for online training. OMAF uses a Transformer-based flow policy that generates each action in a single step. Training relies on an approximate path-score surrogate for synchronized policy optimization and a joint scheme that couples softmax Q-value estimation with a joint flow-policy objective. Across 10 tasks from MPE and MAMuJoCo, OMAF achieves up to 3.4× higher returns and up to 10.5× better sample efficiency than baseline methods.

arXiv:2610.01882 PDF

Asynchronous LLM Post-Training: Group-Mass Capping and Convergence Analysis

Qijia He, Ruinan Jin, Jun Luo, Shaofeng Zou, Yingbin Liang Asynchronous reinforcement learning (RL) speeds up LLM post-training but trains on stale rollouts generated by earlier versions of the policy. The authors derive a convergence bound for GRPO-style algorithms showing that, once the estimator's second moment is controlled, delay affects convergence through the bias that clipping or rescaling introduces. Guided by this, they propose GMC-GRPO, which caps importance weights at the group level to minimize that bias. Compared with TIC-GRPO, it improves the dependence of the fourth-order delay term on the ratio threshold from O(ε⁻⁴) to O(ε⁻²), and its group-rescaling bias vanishes as group size grows, whereas the bias from trajectory-wise clipping can persist. Experiments on Qwen3 models show the best performance among stable baselines under large rollout delays.

arXiv:2610.01896 PDF

Bellman Meets Lyapunov: Unsupervised Reinforcement Learning via Mastering Chaos

Tristan Shah, Wooyoung Chung, Volodomyr Makarenko, Juan Wachs, Stas Tiomkin Unsupervised reinforcement learning (RL) aims to replace hand-designed rewards with intrinsic motivation, but existing intrinsic objectives still require choosing which information variables matter, which brings domain expertise back in. The authors introduce Forward CIP (F-CIP), an RL-native form of the Controllable Information Production objective that depends only on the system's dynamics, and prove it is compatible with standard RL algorithms. Agents trained with F-CIP discover primitive behaviors such as balancing and staying controllable without any task reward. Combined with only a simple forward-velocity reward, the method produces coordinated hopping and running gaits that normally require reward engineering.

arXiv:2610.02012 PDF

CARM: Cancellation-Aware Response Masking for LLM Reinforcement Learning

Yafei Zhang, Songshuo Lu, Sicong Liao, Zhi Chen, Yaohua Tang In LLM reinforcement learning (RL), sampled responses become off-policy because of policy updates and mismatches between rollout and training engines, and sequence-level masking decides which responses to train on. The common rule averages signed token log-probability ratios, so changes in opposite directions can cancel and hide substantial policy drift. Cancellation-Aware Response Masking (CARM) averages absolute log-ratios instead, with a proven bound on how many token ratios in an accepted response can fall outside a set band. CARM improves mean@16 by up to 3.13 points over geometric-mean masking on AIME 2024/2025/2026 and BeyondAIME, and raises average pass@1 across four code benchmarks by 2.88 points over the strongest baseline.

arXiv:2610.02039 PDF

Homomorphic Advantage Operator: Stabilizing Reinforcement Learning Under Fully Homomorphic Encryption Constraints

Abid Mohamed Nadhir, Ahmad Al Hanbali, Beggas Mounir Running reinforcement learning (RL) under Fully Homomorphic Encryption (FHE) means swapping non-linear operations for polynomial approximations, and these diverge through a recursive error the authors call Bellman drift. The Homomorphic Advantage Operator (HAO) applies a zero-mean centering projection, borrowed from advantage-based value estimation, to temporal-difference targets, which removes the uniform baseline driving the drift without adding multiplicative depth or ciphertext bootstrapping. Tests covered a tabular Markov Decision Process, an encrypted CartPole using real CKKS operations, and a 20-node logistics routing benchmark. HAO agents had 0% boundary breaches across all seeds, versus breaches on 3 of 5 seeds with regularization alone, and they gained 18 percentage points in optimal policy accuracy in tabular domains.

arXiv:2610.02074 PDF

Faynt: Scaling and Optimizing Policies for Competitive Melee

Ali Janati, Nikita Kuzmin, Rohit Swamy, Charles Niu Faynt is a family of 10M- and 75M-parameter Transformer policies for Super Smash Bros. Melee, where a single checkpoint controls all 26 characters. The policies are pretrained on about 840,000 human replays, then post-trained with curricula, 75M-to-10M distillation and reinforcement learning (RL) on Fox mirror matches. After RL, the 10M model wins 240 of 244 same-character games (98.4%) against fourteen existing bots, and all 68 games against a zero-delay Slippi-AI model. The authors note that opponents in the main evaluation ran with added action delays while Faynt did not, and they have not isolated the effect of this difference. Inference takes about 5.2 ms per decision on an NVIDIA T4, and the weights, benchmarks and a tournament platform are open-sourced.

arXiv:2610.02144 PDF

When Do Intrinsic Rewards Lead to Exploration?

Scott W. Viteri (Stanford University), Laura Gomezjurado Gonzalez (Stanford University), Clark Barrett (Stanford University) Intrinsic rewards such as prediction error or learning progress are meant to drive exploration in reinforcement learning, but maximizing them need not yield the most informative experience. The authors propose a formal criterion that compares policies by how much counterfactual information they gather, meaning how well their histories could substitute for experience collected under other policies. Using one simple environment, they show that count-based, prediction-error, empowerment and information-gain objectives all have maximizing policies that are Pareto-suboptimal under this criterion. They explain these failures and give conditions under which existing intrinsic rewards do lead to optimal exploration. They also construct an objective that assigns higher value whenever exploration strictly improves under their criterion.

arXiv:2610.02159 PDF

FERPO: Forward Entropy-Regularized Policy Optimization

Sebastian Sanokowski, Alireza Sarmadi, Majid Khadiv Many continuous-control reinforcement learning methods improve the policy using action gradients of a learned critic, but a critic that predicts returns accurately can still have unreliable action derivatives. FERPO (Forward Entropy-Regularized Policy Optimization) is an on-policy maximum-entropy algorithm that uses critic values without differentiating the critic. It derives a target action distribution regularized by entropy and Kullback-Leibler (KL) divergence, then fits the actor to that target with a forward-KL objective estimated by self-normalized importance sampling (SNIS). Forward KL encourages the policy to cover multiple high-value modes, which aids exploration. On MuJoCo Playground and ManiSkill it shows competitive performance and sample-efficiency gains, with faster actor updates than REPPO.

arXiv:2610.02198 PDF

7 more specialized papers

Vision 46

Emergent Object Binding Has a Finite Spatial Horizon

Mayank Singal cross-listed Frozen patch embeddings from self-supervised Vision Transformers can be probed to tell whether two patches belong to the same object, and the high accuracy of such probes is taken as evidence that object binding emerges from pretraining. The authors show that binding is local: the probability of decoding two same-object patches as bound decays roughly exponentially with distance down to a nonzero floor. This holds across probe families, on ADE20K and COCO, and for DINO and CLIP backbones. Treating binding as spatial coherence with a finite range explains why it weakens on large objects, separates same-class instances poorly, and groups parts with wholes, while occlusion has no effect once object size is controlled.

arXiv:2610.00006 PDF

Spatial Lifting for Dense Prediction

Mingzhi Xu, Tao Zhou, Yong Li, Yizhe Zhang cross-listed Spatial Lifting handles dense prediction tasks by lifting inputs such as 2D images into a higher-dimensional space and processing them with networks built for that dimension, such as a 3D U-Net. Despite the added dimension, the approach performs well on benchmark tasks while drastically reducing parameter count and inference cost. The lifted dimension produces structured outputs, which allow dense supervision during training. They also make it possible to estimate quality and uncertainty from self-consistency in a single forward pass.

