Monday, October 5, 2026
Highlights
EditHero: A Benchmark for Long-Horizon Part-Level 3D Editing and Vibe Modeling
3D editing methods are usually evaluated on a single edit, but real assets go through long sequences of revisions, each of which must change only what was requested. EditHero is a benchmark for long-horizon, part-level 3D editing with natural-language instructions and exact target geometry and texture produced by a deterministic assembly engine. It compares non-agentic methods that regenerate the whole object against LLM and vision-language model (VLM) agents that edit the mesh through code. The agents follow instructions more closely and all preserve unedited parts better, but each of their edits takes minutes, while non-agentic methods often miss the change and disturb regions that should stay fixed.
Existing 3D editing methods are scored on one edit from a clean asset, but artists and "vibe modeling" workflows apply many edits in a row, each of which must preserve earlier work. EditHero tests this with chains of part-level edits (add, remove, replace, retexture) whose exact ground truth after every turn comes from an operation log that can be replayed.
- A data engine attaches caption-matched library parts from
PartVerse-XL,Objaverse-XLandHY3D-Benchto segmented host objects, checks contact, scale and interpenetration, and logs each operation; after human review the benchmark has 457 chains, 2,755 edits and 252 hosts, with up to 30 turns per chain (median 6). - Methods are evaluated under self-rollout, where each turn edits the method's own previous output, using region-restricted metrics: instruction following (IF) inside the edit region and content consistency (CC) outside it, designed so that a do-nothing baseline scores IF = 0 and cannot win.
- The four non-agentic latent-regeneration methods (
PartFlow,Nano3D,3DEditFormer,VoxHammer) reach only IF 0.12–0.32, lose to the no-op baseline on whole-object F-score, LPIPS and PSNR, and see retention of the original object fall from 0.60–0.78 to 0.33–0.67 by turn 7 as errors accumulate. - On a 55-chain subset, code-writing LLM agents edit only the named part and preserve the rest far better (CC 0.95–0.98 vs at most 0.90), and five of six follow instructions better, led by
Opus 5.5at IF 0.62 against at most 0.36 for non-agentic methods. - Agents still struggle to build new geometry from primitives (IF 0.10–0.49 for additions and 0.17–0.52 for replacements, versus 0.88–0.99 for removals), and they take 1.5–6 minutes per edit compared with 20–50 seconds for non-agentic methods, which is too slow for interactive use.
SimuVerity: Benchmarking Agents for Engineering-Grade Simulink Model Generation
Existing Simulink benchmarks check whether generated models compile or resemble a reference, not whether they meet engineering requirements. SimuVerity provides 101 text-to-executable Simulink tasks across ten engineering domains. A hierarchical evaluator first checks that a model is delivered, runs, and qualifies, then scores qualified models on six dimensions including accuracy, mechanistic fidelity, robustness, and dynamic response. Of six agent systems evaluated, the best scores only 42.86 overall, and the results show that structural similarity to a reference is a poor proxy for engineering performance.
Benchmarks for LLM-generated Simulink models mostly check whether a model compiles, runs, or looks like a reference, and none of that shows the model actually meets its engineering requirements. SimuVerity instead tests agent-built .slx models against 101 requirement-grounded tasks in ten engineering domains, using native simulation scenarios and a hierarchical, multidimensional scorer.
- For each task, domain experts build an executable-system profile from a reference model and use it to write the spec and design 595 native simulation scenarios of four kinds: operating envelope, interaction and fault, temporal process, and mechanism or causal checks.
- A candidate must pass three gates in order (artifact delivered, native execution, and engineering qualification) before it is scored on six dimensions: accuracy, output quality, mechanistic fidelity, control and causal integrity, robustness, and dynamic response. These scores are combined with an expert-weighted geometric mean that agreed closely with expert ratings (Spearman ρ = 0.94).
- The best systems score only 42.86 (
Claude Opus 4.8+Claude Code) and 41.72 (GPT-5.5+Codex), and the difference between them is not statistically significant; small open models nearly collapse, withQwen3.8-27B-FP8at 1.60. Many delivered models still fail qualification: Opus delivers 94.72% of models, but only 76.90% pass the qualification gate. - Resemblance to the reference is a poor guide to quality: among Opus's own models, structurally distinct designs average 61.20 once they pass qualification, while seven reference-aligned models score zero. Also, 8 of Opus's 31 models scoring at least 70 had severely disordered diagram layouts.
- Tool access matters a great deal: on ten tasks Opus normally solves, removing simulation feedback drops its score from 78.46 to 7.88. The ablation is small, though, and the benchmark depends on MATLAB/Simulink, with slow evaluation.
The Surprising Effectiveness of Shared Memory in Looped Transformers
Looped Transformers reuse the same layers several times per token to add compute without adding parameters, but each recursion writes its own key-value cache, so memory still grows with compute. The authors pretrain looped models in which only the first recursion writes a cache, and later recursions read that shared cache while keeping a short local window of their own. At 150M–1B parameters, sharing memory improves quality rather than hurting it: with five recursions, the hybrid Looped Prediction Transformer (LPT) lowers FineWeb-Edu validation perplexity by 1.12–1.82 compared with a same-size standard Transformer while using 76–79% less context memory. Analysis shows that shared and local memory learn different representations, and that the shared cache also acts as a gradient highway back to the first recursion.
Looped Transformers get extra compute without extra parameters by reusing the same layers several times per token, but each pass writes its own key-value cache, so memory still grows with recursion depth. The authors pretrain looped language models in which only the first pass writes a full-context cache and later passes read it while keeping only a short local window, and find that this shared memory improves quality while also cutting memory.
- In
LPT, the first "state" recursion writes the only full-context KV cache, and each later "prediction" recursion attends to that cache plus its own 64-token sliding window in a single softmax;H-LPTapplies the same rule to the full-attention layers of the Gated DeltaNet hybridLT2, with models trained from scratch at 150M–1B parameters on 20–50BFineWeb-Edutokens. - Against a matched-depth looped baseline (
LT),LPTlowers validation perplexity by 0.38–1.09 and raises mean five-task accuracy by up to 4.8 points, using 2.0–4.7× less KV memory and under 1% more FLOPs;LTstops improving after three recursions, whileLPTkeeps improving through five. - At five recursions,
H-LPTbeats a same-size standard Transformer by 1.12–1.82 perplexity points and 3.1–5.6 accuracy points while using 76–79% less context memory, so it fits 3.6–5.0× as many sequences on a single GPU. - Analysis shows that later recursions put 57–80% of their attention on the shared memory, that shared and local keys form distinct representations, and that the shared cache acts as a gradient highway to the first recursion; blocking those gradients raises perplexity and often causes deeper models to fail to train.
- The approach is not compute-optimal: at five recursions it costs 4.3–4.8× the decoding FLOPs of a standard Transformer, with no FLOP-matched baseline, and evaluation is limited to ≤1B parameters, one corpus, and 4,096-token contexts with no long-context tasks; the main
LTbaseline also uses a normalization that destabilizes it, althoughLTwith the best of five alternative normalizations still trailsLPTby 0.06–0.43 perplexity.
The AI Theorist reveals excitonic structure in $\alpha$-RuCl$_3$
AI Theorist is a system of AI agents that builds physical models on its own by generating hypotheses, running first-principles calculations, and refining its ideas against evidence. The authors apply it to optical and photocurrent data from α-RuCl3, a leading candidate material for a Kitaev quantum spin liquid. The system arrives at a new interpretation that identifies distinct excitonic states with contrasting optical selection rules and different real-space distributions. The authors describe it as the first AI system to autonomously develop a physical model explaining previously unpublished experimental observations in a quantum material.
Turning experimental spectra into a microscopic physical model usually takes a human theorist. AI Theorist hands that job to a team of LLM agents that propose hypotheses, run first-principles DFT–GW–BSE calculations and revise their explanation against the evidence, and it is tested here on previously unpublished reflection and photocurrent spectra of the Kitaev spin-liquid candidate α-RuCl₃.
- Separate agents handle hypothesis, calculation design, execution, critique and synthesis;
Claude Opus 4.6runs most roles, aQwen3.5-27Btrained withSFTandGRPOdesigns the calculations, and 18 tools give the agents literature search,Quantum ESPRESSO,BerkeleyGWandGP-UCBparameter search. - The agents rejected a local-correlation model and a constrained electronic starting state before settling on a third route, in which updating the screening raised the calculated gap from about 1.33 to 1.80 eV and showed that spin–orbit coupling plus electron–hole interactions are needed to produce low-energy optical weight.
- The model assigns bound excitons at 1.077, 1.179 and 1.291 eV to the observed
L,αandα′features, all well below the roughly 1.8 eV quasiparticle gap and spatially compact, unlike an extended 1.964 eV state. αcouples almost only to zigzag-polarized light (armchair-to-zigzag ratio about 9×10⁻⁵), whileα′favors armchair (ratio about 2.9) with only about 5% ofα's optical strength; this yields a testable prediction that rotating the polarization from zigzag to armchair should enhanceα′, and the full run used 154M tokens, about $335 of which was theClaudeshare.- The limitations are substantial: the mean-field and screening calculations are not shown to be converged, the comparison with and without spin–orbit coupling uses mismatched grids and pseudopotentials, the explanation for why photocurrent picks out
α′is qualitative, only one independent human theorist checked the work, and the code and data have not yet been released.
Large Language Continuous Diffusion Models
Discrete diffusion language models decode in parallel, but their non-smooth, high-dimensional space makes it hard to steer generation trajectories. Sigma is a large-scale (3B and 8B) continuous diffusion language model that denoises Gaussian-corrupted token embeddings blockwise along low-dimensional trajectories governed by ordinary or stochastic differential equations, while also learning the embedding geometry. It is warm-started from autoregressive model weights. With classifier-free guidance and score temperature, it matches discrete diffusion and autoregressive baselines on GSM8K, Minerva, HumanEval, and MBPP, and on MATH-500 and AIME after fine-tuning. Steering in embedding space controls the quality-diversity tradeoff for strong pass@k, and quality degrades gracefully with fewer function evaluations, which also enables efficient distillation.
Discrete diffusion language models decode in parallel, but their categorical state space, with over 100K vocabulary entries, is hard to steer and degrades when the step budget is small. Sigma scales continuous Gaussian diffusion over a learned 16-dimensional token-embedding space to 3B and 8B parameters, and shows it can match masked diffusion models on reasoning and coding while gaining steerable, few-step trajectories.
- The model generates text block by block over a key-value-cached clean prefix; within each block it jointly denoises Gaussian-corrupted unit-sphere embeddings and learns the embedding geometry and noise schedule through a blockwise ELBO, warm-started from an autoregressive checkpoint with autoregressive regularization, for over 300B tokens of training.
- At inference, embedding-space classifier-free guidance (packed into one forward pass at under 5% overhead, with no train-time context dropout) is combined with score temperature, and the pair works clearly better than either knob alone.
- Base
Sigma-8Bwith 256DDPMsteps per block averages 69.67 across six math and coding benchmarks versus 70.22 forNemotron-Labs-Diffusion-8B, including 89.07 onGSM8K; at 3B it beatsLLaDA-8BandDream-7Bby 22.6 and 5.6 points on average despite being about half their size. - Guidance and temperature trade quality for diversity, letting
Sigma-3Breach 91.8% pass@256 onMBPPversus 87.6% forNemotron-Labs-Diffusion;DDIMsampling degrades gracefully at 16–32 steps, andSUSRegembedding regularization plus parallel decoding distillation make 4–8-step generation workable. - Limitations: after supervised fine-tuning,
Sigma-8B-Instructslightly trailsNemotron-Labs-Diffusionoverall (59.27 vs 59.92) and onAIME(16.33 vs 23.33 on AIME25), quality still drops off below 16 steps without distillation, and learned embeddings collapse to roughly 4 effective dimensions unless regularized.
MetaRubric: Learning to Reward for Rubric-Based Reinforcement Learning
In rubric-based reinforcement learning, judges can award credit for a criterion even when the response lacks the required information. The authors call this failure Vacuous Credit and show it can flip the sign of a response's GRPO advantage. MetaRubric alternates two steps. In policy optimization, it grants credit only when the response contains sufficient evidence, checked against counterfactual prompts in which one task-relevant fact has been changed. In rubric adaptation, it revises criteria and their weights based on current policy responses. Across multiple backbones, MetaRubric improves PubMedQA accuracy by 6.00–20.40 percentage points over static-judge GRPO, with further gains on HealthBench-Hard and two multimodal medical benchmarks.
Rubric judges in rubric-based RL often give credit for a criterion even when the response leaves out the required information or action, which the authors call "Vacuous Credit." This can flip the sign of a response's GRPO advantage. MetaRubric fixes it with two alternating loops: an inner loop of evidence-aware GRPO training on paired original and counterfactual prompts, and an outer loop that reweights criteria and revises their wording based on the errors the policy keeps making.
- In a deletion test on 1,000 credited rollouts,
GPT-4o-miniandGPT-5.4-minijudges kept 82.0% and 69.8% of criterion awards after the required content was removed, and training reward kept rising while satisfaction measured by a stronger judge panel fell. - Each criterion's score is the minimum of three judge scores (satisfaction, coverage and evidential support), with a response-level support cap and a quality factor on top, plus an auxiliary reward in which a
Qwen3-1.7Breader answers fixed multiple-choice questions using only the response. - At each stage boundary, criterion-group weights shift toward groups that fail more often while keeping the severity ordering, and wording revisions are accepted only if they raise agreement with a reference panel and pass a semantic check against the original rubric.
- Compared with static-judge
GRPO, accuracy onPubMedQArises by 6.00–20.40 points and scores rise by 2.46–3.19 onHealthBench-Hard, 1.29–2.13 onMMOral-Xand 3.09–3.82 onMMOral-OPG, for a best result among training methods in 19 of 21 comparisons (Dr. GRPOwins the other two narrowly). - Retraining from scratch with only the final learned rubric recovers just 5.0% and 20.7% of the gains, which suggests the order of rubric updates matters; limitations are that every result comes from a single seed, all tasks are medical, and the method was not compared directly with other rubric-adaptation methods.
Scaling Trajectories for Complex Tasks through Recursive Self-Rewrite
Successful agent runs on hard tasks make good training data, but they often rely on specialized harnesses whose interventions won't exist at deployment. Recursive Self-Rewrite (RSR) uses a single model, Qwen-3.8-27B, to solve tasks under several harnesses and then rewrite those solutions as trajectories under a general harness. A planner distills each solution into a runbook, a critic screens for leaked verifier details or answers, and an executor replays the runbook in a fresh sandbox, turning 2,001 source trajectories into 11,094 for supervised fine-tuning. The fine-tuned model beats both the base model and direct trajectory fine-tuning, raising pass@3 on Terminal-Bench 2 from 57.0% to 74.2%, with gains on harder terminal benchmarks as well.
Training an agent directly on successful runs collected under specialized harnesses (the scaffolding that controls how a model acts) also teaches it controller interventions and workflow conventions that a general harness won't provide. RSR addresses this by having one model, Qwen-3.8-27B, solve tasks under several harnesses and then rewrite those successes as clean demonstrations under the general Terminus 2 harness for fine-tuning.
- In the discovery stage,
Terminus 2,StateM, and a reflect-and-retry harness (RSRT) were run across about 3K self-curated terminal tasks; together they solved 759 tasks, 34.3% more than the strongest single harness, and 288 of those were solved by only one harness. - The same model then plays three roles in the rewrite stage: a planner turns each source trajectory into a runbook of key steps, checks, and recovery tactics, a critic rejects runbooks that leak verifier details or answers, and an executor follows the runbook privately in a fresh sandbox, keeping only verified passes.
- Rewriting grew the training set from 2,001 to 11,094 trajectories, with an 86% pass rate on rewrites, and the fine-tuned model raised pass@3 on
Terminal-Bench 2from 57.0% to 74.2%, onTerminal-Bench 4from 1.5% to 9.1%, and on the self-curatedTerminal-Bench Hardfrom 39.0% to 63.0%. RSRbeat direct fine-tuning on the raw source trajectories on all five pass@3 benchmarks (+20.8 points onTB2), while direct fine-tuning actually droppedTB2below the base model, which the authors attribute to looping, dead-end behavior absorbed from very long source runs.- Limitations: absolute scores on the hardest sets stay low (6.0% on
SWR100, and zero of 46Long-Horizon Terminal-Benchtasks completed despite process reward rising from 0.21 to 0.29), several benchmarks are self-curated, only one base model is tested, and no baseline with a matched amount of training data is reported.
Source Preference in the Wild: How LLM Agents Favor Items by Source, and How to Reduce It
When LLM agents choose products, hotels or papers for users, they may favor items based on which site or service the items come from. Across end-to-end search with 12 agent models in three domains, every model prefers some sources and avoids others, and the models largely agree on which ones. This preference can outweigh request fit: an item meeting one fewer requirement is chosen about two-thirds of the time when it comes from a preferred source, but almost never in the reverse case. Hiding source information weakens the effect and relabeling an item with a preferred source raises its selection rate, while supplying missing item details or adding a prompt that counters preconceptions about sources reduces the bias.
LLM agents that search and pick products, hotels or papers for users tend to favor items from certain sources, even when competing items meet the request equally well. Across 12 agent models, three domains (shopping, accommodation and scholarly search) and 4,822 requests, the authors measure this bias under controlled conditions, show that the source label itself drives it, and trace it to training shortcuts and to gaps in the item information.
- How the bias is measured: items from different sources that meet the same requirements are paired, each result list is shown in every cyclic rotation so position is held fixed, and a
Bradley–Terrymodel (with ties) gives each source a preference score; 110 of 144 model–source combinations come out significantly preferred or dispreferred, with median scores of +15 and −18 percentage points, and models largely agree (for example, ten models preferBooking.comwhile half avoidExpedia). - Preference can beat item quality: when the item meeting one fewer requirement comes from a preferred source and the better item from a dispreferred one, agents pick the worse item 68% of the time (median), compared with 2% in the reverse case and 19% for pairs of neutral sources.
- The source label itself matters: hiding URLs and source names shrinks the gap between preferred and dispreferred sources by about 5.8–5.9 points in each direction, mostly because of the URL, and swapping the source labels on identical content raises selection for preferred-source labels in every model–domain cell, by up to +64.7 points (
Llama-4-Maverick, shopping). - Where it comes from and how to reduce it:
DPOtraining that pairs an invented source with the better product 80% of the time teaches models to pick that source 70–75% of the time when items are equally good, and balanced training brings an existingAmazonpreference (53.7–62.7%) back to about 50%; at inference, supplying a missing price cuts the preferred-source rate by 2.8–28.3 points. - Limitations: a prompt saying the source is unrelated to price does almost nothing, while a prompt naming the dispreferred retailer as the cheap one cuts preference by 7.4–22.5 points, but that only counteracts the bias, requires knowing which source is dispreferred, and was tested only on shopping prices; the training experiments used only 8–9B models.
HyperBrowseComp: A Multilingual and Multimodal Stress Test for Web-Browsing Agents
HyperBrowseComp is a web-browsing agent benchmark of 423 hand-written, human-validated questions in 13 languages, authored by native or highly proficient speakers. Answering a question requires finding obscure evidence, following multi-step chains of clues, or inspecting videos, scanned documents, images or maps. Questions that models could answer without internet access were filtered out. Models are evaluated both with their providers' built-in search and with a shared external retrieval harness under one agent protocol, and a human evaluation on a sample of questions provides an effort baseline. The intended difficulty comes from discovering and connecting evidence across languages and modalities on the open web.
Existing web-browsing benchmarks are mostly English-only and text-only, and their trivia-style questions are increasingly answerable from model memory. HyperBrowseComp instead offers 423 questions written natively by speakers of 13 languages, each with one short answer that an agent can only find by tracing multi-step clues through obscure open-web evidence such as videos, scanned documents, images and maps.
- The questions were not translated from English, and 64.3% need at least one non-text source, most often video (39.0%), PDFs, books or scanned text (29.8%), and images (18.4%); 77 candidate questions were dropped because models without internet access could answer them.
- The best setup,
Gemini 3.7 Flashwith its provider's built-in search, scores only 31.68%, and 57.68% of questions defeat all five models tested with native search. - Results depend heavily on the search tooling: switching to the
Exasearch API raisesGPT-5.6 Solfrom 19.15% to 26.71% but drops both Gemini models (Gemini 3.1 Profalls from 25.53% to 18.20%), which suggests the Gemini models are closely tuned to Google's own search stack. - The multi-agent
OWLframework reaches only 17.73%, with 93 runs ending in runtime failures, while theExaruns use 1.3–2.2M tokens per question, so higher cost did not buy higher accuracy. - On a 30-question sample, native-speaker humans solved 15/30 and
Gemini 3.7 Flashsolved 13/30, with only 8 questions solved by both; humans averaged 89.5 minutes per question. The main limitations are that live-web results can shift over time, only five models were evaluated, and each model grades its own answers.
World Embedding Benchmark
The World Embedding Benchmark tests how video representations encode physics. It contains 8,000 controlled simulation cases from 80 families across fluid mechanics, solid mechanics, dynamics and optics, each paired with physical annotations, and supports text-video retrieval, physical-property regression and video-description pair classification. Pre-trained omnimodal embedding models retrieve poorly and score near chance on within-family pair classification, yet lightweight probes can recover physical properties from their frozen embeddings. Continual contrastive training on physics video-text pairs improves alignment but degrades property regression, revealing a trade-off between the two. Using the embeddings to retrieve reference videos for retrieval-augmented generation with MiniMax-H3 improves the physical fidelity of generated videos.
Video embeddings are increasingly used to retrieve physical examples and guide world-model generation, but it is unclear whether they actually encode physics or merely scene categories. The benchmark pairs 8,000 controlled simulation videos from 80 families (fluids, solids, dynamics, optics & EM) with simulation-derived ground truth, so cross-modal physical alignment can be measured separately from how much quantitative physical information a probe can recover.
- Videos are rendered from
OpenFOAM,DOLFINx,Project Chrono,HCIPy/Mitsuba 3andMeepruns, and each is scored on three tasks: bidirectional text-video retrieval within each physics branch, linear-probe regression of 10 continuous properties (gravity, Reynolds number, wavelength, elastic modulus and others), and binary pair classification against a within-family or cross-family negative. - Pre-trained embedders such as
Qwen3-VL-Embedding,LCO-EmbeddingandVideo-CLIP-XLscore R@10 of only about 1–7% and sit near chance (47–53%) on within-family pairs, yet frozen-feature probes recover useful physical values, and text-freeV-JEPA2reaches strong regression nRMSE (0.94×10⁻²). - Directly prompted
Qwen3.5/Qwen3.6MLLMs reach 59–61% within-family and 91–92% cross-family pair accuracy, which suggests the physical knowledge exists in these models but is not exposed through embedding similarity. - Contrastive physics adaptation of
LCO-Embeddingon 8k held-out simulation pairs raises R@10 to as much as 21.0 (Dynamics, 7B) and cross-family accuracy to 98.4%, but worsens regression nRMSE (7B goes from 2.02 to 2.89), a trade-off between alignment and recoverable quantitative information; retrieval was also still improving when training stopped. - Used as retrievers for retrieval-augmented generation with
MiniMax-H3on twoPhyGenBenchbranches, the physics-adapted embedder lifts the average score from 0.60 to 0.66, while weakerVideoCLIP-XLreferences slightly hurt it (0.59); the evaluation covers only the optics and thermal subsets and uses top-1 references.
Applications 114
Counterfactual Predictions in Scientific Emulators Without Controlled Experiments
Scientific emulators can be accurate on observed data yet fail on what-if questions where inputs that are correlated in the data are varied independently. The usual fix is to add controlled simulation data, which is expensive. ReRoute avoids that: it pins the queried input of a pretrained model to a reference value, feeds its variation back in through a known mechanistic pathway, and fine-tunes on the original factual data, with the core causal identification argument machine-checked in Lean. On held-out coupled-climate interventions it reduces aggregate climate error by 18.2–31.8% under severe CO2 distribution shifts without losing skill under normal conditions. On an emulator trained on ERA5 reanalysis data, it preserves more of the expected surface warming in a fixed-CO2 counterfactual.
The AI Risk Observatory: What Can We Learn from AI Disclosures in Annual Reports About Societal Resilience?
The authors test whether LLM-processed annual reports can serve as a signal of how companies respond to AI. A two-stage classification pipeline, validated against 474 human-annotated passages, is applied to 9,821 reports from 1,362 UK listed companies covering 2020–2025. The share of reports mentioning AI risk rose from 2.8% to 41.2%, vendor mentions cluster around a few providers led by Microsoft, and disclosures of actual harm appear in only seven reports. In 2025, only 4.3% of reports contained AI risk disclosure classified as substantive, and the Energy and Data Infrastructure sectors lag other sectors.
On-Premises Multi-Course RAG Tutoring for Business Education: Hardware-Software Trade-offs in a Campus AI Tutor
CourseChat is an on-premises retrieval-augmented generation (RAG) tutor for six undergraduate business courses that keeps textbooks and student conversations on campus infrastructure. It runs a FastAPI service, a local vector database, and local LLMs served through Ollama on shared edge hosts. In model bake-offs several larger models failed the classroom speed requirement while a 12B and a 7B model passed, and a mixture-of-experts candidate fixed some errors but introduced new factual ones, so the team kept the 8B production model pending a demonstrated overall improvement. The authors present model choice, evidence selection, serving compatibility, and product design as one joint engineering decision, and state explicitly that the work does not establish learning gains.