arXiv:2610.00017 PDF

EvoGen-Harness: Learning Where and How to Evolve Image-Generation Harnesses

Jiabin Luo, Yinan Liu, Chunlei Meng, Yufei Guo Text-to-image (T2I) systems can be improved without retraining the generator by changing the system around it, but existing methods tune only one part, such as prompts, routing, or workflows. EvoGen-Harness lets several of these parts evolve together. Its Trace method combines evidence across random runs, uses failure attribution to decide which part to update, and re-attributes remaining failures, while checks reject unnecessary or harmful updates. It beats the strongest baselines by +0.2633 on GenEval2, and by smaller margins on T2I-CompBench++ and WISE, with only 1.9% regression.

arXiv:2610.00383 PDF

Scores That Hold, Benchmarks That Leak: Measuring Dataset Contamination in Public Brain-Tumor MRI Classification

Bhanu Prakash Vangala, Sowmya Guda, Latha Peddi, Navya Vangala cross-listed Deep learning models for classifying brain tumors from MRI routinely report accuracy above 98% on public benchmarks. The authors audit the three most-used datasets for leakage at three levels: duplicate images, shared patients, and acquisition source. In the dominant dataset, 28.8% of test images have a near-twin in the training split, and 95.5% of traceable test images share a patient with training. File-header features that contain no anatomy separate tumor from no-tumor at 0.959 balanced accuracy, matching fine-tuned ResNet models. Removing every identified leaked image leaves accuracy essentially unchanged, which shows that a stable score does not prove a benchmark is sound. The authors release lists of contaminated files and deduplicated splits.

arXiv:2610.00421 PDF

Frozen Scenes, Shifting Winners: Configuration Fragility in Text-to-3D Evaluation

Anson Y. Lam, Shuqing Li, Michael R. Lyu Text-to-3D generators are ranked by scoring rendered images, so camera settings and caption wording become part of the measurement. The authors keep 300 generated scenes from six generators fixed and vary eight render and caption settings across 19 alignment evaluators. For 17 of 19 evaluators, variance from configuration alone exceeds the variance between generators, and 18 of 19 change their top-ranked generator under some configuration. However, no ranking reversal holds up under simultaneous statistical inference, so these winner changes are descriptive rather than confirmed. The authors recommend reporting the evaluation configuration alongside each score and using uncertainty estimates that account for configuration selection.

arXiv:2610.00447 PDF

PACT: End-to-End Learning of Human Pose, Contacts, and Forces from Video

Rikhat Akizhanov (MBZUAI), Yangsong Zhang (MBZUAI), Nikolai Kaliazin (MBZUAI), Peter Wolf (ETH Z\"urich), Yoshihiko Nakamura (MBZUAI), Pascal Fua (EPFL) et al. cross-listed PACT is an end-to-end model that jointly estimates human pose, contacts with the environment, and contact forces from a single video, rather than handling these in separate stages. It adds learnable contact-force tokens and a temporal transformer to a human reconstruction foundation model, with physics-based supervision to keep motion and forces consistent. To address the scarcity of force labels, the authors build an annotation pipeline that combines contact labeling with physics-based optimization. They also release ForceWall, a climbing benchmark with ground-truth forces from sensors. PACT achieves state-of-the-art contact and force estimation, outperforms staged approaches, and generalizes to interactions outside its training data.

arXiv:2610.00451 PDF

One-Step Generative Modeling via Training Dynamics Action

Zhangyong Liang, Ying Huang, Haibin Ling One-step generative models are trained through an iterative transport process, but existing objectives judge only how the distribution moves, not how easily a single shared network can realize the requested sample displacements. TDAction (Training Dynamics Action) instead chooses transport targets by their local cost to realize through shared parameter updates, while still requiring a set amount of distributional progress. The cost is posed as a soft-terminal control problem with a closed-form value, and a low-rank implementation based on randomized tangent probes adds no cost at inference time. On ImageNet 256×256, the one-step generator reaches an FID below 1.1 without distillation.

arXiv:2610.00518 PDF

VisionQ: VLM-as-a-Judge Taxonomy, Dataset and Benchmark for Qualitative Analysis in Computer Vision

Vu Dinh Xuan, Duc-Hai Nguyen, Minh-Dung Dao, Vu Quynh Giao, Quang Hong Nguyen, Binh-Son Hua et al. cross-listed Vision-language models (VLMs) are increasingly used to judge qualitative comparison figures, but existing benchmarks only score overall preference, so a judge can pick the right image for the wrong reason. VisionQ is built from more than 1,800 validated comparison figures in 1,409 CVPR and ICCV papers, with 3,911 annotations that tie method outputs to specific visual claims the authors made, plus a 51-leaf taxonomy of visual criteria. A judge gets credit only when it picks the output the authors named as best on the stated criterion. Across 20 VLM judges, the best reaches only 63.1% accuracy against a 32.2% chance level, and a DPO-tuned Gemma-4-E4B judge (VisionQ-Judge) gains 2.5 points on held-out data.

arXiv:2610.00666 PDF

SemanTok: Predictable Semantic Tokens for Efficient Autoregressive Video Generation

Mikhail Dereviannykh, Vikram Voleti, Simon Donne, Mallikarjun Byrasandra Ramalinga Reddy, Shimon Vainer, Mark Boss cross-listed Video world models that combine autoregressive (AR) prediction with diffusion decoding depend heavily on their tokenizer. Existing flexible-length, coarse-to-fine tokenizers apply a representation-alignment (REPA) loss only to early decoder states, a target the decoder can partly meet from its noised input instead. SemanTok feeds frozen DINO features into its encoder and adds lightweight heads that must reconstruct those features from each retained token prefix alone. As a result, a 201M-parameter SemanTok AR model matches or beats a VideoFlexTok AR model 3.4 times its size. Short prefixes carry the clip's semantics and are cheaper to predict, while pixel detail is deferred to later tokens.

arXiv:2610.00686 PDF

Increasing Width Allows Greedy Layer-wise Training to Rival End-to-End Backpropagation in Self-Supervised Learning

Syon Mansur, Joel Zylberberg It is unclear under which architectural conditions networks trained with local, layer-by-layer objectives can match end-to-end backpropagation. The authors study how width and depth affect greedy layer-wise versus end-to-end self-supervised training of convolutional networks. As networks get wider, the advantage of end-to-end backpropagation shrinks, and in shallow, very wide networks greedy layer-wise training actually performs better. Representation analysis shows that very wide greedy-trained networks develop more favorable representational geometry, which suggests that width can compensate for limited credit assignment.

arXiv:2610.00753 PDF

Video Generation Models: A Survey of Post-Training and Alignment

Chaoyu Li, Xiaoyi Gu, Yogesh Kulkarni, Eun Woo Im, Mohammadmahdi Honarmand, Zeyu Wang et al. cross-listed The survey covers post-training and alignment for video generation models, which often fail to follow user intent, stay coherent over time, or respect physical and safety constraints despite strong pretraining. It treats post-training as a unifying framework, distinguishes implicit from explicit alignment, and groups methods into four categories: supervised fine-tuning, self-training and distillation, preference- and reward-based methods, and inference-time methods. It also reviews datasets, benchmarks, and evaluation practices. Open challenges include scalable reward design, long-horizon temporal consistency, and safety-aware generation.

arXiv:2610.00812 PDF

Towards Fast and Disentangled Counterfactuals for Visual Foundation Models

Sidney Bender, Benedikt Kunz, Ahmed Zeid, Shinichi Nakajima, Klaus-Robert M\"uller, Marco Morik Visual foundation models can rely on spurious correlations, and no existing method finds and removes such shortcuts in them without metadata. DiDAE (Disentangled Diffusion Autoencoders) wraps a frozen foundation model in a conditional diffusion decoder, so a counterfactual is a single closed-form edit along a direction from a disentangled dictionary followed by decoding. The dictionary can be built with supervision via Procrustes alignment or without it via SVD or sparse autoencoders. Because no gradients are needed, it is up to 2000× faster than the state of the art with comparable or better counterfactual quality, and its counterfactuals repair downstream classifiers through CFKD (Counterfactual Knowledge Distillation) better than metadata-based correction does.