Out of Sync, Out of Sight: Phantom State Attacks against IIoT Intrusion Detection
Machine-learning intrusion detection systems (IDS) for the Industrial Internet of Things (IIoT) rebuild operational state by grouping telemetry into time windows, so what they see depends on where each observation falls relative to a window boundary. The Phantom State Attack (PSA) exploits this with a passive, zero-query threat model: it adds small timing drift, calibrated to the flow's own inter-arrival variability, to push observations across the nearest window boundary, so the IDS reconstructs a phantom state. Against Random Forest, MLP, and XGBoost detectors on ToN-IoT and CIC IIoT 2025, PSA degrades detection on flows with enough packets and leaves others nearly unchanged. A query-based baseline succeeds more often but needs many queries per window, while PSA needs no queries at all, which exposes temporal aggregation as an attack surface reachable under weaker assumptions than prior evasion techniques.
WebUIProof: Benchmarking WebUI Code Generators with UI-Agent Execution Harness
Generated web UI code can compile and look plausible yet still break when a user interacts with it, and existing benchmarks mostly check only build success or screenshots. WebUIProof provides structured specifications and dense executable interaction tests for general web UIs and 3D interactive simulations. A UI agent runs these tests in a headless browser through a plan-act-observe loop: it finds DOM elements, performs actions, and checks assertions against the result. Across eight commercial LLMs, pages often render correctly but fail their interaction requirements, especially for 3D simulations. Using the test results as reinforcement learning rewards to train compact models such as Qwen2.5 14B and MIMO 7B improved how many features they completed and reduced build failures.
Large language models exhibit unreliable updating of clinical judgment as patient evidence evolves
Using matched intensive-care trajectories from electronic health records, the authors test whether LLMs appropriately revise clinical predictions as patient evidence evolves. Across diverse models, conditioning on a preceding judgment more often increased than reduced prediction error, and controlled interventions revealed two failure modes: models reacted more strongly to worsening than to equivalent improving evidence, and raising the stated prior risk from 10% to 90% shifted estimates by 26.2 percentage points with current evidence held fixed. Prompting did not fix this, while a proposed filter, EVLU (Evidence-Validated Longitudinal Update), made fewer but more reliable revisions at the cost of coverage.
AptMQL-Bench: From Text-to-SQL to Text-to-MQL via Access-Pattern Schema Design and Data-Preserving Migration
Text-to-MQL lets non-experts query MongoDB document databases in natural language. Existing benchmarks are built by mechanically converting text-to-SQL datasets, and those conversions fail to migrate 6 of 21 BIRD databases, silently drop up to 25.9% of rows, and produce slow schemas. The authors instead use coding agents with human verification to design each document schema around expected access patterns and rewrite queries to be MongoDB-native. The resulting AptMQL-Bench has 21 databases migrated without data loss and 3,186 natural-language requests. The strongest model, Claude Opus 4.5, reaches only 57.38% accuracy without external knowledge evidence and 70.34% with it.
Law And Order: Tax Law Autoformalization
Tax forms and filing instructions encode large computations involving arithmetic, branching, recursion, and table lookups, and turning them into correct software by hand does not scale. Law&Order is a neuro-symbolic framework that uses LLMs to synthesize executable symbolic programs from tax forms. It requires both structural correspondence (legal cells and schedules map to program components) and denotational correspondence (each component computes what the law specifies), and it repairs errors iteratively at the cell level using human-written OpenTaxSolver returns as checks. On 51 held-out TaxCalcBench returns never seen during generation or repair, Law&Order reaches 100% cell-level and form-level accuracy, where the strongest LLM alone reaches 66%.
Clinical Concept Centers in LLMs
Evaluations of clinical LLMs usually score only what the model says, even though internal representations can encode more than the output reveals. Using mechanistic interpretability, the authors look for clinical concepts as locatable, causally used representations inside open-weight models and find dedicated clinical concept centers in all eleven models tested. These centers activate only on their matching clinical narratives, causally drive model behavior, and stay in use under adversarial role-based priming. Steering models along these centers improves performance in simulated deployment, and in a blinded study their activation predicted clinicians' preferences.
FASTDIAR: Frame-level speaker encoder for Streaming Diarization
Real-time conversational agents need speaker diarization (working out who is speaking when) that streams and runs on a CPU, but most systems re-run an utterance-level speaker encoder on short, heavily overlapping chunks. FASTDIAR instead turns a state-of-the-art speaker recognition architecture into a causal frame-level encoder that emits one embedding every 80 ms from a two-second window of past audio. It pairs this with online clustering that only updates when the stream's self-similarity supports it. Trained purely by distillation from an utterance-level teacher, it is the most accurate streaming diarizer on low-overlap benchmarks at sub-second latency, degrades less than cache-based systems as the number of speakers grows, and runs five times faster than real time on a single CPU thread.
CreateScore: Domain-Theory-Informed Bayesian Routing for LLM-Based CV Screening
CreateScore reduces the cost of rubric-based LLM screening of CVs by routing each criterion through a hand-built Bayesian network. Decisions with low posterior uncertainty go to a local 8B model, and uncertain ones are escalated to a 120B model. On 200 synthetic Data Science CVs, 77.7% of criterion decisions were resolved locally, token use fell by 65.2% compared with using the 120B model for everything, and exact agreement with that model rose from 32.8% to 42.6%, though the gain was not statistically significant. However, the uncertainty signal did not identify the 8B model's errors (AUROC 0.47, no better than random). The authors also describe how an earlier evaluation was invalidated when truncated reasoning-model outputs were silently replaced by local labels, and recommend safeguards for evaluating model cascades.
PLCWorld: Benchmarking LLM-Generated PLC Programs in Closed-Loop Plant Simulation
Checking whether LLM-generated programs for programmable logic controllers (PLCs) actually meet task and safety requirements means watching what their commands do to simulated equipment. PLCWorld is a closed-loop benchmark that runs Structured Text (ST) programs against simulated plant responses and sensor feedback. It contains 100 synthetic tasks and 473 task-condition pairs in motion control and material handling, and it reports Task Success and Safety Violation separately. The benchmark was validated through practitioner review, reference programs, and 542 targeted counterexamples. GPT-5.5 reaches 82.70% Task Success on Easy cases but only 25.10% on Hard cases, and evaluations of six LLMs and four generate-and-verify workflows show trade-offs among completion, safety, and generation cost.
SecJev: Bringing Security Expertise to System One Decision Models
SecJev is a family of security-specialized decision models from 0.8B to 9B parameters. It is built on Kev's single-pass candidate scorer and returns typed predictions with probabilities rather than free text. The models are trained with scene weighting on SecJev-Corpus, which unifies 14 tasks from eight sources: tool outputs, network traffic, federated updates, consensus, authentication, and vehicle messages. Specialization improves every model in the family, and SecJev-0.8B beats the general Kev-9B by 20.51 percentage points in task-macro accuracy. Compared with answer-only generative fine-tuning, accuracy and latency are similar and peak memory is lower. On held-out sources, the gains carry over to prompt-injection and traffic decisions, though false alarms depend on the capture.
S2S-JEPA: Predicting the Predictable at Subseasonal-to-Seasonal Timescales
Forecasts two weeks to two months ahead, known as the subseasonal-to-seasonal (S2S) range, matter for agriculture, energy and water management. AI weather models lose skill beyond two weeks, partly because they are trained to predict fine-scale details that cannot be predicted at that range. S2S-JEPA adapts the Joint-Embedding Predictive Architecture (JEPA) to predict only the slowly varying components in latent space, using design elements taken from state-of-the-art AI weather models. It matches the gold-standard ECMWF physics-based ensemble and surpasses it on several metrics at weeks 5 to 6.
SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs
Machine-learning weather models usually forecast by stepping forward at a fixed time resolution, which accumulates errors at short time steps and ignores the continuous-time nature of atmospheric dynamics. SDECast is a neural stochastic differential equation (SDE) framework that extends SDE Matching to learn stochastic dynamics directly in physical space without repeatedly simulating the SDE during training. On a simulated geophysical flow it recovers meaningful drift dynamics, and at global scale it produces skillful hourly-resolution probabilistic forecasts up to five days ahead.
CVE2AP: Automated Generation of PDDL-Encoded Attack Paths via Large Language Models
Attack paths encoded in the Planning Domain Definition Language (PDDL) enable automated reasoning about vulnerability exploitation, but they are usually built by hand by experts. CVE2AP uses LLMs to generate PDDL attack paths from natural-language CVE (Common Vulnerability Exposure) descriptions. It combines structured prompting with an error-feedback loop that refines output using syntax and solvability errors reported by a planner. A systematic evaluation across LLMs reports up to 86.9% syntax correctness, 78.6% solvability, and 93.1% semantic correctness under LLM-as-expert judging. GPT-5.5 gives the best quality-cost trade-off, and error feedback provides the most consistent improvement.
16-bit Precision of Convolutional Neural Networks on Microcontroller Units for 8-bit Costs
Deploying neural networks on microcontrollers requires quantization that is both fast and accurate. W16A16 is a 16-bit weight-and-activation quantization scheme designed around the Armv7E-M microcontroller architecture, and the authors explain why that architecture makes 16-bit arithmetic efficient. Measured on real deployments for regression and classification tasks, it yields roughly 10 times lower quantization error than 8-bit schemes with similar or better inference time and energy use.
From Benchmarks to Production: A Text-to-SQL System for Complex Financial Data
General-purpose Text-to-SQL systems perform well on Spider and BIRD but fall below 50% on production financial databases, where concepts are stored as opaque integer keys and even simple questions need many joins. FLINT (Financial LINking Text-to-SQL) adds three components. A lookup agent resolves natural-language concepts to constraints on reference tables, embedding retrieval finds structurally similar query templates from a small expert-written bank, and schema linking prunes the schema by following foreign-key chains rather than relying on name similarity. On 359 questions over production financial schemas, it outperforms state-of-the-art baselines using the same LLM, and the system is deployed in a production data retrieval service.
Structured Composition of Verifiable Atomic Insights for Table-to-Report Generation
Table-to-report generation turns relational tables into analytical reports, but sequential data agents and direct LLM generation tend to fixate on early local observations and miss evidence that spans several tables or dimensions. ComInsight enumerates every valid atomic insight, the smallest executable analysis unit, from the database schema and content. It organizes these insights into a multi-relational graph with logical, temporal, and hierarchical edges, then applies composition operators to fuse them into higher-order conclusions. Every output comes with executable SQL and fine-grained provenance. On InsightBench, DDR-Bench, and T2R-Bench, it consistently beats strong baselines on factual correctness, novelty, and structural completeness.
MRVQ: One Resident Index for Dimension- and Rate-Elastic Vector Search
Dense-retrieval services often need to switch between embedding dimensions and index bit rates as latency, quality and memory budgets change. Training a separate quantizer for each setting forces the service to keep several indices in memory at once. MRVQ (Matryoshka Residual Vector Quantization) is a post-hoc residual quantizer whose maximum-rate code can be truncated by residual stage to lower the rate, or by coordinate to lower the dimension, so one resident index serves every (dimension, rate) pair. On FiQA and NFCorpus, it uses 17.8–22.0× less memory than three separately trained QINCo2 indices and beats PQ, OPQ and AdANNS-OPQ at matched code size, though per-rate QINCo2 still scores 0.026–0.107 higher nDCG@10. The authors also report negative results, including QINCo2 collapsing when trained at high rates.
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Agents 69
ClarifyCodeBench: Evaluating LLMs on Clarifying Ambiguous Requirements for Code Generation
Code-generation benchmarks usually assume perfectly specified prompts, while real requirements are often ambiguous or incomplete. ClarifyCodeBench is an interactive benchmark built from real-world programming tasks, with manual annotations of ambiguity types, the clarification questions they call for, and ground-truth answers. It also introduces two metrics: Turn-discounted Key Question Rate, which penalizes inefficient questioning, and Optimal Round Adherence, which measures how precisely a model elicits requirements. An evaluation of six frontier LLMs finds that strong code generation does not translate into effective clarification, that extra reasoning improves correctness but barely helps models spot ambiguity, and that clarification performance degrades sharply as the number of ambiguities in a task grows.
CONTRA: Discovering and Qualifying Behavior-Changing Questions for Selective Clarification in LLM Code Generation
Coding agents often resolve underspecified requirements by silently making assumptions, which yields code that looks correct but behaves differently from what the user intended, while asking too many questions slows developers down. CONTRA is a training-free method that generates candidate clarification questions, discards those unrelated to required behavior or already answered by the requirement, and keeps only questions where programs generated under two plausible answers behave consistently differently on shared inputs. It then uses the interaction history to pick a question or stop asking. On ClarifyCodeBench, it achieves the highest F1 with all four coding agents tested, beating the best baseline's macro-average F1 by 13.88 percentage points, and outperforms Claude Code and OpenHands in clarification recall and F1; the authors also ship it as a Claude Code plugin.
MACTS-EM: Multi-Agent Collaborative Time Series Forecasting with Emergent Memory
MACTS-EM targets time series forecasting under regime shifts, cross-domain transfer, and multimodal inputs. It uses a team of specialized agents for pattern recognition, anomaly detection, causal inference, and uncertainty quantification. A meta-cognitive layer allocates work among them, and an emergent memory mechanism transfers patterns across domains. Across financial, climate, energy, and pandemic data, the authors report 8–12% better forecasting accuracy and 22–27% better zero-shot transfer, along with greater resilience to regime shifts.
Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses
Agent harnesses make many small typed decisions, such as which model or tool to call, whether retrieved text is relevant, and whether an input contains a prompt injection. System-1 classifiers that answer in a single forward pass promise to make these decisions much cheaper than LLM calls. The authors run a paired, reproducible comparison of an open-weight model (Laya) and a hosted model (Jev) on 11 decision points built from 18 public sources. Jev is more accurate on 9 of 11, neither model beats chance at zero-shot model routing, and Laya changes 30% of its answers when the option order is reversed. Auditing their own pipeline, the authors found errors that inflated their headline claims, including a reported 23.9% cost saving that was actually 4.3% once a pre-screening cost was counted.
SimuVerity: Benchmarking Agents for Engineering-Grade Simulink Model Generation
Existing Simulink benchmarks check whether generated models compile or resemble a reference, not whether they meet engineering requirements. SimuVerity provides 101 text-to-executable Simulink tasks across ten engineering domains. A hierarchical evaluator first checks that a model is delivered, runs, and qualifies, then scores qualified models on six dimensions including accuracy, mechanistic fidelity, robustness, and dynamic response. Of six agent systems evaluated, the best scores only 42.86 overall, and the results show that structural similarity to a reference is a poor proxy for engineering performance.
DeskForge: Dense Supervision from Desktop Environments for Computer-Use Agents
Computer-use agents struggle to locate the right on-screen element in cluttered desktop scenes, and existing training data rarely provides dense, controlled annotations of such scenes. DeskForge is a controllable environment that composes real applications while varying their state, window layout, appearance, and resolution. It fuses screenshots, accessibility trees, and window geometry into DeskForge-1M, a corpus of 1.2M annotated observations. Fine-tuning four vision-language models on 200K grounding examples improves all five external GUI grounding benchmarks, with Qwen3.5-4B gaining 11.51 points on ScreenSpot-Pro. Under a fixed planner, the fine-tuned Qwen3.5-4B raises long-horizon task completion from 31 to 50 of 119 WebArena-Infinity tasks.
Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents
In long-horizon tool use, final-outcome rewards give weak credit to individual steps, and obtaining step-level supervision is expensive. CITA (Comparative Inference for Tool-use Agents) argues that an agent should estimate the long-term value of a candidate tool call before executing it. It trains a Comparative Inference Model (CIM) on paired signals drawn from logged tool behavior, a Bayesian tool-graph simulator, and comparisons judged by LLMs. Across three tool-use benchmarks and several backbone LLMs, CITA consistently improves Tool F1 and task success, and the CIM learns accurate step-level value estimates.
World Editing: Intervening on Executable Worlds at Increasing Depth
World editing is framed as changing an existing executable world while preserving everything that should stay the same, with intervention depth measuring how strongly an edit couples entities, dynamics, and systems. The authors instantiate this through game modding with IGMBench, a benchmark of 110 tasks and over 1.1K executable criteria across Minecraft and Terraria. Frontier coding agents perform strongly: the best configuration solves 78.2% of tasks under a strict task-level criterion. Reliability falls as intervention depth increases, most failed edits still build and load, and every configuration stays below a 50% joint visual pass rate.
MIRROR: Multipath Quorum Integrity for LLM Multi-Agent Communication
Agent-in-the-Middle attacks tamper with messages exchanged between agents in LLM multi-agent systems, and prior work reports success rates near 100%. MIRROR replicates each canonicalized message over k logical routes and accepts it only when a strict majority of routes report the same hash. All integrity rests on the assumption that honest routes form a majority, and the authors show that availability and integrity fail at the same threshold. Across MMLU, HumanEval, MBPP, and a MetaGPT deployment, MIRROR cuts attack success to 0% below the threshold at no extra LLM token cost, whereas an LLM-as-a-Judge defense costs 35 times more and blocks up to 44.2% of benign outputs.
DeReAct: Decomposed Reasoning and Acting for Reliable AI Agents
ReAct-style agents rely on one LLM policy both to choose actions and to decide when a task is done, so errors propagate and unsupported completion claims can end execution early. DeReAct adds two external gates: a Critic that validates proposed actions before execution, and a Context Manager that rebuilds an environment-grounded state and certifies completion. On GAIA and SWE-bench Verified, gains are largest for weaker models, with 6.5 to 7.0 points of Pass@1 for Qwen3-Coder-480B and 4.2 to 5.2 points for Claude Sonnet 4.5. With Claude Opus 4.5, Pass@1 matches ReAct, but trajectories are more evidence-complete and satisfy more constraints.
SEDIMA: Cross-Run Hierarchical Insight Memory for Evolutionary Search Agents
Systems that use LLMs to evolve programs and algorithms usually start every run from scratch, so they keep rediscovering the same improvements and hitting the same dead ends. SEDIMA adds a persistent, hierarchical memory: it condenses raw search traces into natural-language insights, clusters them by meaning using attention-weighted centroids, and retrieves relevant guidance to steer future mutations across runs and problems. It plugs in without changing the search operators, and with a budget of 100 evaluated candidates it raises average final performance by 5.5% on AlgoTune and 6.6% on ALE-Bench LITE. Under OpenEvolve, it needs 32.3% fewer iterations on average to match the baseline's best result across five backbone models.
ArrivalBench: Agent-Generated Data Pipelines Are Correct Once and Wrong Under Time
Benchmarks for agent-built data pipelines usually run the pipeline once against a fixed snapshot. ArrivalBench instead replays each pipeline under adversarial but reproducible delivery schedules, with late, duplicated, out-of-order and retried records, and checks the final state against a batch recomputation of the full log, which lets it tell silently wrong tables apart from crashes. Across 40 tasks and eleven models, single-run grading certifies 86–100% of pipelines, yet replay finds 7.0–79.2% of those certified pipelines silently wrong, and idempotency hazards cause more failures than ordering hazards in every model. Interventions can also mislead: a hazard warning cut one model's silent failures from 48.2% to 10.5%, but raised its crash rate from 9.0% to 37.0%, so total failure fell only from 51.0% to 44.0%.
Automating the Application of HCI Principles: Skills for On-Demand UI Construction, the Human-AI Space to Think, and the Future of HCI
Large language models can now generate working user interfaces (UIs) on demand from a description of the user's task, and this position paper asks how to make those interfaces well designed rather than merely functional. It proposes treating the user–AI dialogue as a shared "Space to Think" in which breaking down the task produces the interface, and encoding classical human–computer interaction (HCI) knowledge as machine-readable skill.md files that the generating agent loads at runtime. Examples include Nielsen's heuristics, Norman's affordances, Web Content Accessibility Guidelines (WCAG) success criteria and cognitive-load limits. The paper argues these skills would be inspectable, version-controlled artifacts owned by the HCI community, and it outlines a research agenda for that shift.
Coco: An Agentic Copilot for the Hardware--Software Co-Design Lifecycle
Co-designing ML models and the accelerators that run them depends on large volumes of fresh simulation data about systems that do not yet exist, so LLMs cannot rely on pretraining knowledge or literature retrieval, and naive chat-with-your-data tools hallucinate. Coco, an agentic platform deployed with TPU architects, has four layers: a datastore that registers every simulation sweep in a normalized relational schema, typed tool APIs that agents compose on their own, agents that encode recurring workflows such as iso-execution analysis, and a UI whose navigation state also serves as agent context. A key design choice is that agents ground every number in a SQL query rather than scraping raw files. The authors report early deployment experience aimed at shortening time to simulation and time to insight, and argue that co-design is a distinct agentic domain in which data must be retrieved rather than memorized.
Inherit-MAS: Test-Time Evolution of Multi-Agent Systems through Workflow and Execution Inheritance
Test-time evolution of LLM multi-agent systems (MAS) improves workflows using execution feedback, but sweeping revisions can break components that already work, and re-running unchanged steps wastes compute. In Inherit-MAS, a meta-model synthesizes a workflow of worker agents with declared roles, inputs and tool permissions, and a separate judge scores each run and diagnoses its weaknesses. Workflow inheritance then edits the latest candidate locally, and execution inheritance reuses stored results whenever the full request and execution context match. With GPT-4o-mini workers it reaches 55.4% completion on WorkBench and 49.7% joint F1 on HotpotQA FullWiki, outperforming EvoAgent, EvoMAS and TacoMAS, and execution inheritance cuts worker-token usage by 29.1–34.6% compared with the same controller with reuse disabled.
Trained Agentic Context Management
Rather than training a model natively on long context or engineering an elaborate long-context harness, the authors train a model to use the simplest possible harness. It has just two tools: one that calls the model itself with any prompt, and one that reads a chosen range of tokens from the input. They fine-tune Qwen3.6-35B-A3B on diverse synthetic data using this setup. Working with only 8,000 tokens of context, the small model matches GPT-5.4 running with 1M tokens of context on OOLONG-synth once documents exceed 40K tokens.
When Terminal-Agent Training Stalls: Demystifying Data Generation and Verification Challenge
Teams often use a frontier model such as Claude Opus as a meta-agent that writes terminal tasks and verifiers for reinforcement learning (RL) training of terminal agents. The paper shows that a runnable Docker image and an executable test suite do not guarantee a sound training pipeline, and sorts the failures into benchmark invalidity, harness brittleness, and reward misalignment. Redesigning prompts and extending context raised baseline solvability 5.6 times. A 9B model then saturated at 81.3% mean pass@2 within 20 steps, and adding harder tasks dropped it to 20.6% with no change to the training configuration, which indicates the band of solvable task difficulty is specific to each model. The authors argue that calibrating that band, auditing verifiers, and accounting for infrastructure errors should be primary evaluation criteria rather than afterthoughts.
The AI Theorist reveals excitonic structure in $\alpha$-RuCl$_3$
AI Theorist is a system of AI agents that builds physical models on its own by generating hypotheses, running first-principles calculations, and refining its ideas against evidence. The authors apply it to optical and photocurrent data from α-RuCl3, a leading candidate material for a Kitaev quantum spin liquid. The system arrives at a new interpretation that identifies distinct excitonic states with contrasting optical selection rules and different real-space distributions. The authors describe it as the first AI system to autonomously develop a physical model explaining previously unpublished experimental observations in a quantum material.
Finding the Move Is Not Winning the Game: XiangqiBench for Closed-Loop Evaluation of LLM Agents
Static benchmarks give a model credit for naming the right move, but an agent has to carry a plan through to the end while an opponent responds. XiangqiBench tests this in Chinese chess: across 119 tactical endgames with forced mates, an LLM agent must checkmate an engine defender through a REPL that keeps real moves, state queries, and forward simulation separate. Across 8,568 multi-turn trajectories from 12 frontier LLMs, models play the reference first move in 26.1% of trials, yet only 13.9% of those trials end in a win. The leading model reaches 38.7% pass@3 but only 5.9% pass^3 (winning all three attempts), and in 49.3% of comparable cases the real defender replied differently from the line the agent had simulated. The authors conclude that agent evaluations should score closed-loop outcomes and report reliability alongside coverage.
SideKernel: A Usable microVM Sandbox for AI Coding Agents on macOS
AI coding agents need autonomy on developer machines but are untrusted, and local open-source sandboxes for them on macOS are few and awkward to use. A user survey finds that fewer than 40% of coding-agent users run their agents in a sandbox and identifies the main usability barriers. The author uses those findings to build SideKernel, an open-source, local sandbox for macOS based on microVMs. On 23 capability tests derived from the survey, SideKernel and Docker Sandboxes score highest on usability among the qualifying tools, and the paper also surveys existing macOS microVM sandboxes.
CUEing User Simulators: Calibrated User Embeddings for Multi-Turn Benchmarking
AI agents are often evaluated in multi-turn benchmarks against LLM-based user simulators. The authors argue that these simulators can sound human but lack outcome calibration: they do not reproduce the success rates and failure patterns seen when real users talk to the same agent. Calibrated User Embeddings (CUE) encodes observed sessions into continuous representations, samples new ones, and decodes them into persona commands that steer an LLM to act as a user simulator, with no training of the simulator LLM. On τ²-Bench, CUE-steered simulators make fewer simulator-caused errors and reproduce real-user failure modes, aggregate success rates, and per-task outcomes more faithfully than other persona-based methods, while staying competitive on established fidelity metrics. They also generalize from customer support to document creation, math tutoring, and casual conversation, and work across different simulator LLMs without retraining.
APDMem: Agent-Controlled Progressive Disclosure for Query-Adaptive Long-Term Memory
Personalized LLM assistants have to find sparse evidence in long conversation histories, and different queries need different amounts of digging. APDMem (Agent-controlled Progressive Disclosure Memory) stores history in four increasingly detailed layers: thematic summaries, key personal facts, turn-level evidence notes, and raw messages. A controller reads the summaries first and drills into finer layers only when a query needs it, and a note synthesizer then organizes the retrieved evidence by consolidating facts, ordering events, and flagging contradictions. On LongMemEval it performs strongly on long-context memory reasoning while accessing only 8% of the total conversations.