arXiv:2610.00895 PDF

Dataset Identity, Not Novelty: The Source of an Inflated OOD Detection Gain

Donghoon Lee, Shinjin Kang Post-hoc out-of-distribution (OOD) detectors are often tuned on a sample of an OOD dataset and validated on held-out samples from that same dataset. This setup cannot tell whether a detector learned to recognize novelty or only learned to recognize that particular dataset. Holding out the entire OOD dataset on ImageNet and CIFAR-100 backbones shows that most of the reported gain comes from dataset identity rather than novelty, and the effect does not shrink with the size of the fit, so it is not ordinary overfitting. A closed-form expression predicts which fits will inflate their results, and only the single tuned constant the field already picks on a designated validation dataset survives the stricter protocol.

arXiv:2610.01096 PDF

PickMoment: Continuous-Time Single-Image-to-Video via Learning Deblurring and Blur-to-Video

Junseong Shin, Hyeonsu Jo, Daehyun Kim, Tae Hyun Kim cross-listed Motion blur is the average of a sharp scene over the exposure window, yet most learned methods recover only the frame at the center of the exposure or a fixed set of frames. Taking inspiration from MeanFlow, PickMoment instead learns the average image over any sub-interval of the exposure. It is trained with a reconstruction loss on available subframes, an additivity loss for consistency across overlapping intervals, and a sharp-frame loss at the zero-length limit. A single deterministic model handles deblurring, blur-to-video and recovery of any chosen moment in one forward pass, achieving state-of-the-art results among generative deblurring methods on GoPro and HIDE and the best per-frame fidelity on GoPro-7 blur-to-video.

arXiv:2610.01279 PDF

Smoother Flow Matching via Contrastive Trajectory Repulsion

Ziqi Jiang, Zhenqi He, Long Chen cross-listed In Flow Matching (FM), sample trajectories cross, and the authors argue that each crossing creates a large local Lipschitz constant in the target velocity field. That makes the field hard for networks to fit and causes integration errors when sampling with few steps. CoFlow brings contrastive learning into FM by adding a repulsive drift, formulated as a stochastic differential equation (SDE) with an equivalent stochastic-interpolant form, that pushes positive trajectories away from negative ones during training. On ImageNet 256x256 it significantly lowers FID in few-step (e.g., 20-step) sampling compared with standard FM, with no added training overhead.

arXiv:2610.01408 PDF

Task-Adaptive Grounded 3D-Programmers Using 2D VLMs

Arman Raayatsanati, Sombit Dey, Anna-Maria Halacheva, Jan-Nico Zaech, Luc Van Gool, Danda Pani Paudel cross-listed Vision-language models (VLMs) reason well in 2D, but their 3D understanding is limited by data scale and training diversity. Rather than retraining them for 3D, the authors add two components: Canonical Coordinate Framing (CCF), which anchors inputs and outputs to a shared Euclidean coordinate system to fix axis ambiguity and scale inconsistency, and Task-Adaptive Feedback (TAF), which closes an iterative feedback loop inside the model's native visual context. The resulting framework, 3D-Prog, performs open-vocabulary 3D understanding, manipulation, and generation without any retraining, at both object and scene level, and the authors report consistent, interpretable results across diverse 3D tasks.

arXiv:2610.02021 PDF

Generative Cinematographer: Composing Camera and Object Motion in 3D

Jiahan Zhang, Chaohao Yang, Namitha Guruprasad, Vivekjyoti Banerjee, Trong-Tung Nguyen, Alan Yuille et al. cross-listed Controllable video generators usually steer object motion with 2D trajectories or drag signals, which are ambiguous when the camera and objects move at the same time. GenCine lifts a single image into an editable 3D scene scaffold. There, artists author a camera path and move foreground regions with local 3D motion handles, and multiple handles can approximate non-rigid motion piecewise. The controls are projected into per-frame guidance maps that encode handle identity and world-space positions. A lightweight guidance branch and LoRA adapters are then trained on a pretrained Wan video model to follow those maps. Experiments show consistent camera-relative object motion and better geometric consistency under viewpoint changes across diverse real scenes.

arXiv:2610.02180 PDF

DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation

Zhengming Yu, Junkun Yuan, Haotian Yang, Gordon Guocheng Qian, Yizhi Wang, Angtian Wang et al. cross-listed Distribution Matching Distillation (DMD) trains fast few-step image and video generators, but it must keep an auxiliary diffusion model fitted to the student's changing outputs, which costs extra memory and compute. DMAD recasts distribution matching as classification. Two discriminator heads on a shared backbone learn the needed log-density ratios directly, and the authors prove this recovers DMD's gradient at the discriminator optimum. DMAD reaches an FID of 1.04 with one-step generation on ImageNet-64x64, 14.47 with four-step SDXL on COCO-10K, and a VBench score of 85.15 with four-step Wan2.1-T2V-14B, the best among the compared few-step methods and multi-step teachers. On joint audio-video generation with MiniMax-H3-33B, human raters preferred its outputs 79.1% of the time over DMD2.

arXiv:2610.02188 PDF

Embedding Prediction Helps Image Generation

Sihan Xu, Ji Xie, Zilin Wang, Hui Shen, Stella X. Yu cross-listed Diffusion transformers usually embed a class label or text prompt once and reuse that same condition at every denoising step. This work instead trains a NEPA (Next-Embedding Predictive Autoregression) Transformer to predict the clean image's embeddings from the condition and the current noisy image, all at once via Multi-Embedding Prediction. A DiT generator is then conditioned on these predictions, recomputed at every step so the conditioning adapts to the current noise state. The approach adds a second network to each sampling step. Combined with REPA, the final NEPA-DiT-XL model reaches an FID of 1.32 on class-conditional ImageNet 256×256 using about a third of REPA's training compute.

arXiv:2610.02203 PDF

27 more specialized papers

Multimodal 38

Encoded but Disconnected: Decomposing Vision-Language Model Failures under a Patching Null

Genpei Zhang cross-listed Across LLaVA-1.5-7B, Qwen2.5-VL-7B, and InternVL3-8B on the POPE benchmark, mid-layer probes find that the correct answer is encoded in 68-91% of the models' errors. Yet residual-stream patching flips 0% of predictions at the layer level on all three models, and per-head patching also flips nothing on two of them, so the encoded signal is not causally used. The errors still split into three failure modes: perception failure, encoded but disconnected, and prior override. These modes can be classified above 60% accuracy. Under oracle labels, each mode responds to a different intervention, which the authors present as evidence that the categories are real, not as a deployable fix.

arXiv:2610.00024 PDF

DramaAgent: Agentic Storytelling Video Generation

Ting Huang, Biao Wu, Ronghao Chen, Zeyu Zhang, Tengfei Cheng, Qizhen Lan et al. cross-listed Text-to-video models struggle to produce long story videos: over many scenes, the narrative drifts, character identity becomes unstable, and audio stops matching the visuals. DramaAgent adds an agentic control layer on top of existing video generators rather than changing the generator itself. It splits generation into story planning, persistent character conditioning, scene-by-scene synthesis, and a reflection step that diagnoses failures such as identity drift or audio-visual mismatch and repairs only the affected clips. Across several video generation backbones, it improves long-horizon coherence, character consistency, and scene-level audio-visual alignment over direct generation and strong baselines.

arXiv:2610.00097 PDF

LEGO-OPD: Factorized Teacher Composition for Multimodal On-Policy Distillation

Jaeyun Shin, Hangeol Chang, Jong Chul Ye cross-listed Multimodal on-policy distillation (OPD) that combines LLM and VLM teachers tends to mix the vision-language model's grounding signal with its own language prior. Stronger visual supervision can also erode the student's language reasoning. LEGO-OPD uses a generalized Bayesian formulation in which a language expert supplies a prior over candidate tokens and a grounding expert contributes only a visual likelihood that updates it. An adaptive calibration then sets the visual update strength at each decoding prefix based on how much the image shifts the grounding expert's predictions. With Qwen3 models, it outperforms the evaluated single- and multi-teacher OPD baselines on both multimodal and text-only reasoning, improving visual perception without hurting text reasoning.

arXiv:2610.00333 PDF

Paying for Too Many Tokens? Valid and Cost-Efficient Multimodal LLM Annotation with Simple Heuristics

Zhixi Zhu, Kristina Gligoric cross-listed Annotating video with vision-language models (VLMs) is expensive, because a 60-second clip at one frame per second can use millions of tokens. The authors systematically test common cost-saving shortcuts, such as frame sampling, image grids, and single-modality inputs, on sentiment and topic classification of short-form videos for computational social science. They score each setup on accuracy, on whether downstream statistical conclusions stay valid, and on token cost. The most accurate setup can still lead to wrong conclusions, and text alone is often strong. A single 2×8 image grid built from shot-transition detection comes close to full-video understanding at about 15% of the token cost.

arXiv:2610.00809 PDF

Platonic Task Arithmetic

Junghwan Park, Woojin Cho Weight-space task arithmetic, which adds or subtracts parameter updates to compose task skills, normally works only within a single model because different architectures share no common coordinate system. The authors hypothesize a shared, model-agnostic platonic task vector and operationalize it for image- or audio-text models as Universal Task Descriptors, architecture-independent matrices that record a task's functional effect and support addition and negation. A descriptor is transferred into a target model using only unlabeled probe images and class-name prompts, either as a closed-form linear edit to the last layer or as a trained low-rank adapter. Across six model families, eight classification tasks, and an audio-text setting, cross-model transfer retains 74–80% of the gain from the target's own descriptors, and transferred tasks can be composed.

arXiv:2610.00929 PDF

When the Judge Acts: Auditing VLM-Guided Image Selection on Culturally Situated Prompts

Huichan Seo cross-listed Vision-language models (VLMs) are often used as judges that pick the best of several generated images. They are usually validated by how well their scores agree with human ratings, not by the images they actually choose. On 300 culturally situated prompts, the authors compare each judge's chosen image against held-out human ratings and against random choice, and rerun every decision with the candidates reordered. A 4B-parameter judge barely beats random, scores below a CLIP similarity baseline, and picks the first image shown in 49% of calls (chance is 28%), changing its choice on 60% of prompts when the order changes. An 8B judge shows almost no position bias and beats CLIP, and filtering decisions by agreement across orderings helps the 4B judge but mostly discards good decisions for the 8B one, so such filters need re-auditing whenever the judge changes.

arXiv:2610.01243 PDF

MWOP: Modality-aware Width-wise Operation Pruning for Efficient MLLMs

Xudong Wang, Hao Wu, Haozhe Hu, Peiran Yin, Xinghao Chen, Yunpu Ma et al. cross-listed Multimodal large language models (MLLMs) are expensive to run on long sequences of images and text, and existing pruning methods treat each attention head and feed-forward network (FFN) channel as a single unit. Modality-aware Width-wise Operation Pruning (MWOP) separately prunes the visual-to-visual, text-to-visual, and text-to-text attention paths within each layer, and selects FFN channels separately for visual and textual inputs. A first-order Taylor criterion guides the pruning, LoRA training recovers accuracy, and custom Triton kernels turn the sparsity into real speedups. On LLaVA-OneVision-7B, MWOP gives a 1.6x prefill speedup while retaining 99.7% of average performance across 12 benchmarks, and combining it with token compression raises speedups from about 2x to 2.7–2.9x.

arXiv:2610.01434 PDF

AURAL: Adaptive Latent Reasoning with Joint Chunk for Speech Language Models

Yuxiang Wang, Kunyu Feng, Yuancheng Wang, Zihang Liu, Shengbo Cai, Qinke Ni et al. Explicit chain-of-thought (CoT) helps speech language models reason about audio, but generating the reasoning tokens delays the spoken response. AURAL reasons in latent space: it models a distribution over several possible reasoning continuations and predicts chunks of future states jointly, which reduces the number of sequential forward passes. It is first trained on AuralReason-683K, about 1,000 hours of bilingual speech with concise CoT, and then refined with RL (AURAL-RL) that rewards brief reasoning and spends more effort on harder questions. AURAL-RL matches CoT-based RL in quality, and on Qwen2.5-Omni it cuts time to the first answer token by 11.8x, from 1.22 s to 0.10 s.

arXiv:2610.01560 PDF

Before It Fades: Reinforcing Temporal Representations at Inference Time in VideoLLMs

Youngwoo Shin, Yusung Ro, Minseo Kim, Junmo Kim cross-listed Video large language models (VideoLLMs) often give the same answer when a video's frames are reversed, which shows a weakness in temporal reasoning. By measuring the layer-wise difference in representations between forward and reversed inputs, the authors find that temporal information peaks in intermediate layers and then fades toward the output. Their training-free method, Temporal Activation Injection (TAI), extracts this signal at the peak layer and reinjects it into later layers to counter the decay. TAI consistently improves temporal reasoning across three VideoLLMs and four benchmarks with negligible impact on non-temporal tasks.

arXiv:2610.01595 PDF

Hob-VL: A Benchmark for Visually Grounded Boolean Reasoning

Yuzhou Wang, Emile Anand, Ijay Narang cross-listed Hob-VL is a benchmark that tests whether vision-language models can check Boolean combinations of visual statements against an image and identify the unique object that matches a Boolean description. It contains 6,000 human-verified, balanced yes/no questions over generated scenes and labeled photographs, plus 1,000 object-identification questions, built with misleading local cues and nested logic. With thinking disabled or minimized, eight model configurations score between 48.52% and 50.57% on the Boolean questions, essentially chance, and reach at most 43.0% on identification. A thinking-enabled GLM configuration improves unevenly and still makes many errors and inconsistent answers.

arXiv:2610.01605 PDF

Not All Error Yields to Scale: Where Scaling Stops in Vision-Language Inference

Xinye Zhao, Yunkai Dang, Yunchen Wu, Wenbin Li cross-listed When deploying vision-language models (VLMs) on high-resolution images under a fixed budget, there is a trade-off between a larger language backbone and more visual tokens. The proposed Separable Law models performance as a function of both, fitted on 26 InternVL and QwenVL models with 1B to 72B backbones across four high-resolution benchmarks at image sizes up to 8K. Whether a question benefits from scaling can be predicted from the skill it requires, but a substantial fraction of questions never improves with scale at all. Combined with a cost model, the law gives a closed-form rule for splitting compute between backbone size and visual tokens.

arXiv:2610.01640 PDF

Do MLLM Judges Judge the Edit? Auditing Bias in Image Editing Evaluation with Verified Quality Preservation

Yuan Huang, Zirui Song, Xiuying Chen cross-listed Multimodal large language models (MLLMs) now serve as automated judges and reward signals for instruction-based image editing, but auditing them for irrelevant-cue bias is hard because any visual intervention may itself change the quality being judged. EditJudgeBias is a counterfactual benchmark of 1,196 real editing samples with 13 cues injected across four evaluation sites, where quality preservation is verified with calibrated multimodal validators, controls and human inspection, and shifts are compared against each judge's own zero-dose and re-query noise floors. Across five judges, every one moved beyond its own noise: fabricated majority opinions raised ratings, irrelevant visual elements shifted judgments more than whole-image manipulations, and swapping candidate order reversed up to 60.9% of pairwise decisions.

arXiv:2610.01670 PDF

Architectural Sampling: Test-Time Scaling via Computational Diversity in Frozen Vision-Language Models

Akshit Singh, Shyam Marjit, Wei Lin, Leonid Karlinsky, M. Jehanzeb Mirza cross-listed Test-time scaling usually samples many responses from a frozen model, but temperature sampling sends every candidate down the same fixed computation path. Architectural sampling is a training-free alternative that generates candidates through different forward computations by reusing selected blocks of decoder layers, varying block location and repetition count without touching weights. Across five Qwen checkpoints and twelve multimodal benchmarks it raises pass@9 by 6.58 percentage points on average at the same nine-candidate budget, with early-layer reuse strongest; the candidates overlap less lexically and work better as rollouts for label-free test-time reinforcement learning.