MEA: A Reward-Driven Multi-Agent System for Faithful Model Explanations
Post-hoc explanation tools for machine learning models take expertise that most domain practitioners lack. MEA is a multi-agent framework in which a Proposer agent picks and configures explanation tools for a given question and data type, and an Actor agent, trained end to end against faithfulness rewards, turns the tool outputs into natural-language explanations for tabular, text, and vision models. The authors also introduce question types for feature attribution, counterfactual reasoning, and spurious-feature detection, each with a perturbation-based faithfulness metric, and find that frontier LLMs systematically produce unfaithful explanations. Across six datasets MEA beats post-hoc, agentic, and closed-source baselines, with faithfulness gains of +28% (tabular), +21% (text), and +34% (vision) over the untrained backbone.
Compound AI System Reliability: A Failure Taxonomy and Resilience Pattern Catalog from 150 Production Incidents
Compound AI systems often fail at the boundaries between components rather than inside individual models, through cascading errors, silent quality degradation, and coordination failures. From 150 production incident reports drawn from open-source projects and anonymized enterprise deployments, the authors build a taxonomy of 23 failure modes in five categories: retrieval, generation, tool, orchestration, and integration. They then measure resilience patterns with controlled fault injection: circuit breakers cut cascade propagation by 89%, output quality gates catch 73% of silent degradation, and component isolation reduces blast radius by 64%. Systems using three or more patterns reduce mean time to recovery (MTTR) by 71%, and the taxonomy and pattern catalog are released for practitioners.
Hypothesis-guided discovery of cognitive algorithms via program refinement
Cognitive scientists want models of the algorithms people use to reason, but hand-built models are hard to scale, while models generated from scratch by LLMs leave no room for human expertise. This hybrid system treats discovery as program refinement: researchers write cognitive models as probabilistic programs, and a team of LLM agents finds mismatches with behavioral data, proposes code changes within researcher-set constraints, and checks structural fidelity. A probabilistic inference module then infers latent variables and computes data likelihoods for each revision. On human behavior in a problem-solving task, revised models consistently fit better than their ancestors and reveal a small set of recurring innovations that capture meaningful variation in behavior.
Learning What to Investigate Next: Meta-Reasoning for Long-Horizon Research Agents
Long-horizon research agents must decide not only how to investigate but what to investigate next, which is hard to learn because those decisions are sparse and their effects show up much later. MIRA (Meta-reasoning for Iterative Research Agents) splits the work in two: an outer meta-reasoner curates context from a persistent research record and writes a work order for the next investigation, and a fresh inner executor carries it out. Even without training, this improves long-horizon inference and compute allocation on theorem proving and open-ended neural-architecture research. The decision boundaries also serve as units for credit assignment: a generative critic trained there beats token-level alternatives, and the resulting MIRA-AC actor-critic, trained on the model's own proxy hill-climbing signals, improves gold performance across four autoresearch environments and transfers with cross-environment value initialization.
How To Train Your World Model: Fine-tuning vs RAG for LM-based World Modeling
World models (WMs) let agents predict the consequences of their actions, and in text environments the usual approach is to fine-tune a language model to act as one. This study compares fine-tuning with retrieval-augmented generation (RAG) across five embodied, web-navigation, and social environments, and finds that fine-tuned WMs give agents higher rewards in 15 of 20 settings. RAG is more data-efficient, while fine-tuning gains more from additional experience. A counterfactual-intervention procedure shows that retrievers often surface suboptimal transitions, which hierarchical query reformulation helps fix. A hybrid system that learns core dynamics parametrically and retrieves from an actively maintained memory store consistently outperforms both approaches across environments and models.
OpenGameEval: Benchmarking Agentic Programming and Exploration in a Stateful Game Engine
OpenGameEval is a benchmark for agentic game development in Roblox Studio, running language models as agents in reproducible, stateful game-engine sessions and scoring each run with executable checks on the edited scene and in simulated play. Its eight-tool action space separates observation tools from editing tools, so exploration can be measured directly. Across 13 frontier models on 84 human-curated tasks, the best model solves only 51.7% of tasks on a single attempt and 39.4% on all five of five attempts, and six tasks go unsolved by every model. Exploration predicts success: with task and model held fixed, runs that inspect every object a reference solution touches before acting pass 13.4 points more often on scene-only tasks and 9.8 points more often on script-only tasks.
Pincer: Resource Authorization for Agents using a Digital Twin
Autonomous coding agents that use a general-purpose shell and persistent memory are hard to protect. Today they rely on user-approved permissions, which lead to policy decay and approval fatigue, or on tool-call classifiers that learn nothing about the specific user. Pincer adds a defense at the resource layer: a "digital twin" model running in an isolated context. The twin continually learns the user's preferences and acts as the user's proxy when the agent requests permissions, enforcing dynamic least-privilege policies. The authors evaluate it on a new dataset of multi-day user-agent transcripts, where Pincer beats LLM-judge baselines and adaptations of Conseca on both security and utility, with large gains on certain attack types.
Open-Endedness Bench: Measuring Epistemic Process from Agent Records
Agents doing open-ended research are judged by their final scores, which do not show whether their claims actually follow from the experiments they ran. Open-Endedness Bench (OEB) reads only an agent's execution record and builds a graph linking each stated claim to the executed actions that test it. From that graph it scores four competence axes (evidence, experiment, revision, and avoiding reward hacking) and profiles the agent's research habits. Across 119 runs on 12 tasks covering LLM post-training, chip design, and training-speed records, only 16-29% of the improvements agents claim are real when checked against logged results. The habit profiles depend far more on the underlying model than on the task.
VERSE: Verified Self-Evolving Optimizer for Agent Harnesses
Harness evolution improves an LLM agent's prompts, tools, and workflow, but the optimizer doing the improving usually stays fixed. In a controlled study, the authors find that letting the optimizer evolve itself only helps when it can verify edits by executing them. Building on this, VERSE lets the optimizer test draft edits, replay failures, and perturb suspected steps before submitting a change, and track fixes and regressions across rounds. It then revises both the executor's harness and its own prompts, skills, tools, and hooks, while all model weights stay frozen. VERSE improves all four harness optimizers it was tested with, and its best harness reaches 42.3% on held-out SWE-rebench tasks and 37.7% on newer out-of-distribution tasks in five languages, compared with 39.2% and 29.3% for the strongest baselines.
Lost in the Request: How Communication Variation Disrupts Retrieval and Action in Email Agents
Most benchmarks test each task with a single canonical phrasing, so they don't measure whether an assistant stays reliable when the same request is worded differently. The authors build validated variants of requests along five communication-style axes and four rule-based English dialect conditions, keeping the task and evidence fixed. They test the variants on a retrieval-augmented generation (RAG) pipeline and two tool-using email agents. Indirect requests lowered performance on all three benchmarks, and formal requests lowered it on both agent benchmarks. Verbose phrasing mainly hurt the lexical retriever, while indirect and formal requests mostly caused agents to skip required actions rather than take unsupported ones.
Coherence-Driven Belief Formation and Population Dynamics of Contagion in LLM Agents
Rather than assuming how individuals adopt beliefs, the authors measure empirically how likely an LLM agent is to adopt a claim depending on how many of its peers endorse it. This adoption curve is sigmoid, the signature of complex contagion, and its threshold depends on the claim's plausibility, the source's reliability, and the agent's disposition. These three factors collapse into roughly one dimension, which the authors interpret as how coherent the claim is with the agent's prior beliefs. In networks of agents, beliefs spread further on clustered networks than on random ones, and consensus is hysteretic: once established, it is much harder to remove than it was to create.
LEAP: Learning Efficient Action Proposals For LLM Agents
LLM agents are slow because each reason-then-act step must finish before the next begins, and earlier action-speculation methods rely on off-the-shelf drafters that are either large and slow or small and rarely right. The authors build a latency framework for a speculative round that weighs gains (drafter accuracy and remaining task horizon) against costs (drafting, waiting for verification, and tool execution). Guided by it, LEAP (Learning Efficient Action Proposals) trains a small 0.6B drafter on the target model's own action sequences, making agents up to 60% faster in end-to-end wall-clock time with no systematic change in task success. The drafter can also be trained online without prior trace collection and match offline training.
Decoupling Memory from Context: Structured Memory for Token-Efficient Test-Time Continual Learning
Agent memory systems that keep appending to a shared context run into rising token costs, context-window limits, and degraded performance. The authors frame memory updates as an optimization procedure over the model's context and propose GraphMemory, a lightweight graph-based memory that accumulates, refines, and links reusable strategies, retrieving only the relevant subgraph per query so retrieved memory stays constant as experience grows. It achieves competitive downstream performance while using roughly 81-85% fewer memory-construction tokens than baselines.
Self-Supervised Scaling of Terminal Environments for Scientific Domains
Training terminal agents for scientific domains requires executable reference behavior and domain-specific verifiers, which are costly to author task by task. Software-in-the-loop reconstruction (SWR) builds tasks from existing software workflows: an agent sees the instruction, input schema, and public input-output examples, must write a program reproducing the workflow, and is checked on hidden configurations by a hierarchical verifier with anti-shortcut checks. Using 500 workflows across six domains, Qwen3.8-Max produced 1,422 verified trajectories, and fine-tuning Qwen3.8-27B on them raised mean Terminal-Bench 2 performance from 47.94% to 53.56%, beating matched-token corpus controls.
Ego2World: Compiling Egocentric Cooking Videos into Executable Worlds for Belief-State Planning
Ego2World is a benchmark that compiles annotated egocentric cooking videos into executable planning environments under partial observation, linking steps and objects to symbolic action rules, persistent world state, and explicit goal conditions, with world state kept separate from agent belief. Evaluating six planners on 105 tasks shows that accepted actions often leave task goals unmet, and execution traces separate interrupted runs, partial progress, and completed runs that miss the goal. In a paired Qwen-Plus study, persistent belief improved action validity by 4.15 percentage points and cut visual queries by 90.27%, at higher token use and with no detected completion gain.
Beyond Correctness: Resolving Underspecification in Agentic Text-to-SQL
Agentic Text-to-SQL systems can ask users clarifying questions. A correct query result can still hide unverified assumptions that happened to match the intended answer. The authors trace this partly to agents ending clarification too early, and find that agents often drop questions they had already identified as relevant, even when prompted to plan. PlanPool keeps the clarification plan as an external, editable question pool: every planned question must be explicitly asked or dropped before the agent submits, and new ambiguities can be added along the way. On three benchmarks derived from BIRD-Interact and Spider, it covers more ambiguities and produces fewer silent failures while keeping execution accuracy competitive.
EpiWorld: Grounding LLM Policy Agents in Epidemiological World Models
A language model reasoning about epidemic interventions on its own lacks epidemic dynamics, surveillance data, and institutional constraints. EpiWorld connects an LLM policy agent to a learned epidemiological world model that predicts how an epidemic evolves under a candidate intervention, plus a tiered skill library of public-health protocols, surveillance tools, and lessons from after-action analysis. Fast counterfactual rollouts give feedback for choosing and refining policies, and their outcomes are distilled into reusable lessons. On retrospective COVID-19 and influenza data, the world model has the best out-of-distribution peak error among forecasting baselines. The full loop reduces cumulative hospitalisation by up to 59% and by about 16% on average across six LLM backbones, outperforming reinforcement-learning and optimal-control baselines.
When History Fails to Become Experience: Action Calibration in Language Agents
Language agents are supposed to learn from earlier attempts within a task, but extra interaction history sometimes lowers success. The authors find that much of history's benefit remains even when past actions are shuffled, which suggests agents do not reliably connect past actions to their outcomes. Explicitly labeling each observation as the outcome of the preceding action improves task success and reduces repeated actions without adding any new information. A learned calibrator that reassesses past actions and selectively records experience improves success further.
Scaling Trajectories for Complex Tasks through Recursive Self-Rewrite
Successful agent runs on hard tasks make good training data, but they often rely on specialized harnesses whose interventions won't exist at deployment. Recursive Self-Rewrite (RSR) uses a single model, Qwen-3.8-27B, to solve tasks under several harnesses and then rewrite those solutions as trajectories under a general harness. A planner distills each solution into a runbook, a critic screens for leaked verifier details or answers, and an executor replays the runbook in a fresh sandbox, turning 2,001 source trajectories into 11,094 for supervised fine-tuning. The fine-tuned model beats both the base model and direct trajectory fine-tuning, raising pass@3 on Terminal-Bench 2 from 57.0% to 74.2%, with gains on harder terminal benchmarks as well.
Harness-Aware Distillation for Small Language Model Agents
An LLM agent runs inside a harness, the software that manages its context, tools, and feedback, and that harness stays in place when the agent is distilled into a smaller model, yet standard distillation treats the harness as just more input. Harness-Aware Distillation (HAD) adds to on-policy distillation an action preference that contrasts the teacher's choices with and without harness information. A validity check drops preference pairs whose preferred action contradicts the harness records, and the method needs no task rewards or success labels. Across several long-horizon agent benchmarks, HAD outperforms on-policy distillation baselines with the same harness, getting stuck in fewer unproductive loops and recovering from errors more often.