arXiv:2610.01687 PDF

CoEvolve: Construct-to-Edit Visual Grounding with Bidirectional State Refinement

Dongwei Sun, Yujie Zhang, Bowen Yao, Pei Liu, Jing Yao, Xiangyong Cao Visual grounding models usually squeeze target identification, spatial reasoning and boundary estimation into one final box prediction, so intermediate localization mistakes cannot be diagnosed or repaired. CoEvolve splits the task into explicit state construction and state editing: Region-Evolution Reinforcement walks a semantic-spatial trajectory where each reasoning step commits to a candidate region, and a Bidirectional Denoising Refiner treats the reasoning text as fixed context while reconstructing the coordinate fields. On natural-image and remote-sensing grounding, a 9B backbone matches models up to 241B parameters, and under deliberate corruption a single refiner pass raises mean box overlap by over 27 percentage points.

arXiv:2610.01710 PDF

VETO: Video Efficient Token Optimization for Vision Language Models

Gueter Josmy Faure, Hao Ping Wang, Min-Hung Chen, Winston H. Hsu cross-listed Running long videos through Vision-Language Models (VLMs) is expensive because attention cost grows quadratically with the number of visual tokens. VETO is a plug-in that does not require training and compresses along two axes in order: it first merges similar tokens within each frame using optimal-transport-style matching, then merges frames that are redundant over time. Shrinking each frame first makes the global matching across frames much cheaper. It gives up to 45% faster inference on LLaVA-OneVision-7B with equal or better accuracy, and with only 10% of the token budget it reaches 55.7% accuracy, ahead of VFlowOpt, VisionZip, and FastV, with results that hold across InternVL-2.5 and LongVA.

arXiv:2610.01785 PDF

LineupRL: Verifiable Reinforcement Learning for Time Series Captioning via Caption-to-Series Identification

Haochen Zhang, Laura Yao, Zachary Plotkin, Gengwei Zhang, Tianlong Chen Supervised fine-tuning for time series captioning can only match the larger model whose captions it copies, and existing reinforcement-learning rewards transfer poorly to this domain. LineupRL is a reinforcement learning with verifiable rewards (RLVR) pipeline whose reward comes from a frozen off-the-shelf LLM. The LLM reads the generated caption and must identify the described series, given as raw values, from among several distractors. Across two captioning benchmarks and on downstream forecasting and reconstruction from captions alone, it beats SFT and RL baselines, and a 3B vision language model trained this way outperforms the 72B model used as the distillation teacher. A case study suggests the reward resists hacking.

arXiv:2610.01800 PDF

Same Reward, Different Skills: When Multimodal RL Learns to Look

Haocun Ye, Xinlong Jiang, Qile Chen, Bingyu Wang, Teng Zhang, Shubai Chen et al. Reinforcement learning with verifiable rewards (RLVR) raises vision-language benchmark scores even when the model sees no images during training, which shows that putting an image in the prompt does not make it part of the learning signal. The authors propose a design rule they call visual resolvability: the task should require visual evidence to answer correctly and still be learnable. They test it on counterfactual coordinate scenes where the target is never named in the question. With standard GRPO, a 7B model raises its target-discovery accuracy from 0.425 to 0.875 on held-out scenes denser than any it trained on. Controls confirm the gain comes from the images: training on gray canvases yields almost none of it, and adding a caption that reveals the answer cuts it by nearly two thirds.

arXiv:2610.01908 PDF

MoLE: Mixture of Latent Experts for Complementary Visual Reasoning

Yingcheng Liu, Tianyi Jiang, Yujuan Ding, jiangbo Ai, Xun Jiang, Guoqing Wang et al. cross-listed Latent visual reasoning gives vision-language models continuous intermediate states, but because multiple latent tokens share the same value projections, they tend to extract redundant information. MoLE (Mixture of Latent Experts) isolates latent visual experts during evidence extraction and uses dedicated summary experts to combine their complementary outputs. Training runs in two stages, first forcing visual evidence through the latent pathway and then restoring direct visual access. Across five visual reasoning benchmarks, MoLE scores 78.6 on average, 4.9 points above data-matched supervised fine-tuning and 3.6 points above the strongest latent reasoning baseline at the same latent budget, and masking the latent pathway costs 9.2 points.

arXiv:2610.01917 PDF

Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes

Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc, Nicolas Thome, Spyros Gidaris cross-listed On-policy self-distillation, where a model learns from a frozen or EMA copy of itself that has privileged information, has improved language-model reasoning but has seen little use in multimodal large language models (MLLMs). Where-OPD gives the teacher textual, spatially grounded guidance naming the image regions relevant to a query, generated automatically from procedurally built synthetic scenes with known object identities and coordinates. The student learns to reproduce the teacher's behavior from the image and question alone. Gains appear on counting, document and chart benchmarks, and despite training only on synthetic scenes, the method improves average performance by 3.23 points on real-world benchmarks including CVBench, V*, BLINK and MME-RealWorld.

arXiv:2610.02117 PDF

19 more specialized papers

Robotics 27

DriftOPD: Sequence-Level Reverse-KL Distillation for One-Step VLA Policies

Youngjun Jun, Kyumin Choi, Youngmin Kim, Seonghyun Jin, Sunwoo Park, Jangho Park et al. cross-listed Vision-Language-Action (VLA) robot policies usually train their action experts on short action chunks. That optimizes local likelihood but ignores long-horizon task success, and sequence-level reinforcement learning would need costly real-robot rollouts. DriftOPD shows that sequence-level reverse Kullback-Leibler (KL) divergence splits into a chunk-level reverse-KL term and a future-potential term. It optimizes the first with a one-step drifting objective and the second with a Q-function critic learned from offline demonstrations, so it needs neither a teacher model nor online interaction. Across several VLA architectures in simulation and real-world manipulation, it generally beats one-step distillation baselines and matches the task success of multi-step teacher policies while generating actions in a single step.

arXiv:2610.00317 PDF

Same Scene, Different Task: Skill Alignment for Compositional Generalization in VLAs

Taegeun Yang, Youngju Na, Yoonki Cho, Sung-Eui Yoon cross-listed Vision-language-action (VLA) models often fail on combinations of skills that never appeared together in fine-tuning, even when each skill was demonstrated on its own. The authors identify a vision shortcut as one cause: the visual scene stands in for the instruction, so the policy repeats a combination it saw in similar scenes instead of following the instruction. CRAFT trains on counterfactual pairs that keep a demonstration's observation but change the instruction to an undemonstrated combination, and it supplies supervision through skill representations that carry over between executions of the same skill. Across three VLA models and two simulation benchmarks, CRAFT improves success on undemonstrated skill combinations while keeping success high on demonstrated ones, and it also helps on a real robot.

arXiv:2610.00524 PDF

When Reasoning Helps Action: Monitoring and Steering Chain-of-Thought in Vision-Language-Action Policies

Sathwik Karnik, Joseph JR. Lee, Aryaman Gupta, Somil Bansal cross-listed Vision-language-action (VLA) policies that produce chain-of-thought reasoning seem to offer a runtime safety interface, but it is unclear whether fixing the reasoning actually changes behavior. The authors propose TRUST, an offline-trained value model that predicts from partial reasoning whether the final reasoning will be correct, and use it to monitor and steer generation in frozen policies. On the Alpamayo 1.5 driving VLA, it raises reasoning correctness from 75.9% to 90.0% and reduces collision rate by 30.4% on a hard subset in AlpaSim. On the DeepThinkVLA manipulation policy, reasoning accuracy improves similarly but task success on LIBERO-Plus barely changes, showing that more correct reasoning does not automatically produce better actions.