AgentTrap: Stateful Feedback Deception against Autonomous Penetration Testing Agents
Conventional honeypots rely on static decoys, so they cannot keep up with autonomous penetration-testing agents that adapt their plans to the responses they get. AgentTrap is a closed-loop honeypot built for such agents. It uses sentinel endpoints to avoid catching benign traffic, stateful deception grounded in the protected application, and behavior-guided escalation that keeps the agent engaged and collects evidence about it. Against eight pentesting agents attacking a deployed web application, it lowers the real-target attack success rate from 95.8% to 79.2% and tricks agents into leaking their own API keys in 18.8% of runs, beating static deception and fixed escalation. Trace analysis shows that resisting this kind of counterattack depends both on whether the model recognizes deceptive requests and on whether the agent architecture isolates sensitive resources.
Interpreting at Write Time: A Policy Ablation for Multi-Goal Agent Memory
A long-running assistant has to summarize its history to save space, and what it keeps depends on which goal it is summarizing for, a choice made before it knows which of the user's standing goals will ask. The authors compare three write policies with the read step held fixed: a goal-neutral summary, one summary covering all goals, and a separate summary per goal. They test these across several models and event streams. Summaries written for different goals overlap less than a summary overlaps a rewrite of itself, and per-goal summaries win on relevance, completeness, and accuracy, while the combined all-goal summary does worse even than the neutral one written with a fraction of its budget.
Discriminating Fixture Coverage in Agent-Infrastructure Verification Suites
Invariant suites that gate agent deployment are usually validated by showing that they pass a correct implementation and fail one broken one. Using mutation analysis on a suite for a multi-session agent state-projection layer, the authors show this check approved a suite that missed a mutant removing event-identity deduplication. After fixing that, they froze the twelve-check suite, and it killed only five of ten adversarially specified mutants. The five survivors fell into two groups: three were never triggered because no fixture exercised the changed behaviour, and two corrupted internal state that no check could observe. Treating the missing inputs as a coverage question, the authors added one fixture per uncovered input dimension, and all five survivors were then killed, each by the check predicted in advance.
When to Compile a Computer-Use Agent? Measuring Payback and Making Compilation Decisions for Token Efficiency
Compiling GUI procedures that a computer-use agent repeats into programs can cut token costs, but compilation may fail or need repair, and future reuse is uncertain because tasks may stop arriving or the GUI may change. PACE (Payback-Aware Compilation from Experience) pairs a measurement protocol, which records compilation costs including failures and compares agent and program execution on matched inputs, with an online algorithm that compiles only when estimated savings outweigh the cost. Under stated assumptions, its total cost stays within 1+ε of running every task with the agent, and successful compilations pay back after 2–16 uses. In simulations on recorded task arrivals, PACE reduces token costs by 17.3% compared with ReAct and by 24.9% compared with an AutoRPA adaptation.
Continual Graph Memory for Mathematical Research Agents
Agent harnesses working on frontier mathematics can run many agents in parallel for long periods, and they produce large volumes of intermediate results that are hard to organise and reuse. Ansatz is a mathematical research agent built around Continual Graph Memory, a graph that stores facts, plans, and counterexamples along with the relationships between them, and carries knowledge across problems. It adds dependency-aware retrieval, a curator that updates the research frontier and distills lessons from earlier attempts, and scoped recall that has earlier results re-proved locally rather than reused blindly. Ansatz closes all ten First Proof Second Batch problems and, without human intervention, produces solutions to the Jamison caterpillar conjecture and Erdős Problems 289, 348, and 488.
GTDD: Generative Test-Driven Development for AI Coding Agents with Adversarial Testing
AI coding agents doing test-driven development can fit their code to the tests they see and still leave much of the intended behaviour unimplemented. Generative Test-Driven Development (GTDD) adds a separate testing agent that writes new inputs after each candidate implementation is fixed, guided by a human-written behavioural contract. A trusted evaluator checks these inputs, returns reduced counterexamples to the coding agent, and keeps them as regression tests. A finite-population analysis bounds how often a flawed implementation gets accepted and shows that fresh random audits after each candidate is committed keep this under control. In a paired experiment on a stateful key-value store, policies that regenerated tests during development ended with lower failure rates than generating tests once, and giving the tester the candidate's source code added no detectable benefit.
Sentry: Learning to Recover from LLM Agent Failures at Test Time
LLM agents often fail mid-task through invalid tool calls, repeated actions, or poorly grounded reasoning. The authors find that lessons learned from past failures can hurt when they sit permanently in the agent's context, because they misfire on tasks where that failure is absent. Sentry is a failure-management layer that runs alongside the agent. When it detects a failure, it retrieves matching lessons from an external playbook, checks without access to task rewards whether the agent recovered, and stores a new lesson only when the recovery succeeded; the full playbook never enters the agent's context. Sentry outperforms the strongest runtime-intervention baseline by 37% on average and the strongest context-evolution baseline by 39%, and its learned lessons transfer to held-out tasks.
Beyond Predefined Sinks: Security-Aware Dependency Analysis for LLM Agents
LLM agents connect model decisions to sensitive capabilities such as command execution, filesystem and network access, and browser control, but static analyses that only flag predefined sensitive operations cannot tell whether a given call is actually dangerous. AgentSecGraph builds a Security-Aware Agent Dependency Graph (Security-ADG) around each candidate operation, adding agent relevance, data sources and dependencies, trust boundaries, guard checks, and external effects. On AgentSecBench, a corpus of 67 real agent repositories, it identifies 23,866 candidate operations in 50.8 minutes, and reproduction-backed checks establish 22 security-sensitive behaviors, including one confirmed vulnerability. On nine held-out cases, Security-ADG preserves 91.1% of the reference context, compared with 20.0% for a sink-only view.
DyadMem: A Long-Term Memory Benchmark of How Agents Work with Users
Long-term agents need to remember facts about a user and also how they themselves should work with that particular user, but existing benchmarks focus on user facts and score only final-answer QA. DyadMem introduces User-conditioned Relational Agent Memory (URAM) and annotates it alongside user-side memory across 3,065 episodes, 50,961 sessions, and 61,210 QA instances. Annotations cover each stage of memory handling: what to capture and update per session, and what to recall per query. Across 16 open-weight and 4 proprietary models, QA is strong when gold memory is provided but drops sharply when the model runs the full memory pipeline. Even frontier models show low capture recall, incomplete recall, and unsafe deletions, and adding URAM helps all 20 models.
When Numbers Start Talking: Numerical Signalling and Strategic Behaviour Among LLMs
The authors study how communication shapes cooperation among LLM-based agents in strategic games. Agents built on four popular LLMs play four games with different cooperation equilibria, exchanging natural-language messages, numerical signals, or random sequences under various assigned personalities. Structured messages change final payoffs in most games and models, but not in any predictable pattern, which challenges the assumption that agents reliably converge to stable equilibria. Agent-generated numerical messages are clearly non-random and track the payoff structure, yet are hard for humans to interpret. The authors therefore suggest that monitoring for coordination should focus on message-level fingerprints, which generalize across models, rather than on behavioral decisions, which do not.
WebFovea: When the Model Is Right but the Click Is Wrong -- Reliable Round Trips for Vision-Based Web Agents on Live Websites
WebFovea, a vision-based web agent that placed second in the WebRetriever Challenge 2026, shows that many failures on live websites come from the harness, meaning the code between the model and the browser, rather than from the model's reasoning. The authors split each step into four stages: parsing the reply into an action, executing it on the page, reporting the result back, and showing the model the information it needs. They found bugs such as a coordinate mismatch that placed every click at 3/4 of its intended position, silent failures on dropdowns, iframes, and text boxes, and chat-template tokens contaminating 4.9% of episodes. With the same model in every submission, hardening the harness alone raised the hidden-set score from 31.0 to 57.0. The paper also includes negative results and a failure analysis.
Peer Influence across Heterogeneous AI Models
The authors test how often one language model changes its answer after a single exchange with a dissenting peer, across seven open-weight models and three language understanding tasks. Persuasion is strong, and neither a model's standalone certainty nor its scale reliably predicts how persuasive or persuadable it is. The size of the shift depends more on how susceptible the listener is than on how persuasive the speaker is. As a result, a small dissenting model can overturn the judgment of a much larger one, so mixed-model multi-agent systems have to be evaluated in the specific pairings they will run in.
Ask, Relax, or Act? Evaluating Actionable Indeterminacy in LLM Preference Reasoning
An LLM agent can recognize that a request is uncertain and still pick the wrong next step. The authors formalize when an agent should act (one action is acceptable under every admissible preference), ask for clarification (each possibility is feasible but no single action works for all), or propose a minimal constraint repair (the request is infeasible). Their solver-grounded benchmark covers allocation, meeting scheduling, apartment choice and stable matching, and uses matched pairs that differ only in whether intervention is needed. Models tend to intervene even when a justified action already exists, a correct decision label does not guarantee a usable question or repair, and stating the required response content explicitly improves fully correct responses and can change the decisions themselves.
Source Preference in the Wild: How LLM Agents Favor Items by Source, and How to Reduce It
When LLM agents choose products, hotels or papers for users, they may favor items based on which site or service the items come from. Across end-to-end search with 12 agent models in three domains, every model prefers some sources and avoids others, and the models largely agree on which ones. This preference can outweigh request fit: an item meeting one fewer requirement is chosen about two-thirds of the time when it comes from a preferred source, but almost never in the reverse case. Hiding source information weakens the effect and relabeling an item with a preferred source raises its selection rate, while supplying missing item details or adding a prompt that counters preconceptions about sources reduces the bias.
Toward SLM-based agentic task-tool intent matching
Standard authorization can decide whether an agent is allowed to call a tool, but not whether that call actually serves the task it was given, which leaves room for rogue or manipulated agents. The authors examine whether Small Language Models (SLMs) can act as low-latency, on-premises classifiers that judge each selected tool's relevance to the assigned task and pass that signal on for enforcement. They build a new dataset of multi-tool tasks whose required tools span different Model Context Protocol (MCP) servers, and they specialize SLMs through prompt optimization, supervised fine-tuning and reinforcement learning with GRPO.
D2K-Bench: Can LLM Agents Turn Expert Designs into Efficient GPU Kernels?
D2K-Bench is a diagnostic benchmark of 26 tasks and 85 workloads that measures how well LLM agents turn expert design guidance into efficient GPU kernels. The guidance comes at three levels: algorithmic insight, dataflow design and low-level optimization tricks. Each model is run with and without it under the same tools, hardware and 350-turn budget. Across five models on NVIDIA B200 GPUs, guidance raises correctness from 93.1% to 98.5%. For the three frontier models (GPT-6-Astra, Claude-Opus-4.8 and GPT-5.6-Sol), geometric-mean speedup rises from 1.69x to 2.49x, though the benchmark's analysis of the generated code shows some design properties still go unimplemented.
JOVE: Joint Execution and Verification for Resource-Aware LLM Task Graphs
Complex queries can be broken into directed acyclic task graphs and spread across heterogeneous LLMs, but it is unclear in advance which model suits each subtask, and execution alone does not show whether an output is correct. JOVE jointly assigns executor LLMs to subtasks and decides which intermediate outputs to send for paid, asynchronous verification. It feeds verification results back to improve later assignments under a long-term budget and a per-query latency limit. Each query is solved as a mixed-integer linear program with an information-gain bonus for learning, and the authors prove sublinear regret for learning model quality. On four reasoning benchmarks it matches standard inference baselines in accuracy while cutting average cost and latency by at least 3.17x.
Lightweight, Rubric-Guided Trajectory Evaluation for Production AI Agents
Evaluating the long trajectories of production LLM agents, which are full of tool calls, observations, and retries, is expensive to do repeatedly. LiteTrajEval derives compact domain-specific rule profiles offline. Online, it preprocesses each trajectory, flags heuristic failure signals, serializes the trace under a fixed token budget, and calls a single rubric-guided LLM judge to produce structured diagnostic reports. On Magentic-One-style and τ-bench-style datasets it improves agreement with human failure localization by up to 20 to 35 percentage points over AgentRx while cutting cost about 6x and evaluation time more than 8x. The system is deployed in an enterprise agent platform.
ReFract: Benchmarking Perspective Awareness in Language Model Agents with Text World Models
Large language model (LLM) agents used in settings like industrial maintenance must tailor their actions to the user's role, because a wrong action on physical equipment can cause irreversible damage or harm. The authors call this capability Perspective Awareness and introduce ReFract, a benchmark of 150 expert-validated entries in which the same query requires different actions depending on who is asking. The entries are grounded in anonymized support conversations and simulated with Text World Models. State-of-the-art LLMs solve at most 69% of tasks, and more than half of their trajectories include attempts at actions the user's role should not take.
SCAD: Structured Credit Assignment and Distillation for Long-Horizon Agents
Training long-horizon agents is hard because sparse terminal rewards hide intermediate contributions, and on-policy distillation loses teacher guidance as the student's own histories grow. SCAD (Structured Credit Assignment and Distillation) splits interactions into planning and bounded subtask execution. It distills execution in local contexts and refines planning credit using subtask prefix trees built across rollouts. Planning gets full terminal credit, while execution gets positive terminal credit plus teacher guidance. Averaged across benchmarks, SCAD beats the strongest training baseline by 4.48 percentage points on text tasks and 4.19 points on multimodal tasks.
Knowledge or Calculator? Decomposing the Skill Premium in Verifiable Financial Agent Workflows
FinSkillBench evaluates financial AI agents on 2,603 point-in-time episodes covering portfolio construction, risk management and fundamental analysis, each scored by a deterministic verifier. Across 17,820 runs with 9 models, curated skill packages raise mean scores by +16.2 points, while skills the agent writes for itself during an episode add only +0.5 points and cost more tokens. Splitting the curated gain apart, executable domain tools alone add +19.5 points and human-written procedure documents alone add +5.6, and combining them helps less than the sum. A second, independently built harness reproduces the direction of the effects but not their size. The authors conclude that a skill premium belongs to the whole model, resource and harness system rather than to the model alone.
HyperBrowseComp: A Multilingual and Multimodal Stress Test for Web-Browsing Agents
HyperBrowseComp is a web-browsing agent benchmark of 423 hand-written, human-validated questions in 13 languages, authored by native or highly proficient speakers. Answering a question requires finding obscure evidence, following multi-step chains of clues, or inspecting videos, scanned documents, images or maps. Questions that models could answer without internet access were filtered out. Models are evaluated both with their providers' built-in search and with a shared external retrieval harness under one agent protocol, and a human evaluation on a sample of questions provides an effort baseline. The intended difficulty comes from discovering and connecting evidence across languages and modalities on the open web.
NeutronGym: Physics-Graded Neutron Instrument Design for LLM Agents
NeutronGym is an executable environment where LLM agents design neutron scientific instruments using validating tools. The McStas simulator ray-traces each design, and a level-resolved ladder grades syntax, runtime, structure and science with no LLM judge. On the curated McStasBench slice of 16 tasks drawn from published instruments, seven models reproduce at most 7, and none meets an improvement target. Reinforcement learning on the environment's reward takes Qwen3-8B from 11% to 77% on held-out instances, surpassing an untrained Qwen3-32B. Removing the ladder's partial credit drops that gain by 60 points. The authors also describe four task designs they discarded because no-model baselines could solve them, and they release the probes that caught those designs.
Credit Where It Matters: Dependency-Aware Policy Optimization for Terminal Agents
When reinforcement learning (RL) trains terminal-using agents, the reward often gets credited to commands that had no bearing on the final outcome. DepGPO (Dependency-Aware Group Policy Optimization) builds a command dependency graph from execution traces and traces backward from the resources the task verifier inspects. It then assigns credit to the relevant writes and the reads that support them, redistributing trajectory-level advantages across steps along these paths. Comparisons and ablations show that dependency-aware credit assignment improves both task performance and training stability on complex terminal tasks.
FrugalEvo: Towards Cost-Aware LLM-Guided Program Evolution
LLM-guided program evolution methods such as AlphaEvolve are usually judged on gains over a fixed number of iterations, not gains per unit of cost. FrugalEvo uses a stronger, pricier LLM to explore solution strategies and a cheaper LLM to implement and refine the code, with prompts structured to maximize prefix-cache reuse. The authors also introduce Budget-Aware Area Under the Curve (BA-AUC), which measures best-so-far score against cumulative LLM cost. Across 10 math and systems optimization tasks, it matches or beats OpenEvolve, ShinkaEvolve, AdaEvolve and EvoX on final quality and achieves higher BA-AUC on 9 of them. On circle packing it reaches state of the art for $0.55–$1.68, compared with about $50 for multi-agent methods.
4DCodeBench: Benchmarking Agents on Inverse Graphics of Dynamic Scenes
4DCodeBench tests agents on 4D inverse graphics: rebuilding dynamic scenes from video as executable graphics programs, which can require writing physical simulations to reproduce the observed behavior. The benchmark combines curated real-world videos with synthetic scenes covering deformation, fluid flow, and fracture. Extensive evaluation of frontier models finds that strong static reconstruction ability does not yet carry over to reliable reconstruction of complex dynamics.
1 more specialized paper
- HazardWeaver: Scientific Route Selection for Hazard Analysis Agents Wangshu Zhu, Xueqi Cheng, Liang Wu et al.
Other 69
MintFlow: Minimal Trajectory Intervention for Constrained Flow Matching
Forcing samples from a pretrained flow matching model to satisfy constraints, such as observed measurements or physical laws, often pushes them away from the learned data distribution. MintFlow is a training-free sampler that finds the smallest perturbation to an intermediate flow state such that the unchanged pretrained flow then carries it to a constraint-satisfying sample. An adjoint formulation gives this perturbation in closed form, and the intervention time is chosen adaptively. Across generative vision and physical-system tasks, it achieves competitive constraint satisfaction while preserving the pretrained distribution substantially better than state-of-the-art constrained samplers.
MuLoRA: Spectrally Balanced Low-Rank Adaptation for Continual Learning
When low-rank adaptation (LoRA) is used for continual learning, its nominal rank can hide a loss of effective capacity. The authors call this spectral plasticity collapse: update energy concentrates in a few singular directions over successive tasks. MuLoRA uses historical whitening to pick input directions that respond strongly to the current task relative to past tasks, then orthogonalizes momentum updates within that subspace to spread the update spectrum. The authors back the design with spectral bounds. Across five class-incremental benchmarks, MuLoRA achieves the highest mean accuracy on 15 of 16 reported metrics.
Autoregressive Differentiable Method for Integer Programming
The authors solve 0-1 integer programs by fixing an order over the binary variables and training a transformer to predict each next bit while staying in the feasible set. The transformer is first trained on feasible solutions from any existing solver, then refined with a Lagrangian penalty on infeasibility and Gumbel-softmax activations to explore the relaxed objective. On non-convex quadratic knapsack instances, the method shows consistent improvement over state-of-the-art open-source solvers on dense problems with up to 10,000 binary variables. The authors also observe a tunneling-like effect: reparameterizing binary variables as transformer weights lets the search cross barriers in the relaxed objective landscape.
DyRA: Dynamic Residual Approximation for Efficient Matrix Multiplication in DNNs
Structured approximations such as low-rank factorization speed up the dense matrix multiplications in large models, but they approximate the weights, and input activations can amplify small weight errors into large output errors. DyRA (Dynamic Residual Approximation) keeps the cheap structured weight computation and adds an input-dependent, low-rank correction of the residual output error at inference time. The authors show that optimizing low-rank factors of the output gives a closer approximation of the full multiplication for the same compute budget. Across vision, speech, and language models it improves the accuracy-efficiency trade-off, including a 1.5x end-to-end GPU speedup for DINOv3 with more than 3x less accuracy loss than weight-only baselines.
Mapping and Advancing the Scalability-Accuracy Frontier of Nonlinear Causal Discovery
Scaling nonlinear causal discovery to many variables requires both flexible models of each causal mechanism and efficient search over possible graphs, but the accuracy-runtime trade-offs of competing approaches are poorly understood. An empirical comparison of differentiable, amortized, score-matching, and combinatorial methods finds that combinatorial search stays accurate but is slowed by repeatedly recomputing redundant local scores. The authors introduce SPADE, a spline-based scoring scheme that compiles sufficient statistics once and reuses them throughout the search, cutting the complexity of its Gaussian variant from O(nd^3) to O(nd^2+d^3) under bounded indegree. It solves 100-variable problems with 160K samples in seconds and 1600-variable problems in minutes while keeping high structural accuracy on synthetic and real-world benchmarks.
Optimal Planning in a Dynamic World
When feasible states or actions change over time, as with moving obstacles or trains that can only be boarded while stopped, the optimal plan depends on when execution starts, which is often unknown at planning time. The authors define any-start-time planning and introduce the compound arrival time function (cATF), a data structure that encodes the optimal plan as a function of start time. Heuristic graph search algorithms build cATFs by propagating functions along edges instead of scalar costs, and the cATF size is provably at most linear in the problem size. On safe-interval path planning among moving obstacles, replanning agents often fail on hard problems, whereas cATF-based agents can quickly look up the optimal plan once the start time is known.
Refinement Buys Intelligibility, Search Buys Identity: What Test-Time Compute Buys in Masked-Diffusion TTS
Masked-diffusion text-to-speech (TTS) models can spend compute on model depth or on inference refinement steps, and the study asks whether these help all capabilities equally. Training 15 codec TTS models from 19M to 133M parameters and sweeping refinement steps shows that refinement closes 86.2% of the intelligibility gap but only 46.4% of the speaker-identity gap, a 1.86x asymmetry that more training reduces but does not reverse. Best-of-K sampling recovers speaker identity where extra refinement fails. Depth and steps act as separable rather than interchangeable factors, and 62% of the remaining identity deficit is traced to the audio codec rather than the generator.
Below what training size do deep tabular generators stop beating trivial baselines? A preregistered benchmark on a size ladder of clinical and standard datasets
Deep tabular generative models are usually benchmarked on tens of thousands of rows, but clinical datasets often have only hundreds. In a preregistered benchmark of 2,220 runs, the authors subsample 8 public datasets from 200 to 20,000 rows and compare CTGAN, TVAE, and TabDDPM against trivial baselines such as independent marginals, a Gaussian copula, and SMOTE, scoring each by how well classifiers trained on synthetic data perform on real data. In 23 of 24 dataset and model pairs, no deep model beats the best trivial baseline by more than seed noise at any size, and a preregistered prediction that rankings would be unstable at small sizes was falsified. Rankings on natively small clinical datasets agree only moderately with rankings on subsampled large ones, which suggests subsampling is a poor substitute for genuinely small data.
PoCoFL: POlicy-COmpliant Federated Learning
Federated learning (FL) keeps training data on clients but does not guarantee that clients submit contributions that follow policy, or that aggregators process them correctly. PoCoFL handles the FL type, the policy semantics and the cryptographic realization as separate concerns. Clients prove compliance using commitments and non-interactive zero-knowledge proofs, and aggregators prove that admitted contributions were aggregated according to the chosen policy. The authors formalize and implement vanilla, continual, personalised and threshold-encrypted FL instantiations, showing that the framework is practical and independent of network topology.
60 more specialized papers
- A Manifold-Aware Topic Modeling Approach via Rank-Based Prototypes Thiago C\'esar Castilho Almeida, Daniel Carlos Guimar\~aes Pedronette
- RINS: Residual-Image Neural Subspace Solvers for Large Sparse Linear Systems Zhongyan Ouyang, Weixin Liao, Mingquan Feng et al.
- $\Psi$-Resilience: Model-Free Feature Importance from 1D Topological Signals Fabian Galis, Darian Onchis, Pedro Real Jurado
- Joint Movement and Compression Ratio Design for Mobile Embodied AI Networks (MEAN) Yahao Ding, Jiaxiang Wang, Zhouxiang Zhao et al.
- Mitigating Convergence Collapse in Fixed-Target Anomaly Detectors via Kernel-Anchored Locality Regularization Jos\'e Lucas De Melo Costa, Fabrice Popineau, Arpad Rimmel et al.
- From Behavior to Provenance: Attributing Tabular Foundation Models to Synthetic Pretraining Data Mohamed Bouadi, Nassim Bouarour, Shivam Dubey et al.
- Conformal Prediction for Time Series with Deep Sequence Models Junghwan Lee, Jonghyeok Lee, Yao Xie
- FlashSinkhorn 2: Block-Sparse Entropic Optimal Transport Felix X. -F. Ye, Yu Chin Fabian Lim, Naigang Wang et al.
- Efficient Neural Field Learning via Adaptive Coverage and Focused Sampling Guang Zhao, Xihaier Luo, Huan-Hsin Tseng et al.
- CRISP: A Framework for Clause-Reconstructed Interpretable NeuroSymbolic Propositions Alex Chan, Shafi Muhtasim Chowdhury, Ekin Can Erku\c{s} et al.
- A Generative Model of Complex Networks Using Graphons and Neural Inverse Operators Wooseong Choi, Italo'Ivo Lima Dias Pinto, Chen Sun et al.
- SD-DPC: Sparse Dictionary Differentiable Predictive Control Ali Reza Daneshvar Garmroodi, Jan Drgo\v{n}a
- Threshold-Aware Conformal Routing Shiwei Tan, Huzefa Rangwala, Danielle C. Maddix
- Learning the Latent Structure: A Feature-Centric Approach to Graph Data Augmentation Yu Song, Zhigang Hua, Yan Xie et al.
- BaCP: Backbone Contrastive Pruning for Preserving Representations in Extremely Sparse Neural Networks Mohammad Haroon Khawaja, Muhammad Haseeb, Mohammad Fatim Shoaib et al.