arXiv:2610.00601 PDF

MIKASA-Robo-VLA: Benchmarking Memory in VLA Models for Long-Horizon Manipulation

Egor Cherepanov, Nikita Kachaev, Aleksandr I. Panov, Alexey K. Kovalev Most vision-language-action policies see only one or a few recent frames, which makes it hard to test whether they remember information that disappears during a task. MIKASA-Robo-VLA is a benchmark of 90 language-conditioned manipulation tasks; 80 hide the cue an action depends on, and 10 keep it visible as reactive controls. In 28 tasks the period when the cue is absent exceeds the 16-frame window of the widest fixed-context VLA the authors surveyed. The release includes 22,500 oracle trajectories in RLDS and LeRobotDataset v3 formats, and a fine-tuned π0.5 baseline without memory reaches only 0.211 mean task success.

arXiv:2610.00604 PDF

Kinematic MeanFlow: One-Step Action Generation Policy for Robotic Foundation Models

Jiawei Fan, Sifeng Wang, Yuqing Hou, Anbang Yao cross-listed Robotic foundation models (RFMs) that generate actions by multi-step flow matching have high inference latency, and applying the one-step MeanFlow method directly makes performance collapse. The authors trace the collapse to two properties of the RFM velocity field: local acceleration surges near the end of denoising, and its magnitude spreads more widely across samples as denoising progresses. Kinematic MeanFlow (K-MF) uses a kinematic identity to split MeanFlow's time-derivative term into two sub-intervals that capture early and late dynamics separately, which limits error amplification. It enables one-step action generation that outperforms multi-step flow matching in most settings and cuts GR00T-N1.6 action-head latency by 67.5–74.4% on L40 and Jetson Orin hardware.

arXiv:2610.00864 PDF

TOAST: Stochastic Robot Action Tokenization for Autoregressive Vision-Language-Action Models

Keisuke Shirai, Tomohiro Motoda, Hanbit Oh, Ryoichi Nakajo, Roman Mykhailyshyn, Ryo Hanai et al. cross-listed Autoregressive vision-language-action models often encode continuous robot actions with the FAST tokenizer, which assigns one deterministic token sequence to each action even though many token sequences decode to the same motion. TOAST samples alternative tokenizations of the same quantized action during training, which diversifies supervision without requiring additional demonstrations. On LIBERO it consistently beats the deterministic version, and the gap widens as data shrinks, reaching a 6.8-point success-rate gain with 1/16 of the training data. On four real-robot manipulation tasks it raises mean success by 15.8 points.

arXiv:2610.00899 PDF

Screw Attention: Rigid-Body Algebra Inside a Transformer

Aly Magassouba cross-listed Learned robot manipulation policies typically relearn from data the spatial relations that rigid-body mechanics already supplies in closed form, which costs data and makes them fragile to geometric changes. Screw Attention is a transformer layer in which every token is a body with a pose: messages are transported along relative poses and joint screws into the receiver's frame, while attention scores see only frame-invariant quantities, making the layer equivariant to per-token frame changes. With only 16,162 parameters it reaches 97.3% on LIBERO-Spatial from object poses alone, beating a flat network 27 times larger, and its success is unchanged under a change of per-link frame convention that drops every other learned network below 3%. Used as a gated residual on an analytic controller, it raises insertion success by 17.3 points and tolerates pose noise up to 10 mm.

arXiv:2610.00904 PDF

A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform

Chengkai Xu, Yiming Cui, Jiaqi Liu, Yicheng Guo, Cheng Qin, Geyuan Zhang et al. cross-listed End-to-end autonomous driving (E2E-AD) maps multimodal sensor inputs directly to driving actions with a single differentiable model, and its effectiveness depends heavily on its training ecosystem. This survey organizes that ecosystem under a Data-Strategy-Platform taxonomy: the data layer defines what can be learned, the strategy layer governs how learning aligns with driving objectives, and the platform layer supports scalability and continuous evolution. It reviews data pipelines, learning paradigms, and training infrastructure and how they interact to shape performance, robustness, and deployability. It closes by arguing for shifts from data quantity to data value, toward foundation-model generalization, and toward integrated training-testing loops.

arXiv:2610.00926 PDF

Divide-and-Remember: Recursive Action-Relevant Memory for Long-Horizon VLA Policies

Xuehui Yu, Eason Yu, Meiyi Wang, Haozhe Du, Stefano V. Albrecht, Harold Soh cross-listed Vision-language-action (VLA) models do poorly on manipulation tasks where the right action depends on history, and existing memory schemes choose what to keep with hand-designed rules. Starting from a POMDP formulation of imitation learning, the authors show that the optimal memory maximizes the conditional mutual information between the action and the memory given the current observation. Divide-and-Remember (D&R) learns this memory by recursively splitting selection over the full history into small top-K-of-2K subproblems, all handled by one shared lightweight selector, so it can cover unbounded histories cheaply. On the 16 long-horizon tasks of RoboMME, D&R reaches a state-of-the-art average success rate with a budget of only 64 memory tokens, and real-robot experiments show the same gain.

arXiv:2610.00982 PDF

MASkillBlender: Decentralized Whole-Body Coordination for Multi-Humanoid Loco-Manipulation via Skill Blending

Yifan Hu, Luhang Hong, Mingkang Long, Danning Wang, Chengfeng Jia, Rong Su et al. cross-listed Coordinating several humanoid robots for whole-body loco-manipulation is hard because of high-dimensional control, decentralized decisions, and the reward engineering it usually requires. MASkillBlender is a multi-agent reinforcement learning framework that learns one shared, decentralized high-level policy that blends reusable, pre-trained single-humanoid skills, using only task-level rewards and no task-specific motion references. A permutation-based data augmentation for teams of identical humanoids improves learning efficiency, and the authors prove that it preserves the policy-gradient direction. In simulation, the framework performs strongly across several coordination tasks and two humanoid embodiments.

arXiv:2610.01102 PDF

PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots

Amr Mousa, Rifny Rachman, Neil Karavis, Michele Caprio, Richard Allmendinger cross-listed Standard reinforcement learning (RL) for legged robots fixes the trade-off between command tracking, stability and energy efficiency into a single reward at training time. PROMO instead conditions one locomotion policy on operator preferences given at runtime, while keeping the reward terms that any viable gait needs fixed. In simulation, 67 of 100 sampled preferences yield non-dominated (Pareto-optimal) behaviors, and the policy transfers zero-shot to a Unitree Go2. On the real robot, changing preferences alone cuts specific energy by up to 30.4% and position error by 38.7% relative to the balanced setting.

arXiv:2610.01260 PDF

Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrations

Giulio Schiavi, Andrei Cramariuc, Michael Pantic, Roland Siegwart cross-listed Most grasp synthesis systems are trained offline and stay fixed during deployment, so they can struggle with unfamiliar objects. The authors present a continual-learning framework for single-view 6-degree-of-freedom (6-DoF) grasping in cluttered scenes that adapts through a memory in a learned embedding space instead of fine-tuning a large model. Outcomes of past grasps adjust future grasp scores, and optional user demonstrations are recalled as extra candidate grasps. Across more than 1,500 real-world trials, it matches existing baselines before any adaptation and exceeds 90% success on several challenging object categories after only 50 online grasp attempts, with limited forgetting over long horizons.

arXiv:2610.01301 PDF

Completion Aware Guidance for World Action Models

Seungyeon Kim, Junhoo Lee, Baekseung Kim, Minkyu Kim, Nojun Kwak cross-listed World Action Models (WAMs) predict both future video frames and robot actions, but they often imagine plausible continuations that skip the step needed to finish the task. The authors show that this failure comes from adapting the model for short-chunk control rather than from the world-model backbone. Their fix, Completion Aware Guidance (CAG), is a training-free sampling method that steers generation toward task completion. It raises success from 64% to 70% on a RoboTwin 2.0 subset and from 69% to 75% in zero-shot simulation, and cuts task-incomplete imagination from 79% to 40%.

arXiv:2610.01559 PDF

Measuring the Stability Assumption Behind Action Chunking

Aryan Goyal Action chunking improves behavioral-cloning policies, and one common explanation is that it reduces compounding errors. To test this, the authors inject small action errors and measure whether they grow or shrink under open-loop chunk replay and under closed-loop replanning, across twelve manipulation tasks from three benchmark suites. Confidently stable states are rare and error amplification is common, amplification is concentrated in the first steps after the perturbation, and replanning seldom turns amplification into contraction. The authors conclude that closed-loop recovery should be trained explicitly, for example with perturbation-oriented data, instead of expected to emerge from standard imitation learning.

arXiv:2610.01626 PDF

Training-Free Diffusion Planning with Analytical Local Scores

Michael Y. Fatemi, Jinhao Liang, Ferdinando Fioretto cross-listed Diffusion-based motion planners need large sets of feasible demonstration trajectories, which ties them to specific maps. This work replaces the learned trajectory score with analytical local scores built from obstacle, smoothness, velocity, and inter-agent feasibility terms. It relies on the observation that a trajectory's score can be reconstructed from interactions between neighboring waypoints and nearby constraints alone. The resulting training-free planner generates feasible paths for more than 300 agents among 100+ obstacles in under 6 seconds on a GPU, and it outperforms both learned and optimization-based baselines.

arXiv:2610.01959 PDF

HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution

Kyochul Jang, Seohyeon Park, Ohchul Kwon, Sangjun Park, Junhyeok Choi, Seungyeop Yi et al. cross-listed No existing benchmark jointly tests whether a humanoid robot can pick a suitable tool and then coordinate manipulation and locomotion to use it. HumanoidToolBench provides 18 tasks across three scenarios, three execution levels and two tool-set modes, along with ToolBook, a dataset of 3.1k demonstrations collected in simulation and on a real Unitree G1. Evaluating seven policies in simulation and three on the real robot reveals substantial gaps between selecting the right tool and completing the task. Probes of GR00T N1.7 show lower selection accuracy on unseen tools and that the policy keeps executing the task even when given unrelated instructions.

arXiv:2610.02089 PDF

DuoMind: Enabling Distributed Multi-Robot Coordination with Semantic Communication

Hanchu Zhou, Dechen Gao, Hang Wang, Brendan Lynch, Boqi Zhao, Qiyao Ma et al. cross-listed Most progress with vision-language models (VLMs) and vision-language-action models (VLAs) in robotics targets a single robot. DuoMind extends these models to distributed multi-robot coordination. On each robot, a VLM-based orchestrator reasons over the task, its local observations and messages from peer robots. It then issues low-level instructions to a VLA action model and sends semantic messages to the other robots. The authors also introduce RoboPoly, a benchmark of long-horizon manipulation tasks that require coordinated closed-loop control. Experiments on RoboPoly and RoboTwin show improved multi-robot task performance, and ablations confirm that both the hierarchical orchestration and the semantic communication contribute.

arXiv:2610.02161 PDF

Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents

Yen-Jen Wang, Haozhe Jiang, Shuying Deng, Haoru Xue, Weirui Ye, Rocky Duan et al. cross-listed RPG (Reconstruct, Practice, Go Real) improves a robot execution system on its own, without updating any model weights. It identifies manipulation capabilities in an offline dataset and builds related practice tasks in simulation. It then diagnoses failures from execution feedback, privileged simulator state, and dataset videos, and in response writes new symbolic skills, refines existing ones, and revises the system prompt. Candidate changes are tested across tasks before they are kept, and at test time a multimodal LLM coordinates perception and control using the resulting prompt and skill library. Across 22 held-out manipulation tasks, success rises from 28.6% to 95.0% over 15 practice rounds, beating ASPIRE (75.5%) and CaP-Agent0 (60.0%), and the frozen system succeeds in all 30 physical trials.

arXiv:2610.02204 PDF

9 more specialized papers

Reasoning 22

What Do Rationales Communicate? A Message-Intervention Study in Role-Specialized QA

Jiameng Zhang, Hongqiu Wu Some multi-step QA pipelines pass a rationale from a reasoner model to a verifier model, and the authors ask what that message actually contributes. Their diagnostic holds the evidence and candidate answer fixed and varies only the rationale, on 400 examples from MuSiQue, HotpotQA, and 2WikiMultiHopQA with DeepSeek as both generator and verifier. Faithful rationales add almost no answer accuracy, but corrupted rationales shift the verifier's support judgments by 10-22%, and by 34-55% when the verifier is explicitly told to check the rationale. Human audits find many cases where the model accepts corrupted rationales that people reject. The authors argue that rationale sharing should be evaluated as a verification channel and a possible failure surface.

arXiv:2610.00018 PDF

Certainty Is Not Just Correctness: Rethinking Token-Level Certainty in LLM Reasoning

Yunfan Zhou, Ye Zhu, Zhihai Wang, Jianguo Yao, Haibing Guan, Xijun Li Token-level certainty is widely used as a stand-in for correctness in LLM reasoning. Controlled evaluations here show it is much better at identifying which questions a model will likely answer correctly than at telling correct from incorrect responses to the same question. Certainty also varies systematically with token type and position within a word. Signal about question difficulty appears early in generation, while the weaker signal about answer correctness concentrates near the end. Using early certainty to decide how many responses to sample and late certainty to weight votes raised accuracy from 78.71% to 79.54% over fixed-sample majority voting while cutting generated tokens by 82.4%.

arXiv:2610.00296 PDF

Mathematical Transfer in LLMs Follows Reasoning Approach More Than Topic

Sajad Goudarzi, Samaneh Zamanifard, Seyed Amin Seyed Haeri, Moloud Nasiri, Hamed Rahimian When choosing math fine-tuning data for LLMs, should training examples match the target's topic or its solution method? Two counterbalanced 2x2 designs answer this by crossing topics (for example, probability and combinatorics) with reasoning approaches (for example, invariants and double counting) and holding out each cell in turn as the target. Across five base models and three seeds, same-approach training data beats same-topic data in all 40 model-target comparisons, by 10.8 and 14.3 percentage points on average in the two designs. This holds even though same-topic sources look more similar to the targets under both embedding and lexical measures.

arXiv:2610.00331 PDF

The Weakest Link: Distilling LLM Reasoning with Worst-Case Constrained Reinforcement Learning

Matthieu Zimmer, Xiaotong Ji, Tu Nguyen, Haitham Bou-Ammar Distilling LLM reasoning into smaller students with pure task rewards (for example GRPO) invites reward hacking through flawed intermediate steps. KL-based teacher regularization instead averages over the trajectory, so a student can offset one severe logical error with high teacher agreement elsewhere. The authors frame distillation as constrained reinforcement learning: maximize task reward subject to a worst-case bound on teacher log-likelihood at every prefix of the chain of thought. They derive an unaugmented constrained MDP whose reward transformation keeps the hard-constraint semantics without dual solvers or test-time teacher access. On math reasoning and code generation, the method matches the final-answer accuracy of pure RL while sharply reducing constraint violations, and it reaches the highest Reasoning Success Rate in every evaluated setting.

arXiv:2610.00332 PDF

Random Recursive Models

Jama Hussein Mohamud, Mirco Ravanelli Recursive models gain depth by reusing parameters, but they usually apply layers in a fixed order. The Random Recursive Model (RRM) keeps a pool of learned layers and, for each example and recursive step, samples one layer at random with replacement. On challenging reasoning tasks, it matches or beats baselines often with 50–75% fewer parameters. It can also run at greater depth during inference than it saw in training, and it supports Monte Carlo inference and probabilistic test-time scaling, all without retraining.

arXiv:2610.00541 PDF

Interpreting Reasoning of Large Language Models via Partial Information Decomposition

Barproda Halder, Qiuyi Zhang, Sanghamitra Dutta cross-listed Large reasoning models often produce long, repetitive reasoning traces. SLIDER is an interpretability framework that uses Partial Information Decomposition to split the answer-relevant information in consecutive reasoning steps into unique, redundant, and synergistic parts. From this it defines the Step-wise Repetitive Reasoning Index (Step-RRI), which improves step-level redundancy detection on PRMBench by over 10 points compared with embedding-similarity and information-gain baselines. A trajectory-level version correlates strongly with reasoning length across QwQ-32B, DeepSeek-R1-Distill-Qwen-32B, and GPT-4.1, and using it to select fine-tuning data makes the fine-tuned model reason more efficiently while largely keeping its accuracy.

arXiv:2610.00571 PDF

Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI

Nishtha N. Vaidya, Stephan Grimm, Thomas Hubauer, Thomas A. Runkler The authors present a pipeline that automatically builds multiple-choice question (MCQ) benchmarks from any sufficiently axiomatised OWL 2 ontology, aimed at testing large language model (LLM) logical reasoning in scientific domains. Correct answers come straight from the ontology. Distractors are made by perturbing class definition axioms, and an OWL reasoner formally verifies through entailment checks that each one is wrong. Applied to the Pizza, PMDco, and DOID ontologies, the pipeline produced 112, 2,491, and 15,216 questions, whose distractors fall into four diagnostic categories of reasoning failure. Six LLMs evaluated zero-shot score 41.1-76.8% accuracy, against a 25% random baseline.

arXiv:2610.00682 PDF

Reason in Style: Discovering and Controlling Style in Language Models

Ioana Marinescu, Eric Karl Oermann, Kyunghyun Cho Language models learn content and style together, so the stylistic variation in their outputs is hard to identify or control. The authors build an unsupervised algorithm that separates content representations from style representations. Applied to more than 100K verified math reasoning traces from nine teacher models, it finds six recurring but imbalanced styles. Smaller student models are then fine-tuned to follow a requested style, with importance weighting to balance the styles, and this improves Pass@k over standard fine-tuning on six math reasoning benchmarks. The chance of solving a given problem depends on which style is requested, and different problems benefit from different styles.

arXiv:2610.00724 PDF

Adapter Thickets: Splitting an RLVR Budget Beats Concentrating It

Jonathan Williams, Esin Tureci Karthik R. Narasimhan The usual recipe of training one policy with reinforcement learning with verifiable rewards (RLVR) and then majority-voting over many samples loses accuracy, because RLVR makes a policy's samples repeat the same mistakes. With 160 completions per problem on models from 1.5B to 8B parameters, a single LoRA adapter trained on the full budget improves single-sample accuracy but leaves the majority vote below the untrained base model on three of four models. Splitting the same data and compute across K adapters, each trained on its own disjoint shard, gives an adapter thicket that out-votes the fully trained adapter in all 16 settings tested. The thicket's lead also grows as the number of votes increases.

arXiv:2610.00991 PDF

From Discovery to Decision: Finite-Budget Recoverability in LLM Voting

Shaoang Li, Jian Li When final answers are chosen by majority vote over several large language model (LLM) responses, sampling a correct answer does not guarantee it will win: under a fixed call budget, it must still gather enough votes in the remaining calls. The authors formalize this gap between discovering an answer and selecting it. They derive a sharp threshold for when an answer can still win given the current vote counts and remaining budget, and they prove that merging distinct wrong answers into one cannot improve plurality accuracy. From this they build a ground-truth-free "locking" certificate that stops sampling once the final answer can no longer change, which saves 28–30% of calls at a 16-call budget without changing any output. In a controlled Word16 study, randomly permuting the input across calls raises plurality accuracy by 21.1 points while single-call accuracy stays about the same.

arXiv:2610.01014 PDF

Improving Math Reasoning through Value-guided Informative Search

Shaohuai Liu, Yuning Wu, Haoran Liu, Enzo Jia, Devin Chen, Kai Wei Adding search to reinforcement learning with verifiable rewards (RLVR) makes rollouts more diverse, but diversity alone does not guarantee that the searched rollouts improve on the current policy. APIVIS adapts finite-budget Gumbel search to chunk-level mathematical reasoning and mixes direct and searched responses in each rollout group, so improvements found by search produce informative relative rewards. It also supervises search-improved tokens directly, which keeps a learning signal when uniform group rewards would make GRPO ineffective. The authors prove that exact value-guided selection improves expected verifier reward, with an approximate guarantee under bounded value-estimation error, and report substantial gains over search-based baselines on standard math benchmarks across several model scales.

arXiv:2610.01080 PDF

Does Scaling Reinforcement Learning Really Require More Training?

Bangji Yang, Jiajun Fan, Hongba Ma, Ruihan Guo, Ge Liu The authors argue that a finished reinforcement learning (RL) run already contains policies stronger than any checkpoint its optimizer visited, a direction they call policy-space scaling. SURGE merges two checkpoints from the same run, a high-accuracy anchor and a donor that writes shorter responses, by spectrally decomposing the anchor's weight update relative to the shared initialization and adding the donor's complementary component, without any training or candidate evaluation. Across DeepSeek and Nemotron 1.5B math-reasoning runs and an OLMo 7B coding run, the merged model beats both input checkpoints while using fewer reasoning tokens than the anchor, reaching 54.17% on AIME24 versus a native maximum of 50.83% and 83.7% versus 82.8% on HumanEval+.

arXiv:2610.01133 PDF

ReSolve: Reusing Candidate Reasoning through Selective Generative Moderation

Bangji Yang, Jiajun Fan, Hongba Ma, Xi Zhu, Weizhi Zhang, Minghao Guo et al. When a model samples multiple solutions, the intermediate reasoning in those candidates is usually thrown away after voting. ReSolve is a training-free inference procedure that asks a model to examine the existing derivations only when candidates disagree or have no parseable answer, then folds the new solution into a bounded loop. On 130 competition-math problems, it gets 100 and 99 correct answers across two candidate pools, versus 91 and 92 for four-candidate voting, and it beats eight-sample self-consistency (94 and 96 correct) while using about 46–47% fewer tokens. An ablation that hides the derivations drops accuracy from 100 to 93, which supports the claim that the reused reasoning, and not just the answer tallies, drives the gains.

arXiv:2610.01140 PDF

Function-Structured Reinforcement Learning with Executable Verifiers for Mathematical Reasoning

Zihan Liu, Xurong Xie Rewards based only on the final answer give little signal about where a multi-step math solution went wrong, and code that runs is not necessarily mathematically correct. FSG-RL (Function-Structured Graph Reinforcement Learning) breaks each problem into a graph of subproblems and trains a policy to turn that graph into Python. Training starts with supervised fine-tuning, then uses GRPO (Group Relative Policy Optimization) with rewards that depend on a correct answer, feedback from several verifiers, and credit assignment over individual spans of the output. On a benchmark built from GSM8K, MathQA, MATH, and Omni-MATH, reinforcement learning raises final-answer accuracy from 43.25% to 67.50% and full-solution success from 32.25% to 52.25% compared with supervised fine-tuning alone, and further RL with teacher supervision adds more.

arXiv:2610.01729 PDF

The Missing Primitive: Diagnosing and Repairing Mathematical Reasoning in Large Language Models

Shuo Xing, Zilin Dai, Chengyuan Qian, Fangzhou Lin, Wenjing Chen, Ping He et al. The work asks whether large language models (LLMs) that solve hard math problems actually understand the structure of their solutions. It introduces the notion of a Mathematical Primitive and a benchmark that tests reasoning along four dimensions: Discovery, Generation, Digestion, and Execution. The diagnosis shows that overall accuracy hides distinct capability profiles, that supplying primitives unlocks substantial latent execution ability, and that Discovery is the dominant bottleneck. Building on this, the authors propose a primitive-privileged self-distillation framework that transfers primitive-guided reasoning into a student model, and report consistent gains across model scales and challenging benchmarks.

arXiv:2610.02191 PDF

7 more specialized papers