- Neuron merging via inverse-activation regression for post-training compression of sigmoid neural networks Ao Kuniya, Jun Ohkubo
- Learning Closure of Dynamical Systems with Kernel Ridge Regression Evan Habbershaw, John Harlim, Senwei Liang
- How Causality Bridges the Semantic Gap Shuhao Zhang, Xuran Zhou, Han Guo et al.
- Time Series Forecasting Benchmarks Need Scenario-Grounded Stress Testing Yuyang Zhao, Lian Xu, Hao Xue
- Seer: Maximum Likelihood Regression for Learning-Speed Curves Carl Myers Kadie
- Where Quantum Fourier Sampling Stops Short: A Three-Gate Audit Protocol for Delay-PUF Security Models Owen Friedewald, Ali Shiri Sichani, Chi-Ren Shyu
- When Normalization Selects the Sign: Auditing Robustness Ablations in Quantum Attention Owen Friedewald, Srikar Alla, Ali Shiri Sichani et al.
- RAOA: Alternating-Operator Neural Computation with Programmable Radio Propagation Toshiaki Koike-Akino
- Label-Efficient Time Series Classification at Scale: A Dual-Stream OSSE-LSTM with Counterfactual Attribution Nguyen Ho, Bach Tung Tran, Trung Ky Nguyen et al.
- Inner Momentum for Differentially Private Muon Bishnu Bhusal, Minh Vu, Ben Southworth et al.
- Jumping up and down: Denoiser diffusion models for discrete ordinal data Yair Shenfeld, Ricardo Baptista, Stefano Peluchetti
- Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts Yue Hou, Ruomei Liu, Yingke Su et al.
- ROUTEAUDIT: Interaction-Aware Identification for Budgeted Multi-Verifier Routing Miaobo Hu, Shuhao Hu, Xiaobo Guo et al.
- Adaptive Spectral-Koopman Dynamics Modeling for Temporal Domain Generalization Tengxue Zhang, Yu Ke, Yang Shu et al.
- Counterfactual Action Evaluation, Observation Bottlenecks, and Representation Geometry in Joint-Embedding Predictive World Models Arjun Subramanian
- On Unlearning for Time-series Forecasting Zeyu Shi, Yanhui Luo, Ziming Hong et al.
- Distributionally Robust Survival Models under Subpopulation Shift and Outlier Contamination Seonghwi Kim, Sung Ho Jo, Minwoo Chae
- Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties Xiaojing Du, Jiuyong Li, Lin Liu et al.
- Tangent Schr\"odinger Bridge Matching: Learning Stochastic Transport with Mechanistic Sensitivities Jowaria Khan, Elizabeth Bondi-Kelly
- Dirac-Interconnected Neural Elements: Discovering Modularity in Physical Systems Without Reduction Reiho Li, Razmik Arman Khosrovian, Takaharu Yaguchi et al.
- Differentiable Koopman Operator for Contrastive Learning on Dynamic Graphs Md Abrar Jahin, Taufikur Rahman Fuad, Md Rizwan Parvez
- Adaptive Second-Order Solvers for Fast Stochastic Diffusion Sampling Ella Kemperman, Luca Ambrogioni
- RIPPLE in Still Water: Zero-Shot Clustering in Federated Learning with Wavelet Scattering Transform Alessandro Licciardi
- When Does Synthetic Relational Data Teach Models to Use Relations? Tracing Predictive Structure from Pretraining Data to Model Behavior Shivam Dubey, Mohamed Bouadi, Nassim Bouarour et al.
- Learn Feasibility Once, Optimize All Objectives: Derivative-Free Diffusion Models for Chance-Constrained Programming Ziwen Liu, Yan Liu, Congying Han et al.
- Exploring the Trade-Off Between Structured Pruning and Fault Tolerance in Deep Neural Networks for Space Applications Toon Vinck, Na\"in Jonckers, Jaro De Roose et al.
- HyperFuse: Fast Self-Supervised Node Embeddings for Attributed Hypergraphs Megha P, Harshit Kumar, Srajan Agarwal et al.
- Evolving Hybrid Quantum-Classical Architectures for Image Classification Devroop Kar, Daniel Krutz, Travis Desell
- WAMpy: Efficient Synthesis of Prolog Programs in Python Dominik Magiera, Lukas R\"ohrig, Frank J\"akel
- SPEAR: A Spectral-Disentangled MoE Neural Operator with Knowledge-Guided Expert Aggregation for Large-Scale PDE Pretraining Dengdi Sun, Xiaoya Zhou, Xiao Wang et al.
- Shrome at Touch\'e: Soft-Vote Ensembling and Counter-Causal Augmentation for Causality Extraction Roham Zendehdel Nobari, Shayan Sooratgar
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- Benchmarking Candidate Coverage in Typed Decision Models Jiawen Lu, Tongtong Wu
- DriftTTS: Few-Step Text-to-Speech Without Distillation via Distribution-Matching Drift Mohammad Nur Hossain Khan, Subrata Biswas, Bashima Islam
- Contrastive Neural Embeddings Reveal Individual Traits Beyond Conversational Role Hubert Huang, Michelle McCleod, Brendan Ames et al.
- AIBL: Augmented Instance-Based Learning with Structured Memory and Neural Embeddings Radha Poovendran, Andrea Stocco, Linda Bushnell
- Becoming Suspicious Across Borders: Algorithmic Extraterritoriality and AI-Driven Financial Surveillance Georgios Pavlidis, Savvas Chatzichristofis, Eleni Gavriil
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Large Language Models 68
IntentCoding: Amplifying User Intent in Code Generation
Large language models (LLMs) often fail to follow fine-grained coding instructions that carry several constraints, and their accuracy drops quickly as the number of constraints grows. The authors find that the user's intent does shift the model's logits, but not strongly enough to steer decoding. IntentCoding is a training-free, model-agnostic decoding strategy that isolates the intent's influence by comparing outputs with and without the intent masked out, then amplifies that influence using an ensemble of amplification strengths. Evaluated on a new constraint-compliance benchmark, CodeConstraints, and on IFEvalCode, HumanEval and LiveCodeBench, it reports up to 71.0% relative improvement on CodeConstraints and up to 29.3% relative improvement in pass@1 over greedy decoding.
Budgeted Cache Repair for Cross-Context KV-Cache Reuse
Cross-context KV-cache reuse predicts a shared text segment's keys and values under a new prefix instead of recomputing them, and earlier work reported no quality loss. The authors find that reuse substantially hurts accuracy on MMLU and GSM8K, and that no rule for deciding whether to reuse a cache removes this cost; choosing which parts to recompute does help, but only at fine granularity. Budgeted Cache Repair (BCR) drafts two tokens from the assembled cache, ranks cache rows by the attention those tokens pay them, and exactly recomputes a fixed budget of rows. It restores GSM8K to full-prefill accuracy while still serving most calls from cache, and its selector outperforms a random-selection control that previous evaluations lacked.
CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache
In long-context decoding, the key-value (KV) cache's memory and bandwidth costs are a growing bottleneck, and existing compression methods treat deciding which states to keep separately from compensating for the ones they drop. Starting from an exact decomposition of eviction error into evicted attention mass times a directional gap, CORE distills an offline allocation that combines query utility with log-determinant coverage into a lightweight indexer. At inference, a single calibrated distribution both selects which KV states to retain and weights how evicted mass is written into a latent memory. Across three backbone models, CORE beats the strongest RULER baseline by up to 3.78 points at 90% compression, with further gains on LongBench.
Hardware-Native Joint Sparse-Quantization for Trillion-Scale Mixture-of-Experts
Trillion-parameter mixture-of-experts (MoE) models are limited by memory footprint and bandwidth, and the semi-structured sparsity supported by GPU Sparse Tensor Cores has been hard to apply to them without large quality losses. The authors jointly optimize sparsity masks and quantized expert weights, relaxing the discrete mask choice into a differentiable form under a router-weighted reconstruction objective, and write a custom grouped sparse matrix-multiplication kernel for low-precision sparse MoE inference. On models from 30 billion to one trillion parameters, the method improves joint sparse-quantization accuracy by up to 4.35 points while keeping 96.09% of the original model's performance, and on NVIDIA B200 GPUs it cuts end-to-end latency by up to 4.03×.
Does Every User Need a Private LoRA? Decoupling Personalization from Per-User Adaptation
Personalized LLMs typically store a full adapter for each user, which scales poorly as the number of users grows. Analyses show that independent per-user LoRA adapters share substantial reusable structure, which motivates LINEUP. It learns a bank of shared low-rank personalization factors, composes them according to the user and the query, and limits per-user adaptation to a tiny correction code. Each user trains only eight scalars, versus 4.19 million per-user parameters for a private LoRA, yet LINEUP leads on all 12 metrics across six tasks, including an 11.4% RMSE reduction on LaMP-3.
Lexicographic Multi-Objective On-Policy Distillation
Reinforcement learning from verifiable rewards (RLVR) usually optimizes only answer correctness, and methods that combine several rewards tend to merge them into one score, so a minor goal like conciseness can be gained at the expense of correctness. LMOPD (Lexicographic Multi-Objective On-Policy Distillation) merges several reward-specialized teacher policies under an explicit priority order. For each student rollout, it picks the specialist for the highest-priority objective that a gate flags as deficient, then projects that specialist's correction to remove any component that works against higher-priority specialists. On three math benchmarks with 30B-A3B mixture-of-experts models, the four-expert version retains about 90% of both the accuracy gain and the reasoning-correctness gain, versus about 57% for the next-best baseline, and ablations credit both the lexicographic routing and the projection step.
Latent-MOPD: Latent Multi-Teacher On-Policy Distillation
Existing multi-teacher on-policy distillation (OPD) for LLMs, in which a student learns from its own generated responses, transfers only the specialists' output distributions. Latent-MOPD also distills the specialists' hidden states without any extra teacher training: it selects late-layer targets based on the teacher–student relationship, bridges different hidden sizes with a shared projection, groups updates by domain, and gradually shifts each teacher's supervision from hidden states to token predictions. In the main same-family setting, it beats token-only, representation-only and uniform-averaging baselines on all nine math, code and logic benchmarks, and the student, which is the same size as each teacher, surpasses the best per-benchmark teacher on most of them. With larger cross-family teachers it still outperforms both single-channel baselines, while a representation-only control collapses when teacher domains are mixed within a single update.
The Surprising Effectiveness of Shared Memory in Looped Transformers
Looped Transformers reuse the same layers several times per token to add compute without adding parameters, but each recursion writes its own key-value cache, so memory still grows with compute. The authors pretrain looped models in which only the first recursion writes a cache, and later recursions read that shared cache while keeping a short local window of their own. At 150M–1B parameters, sharing memory improves quality rather than hurting it: with five recursions, the hybrid Looped Prediction Transformer (LPT) lowers FineWeb-Edu validation perplexity by 1.12–1.82 compared with a same-size standard Transformer while using 76–79% less context memory. Analysis shows that shared and local memory learn different representations, and that the shared cache also acts as a gradient highway back to the first recursion.
Are you Synthesizing or Recalling? Evaluating LLMs on Algorithmic Code Retrieval
For well-known algorithms, the authors argue, code generation is better measured as parametric code retrieval: reproducing a named algorithm from what the model memorized rather than inventing a solution. AlgoREval covers 599 problems spanning 77 classical algorithms, 14 domains, 7 programming languages, and 4 graph-input formats, and the authors use it to evaluate 15 models from 7B to 34B parameters zero-shot. Retrieval accuracy varies substantially across languages and input representations, even for widely documented algorithms. Retrieved code snippets or structured hints help on complex algorithms, SFT gives broader gains across languages, and GRPO gives larger gains in specific languages.
Capability Scaling-Down Laws for LLM Compression
Choosing an LLM compression method is still mostly trial and error, because similar resource savings can cost very different amounts of capability. The authors measure capability loss in math, code generation, and question answering under pruning, quantization, and distillation, then fit simple predictive relations from model size, training stage, compression settings, and data exposure. Sharing the density response across pruning levels halves the measurements needed to fit a pruning predictor, which stays within 0.020 nats per token of a full regression on new Pythia and OLMo-2 states and under Wanda pruning. Choosing a method from these predictions captures most of the available benefit for question answering, but a fixed method priority does equally well there, and the gains for math and code are smaller.
What Does a Token Cost? A Mixture-of-Agents Measurement of Sufficient Per-Token Compute
LLMs spend the same compute on every token they generate, and methods such as speculative decoding and model routing assume much of it is unnecessary, but how much compute an individual token actually needs had not been measured. The authors use a Mixture-of-Agents (MoA) panel of fifteen models of increasing size from three families: each model tries to reproduce a reference sequence token by token, and the cost of the smallest model that succeeds defines that token's sufficient compute. A 0.5B model reproduces 92–95% of reference tokens, and the hardest 10% of tokens account for 64–80% of estimated FLOPs across Qwen, OLMo, and R1-distilled panels. On MATH-500, the resulting map cuts projected routing latency from 7.59 to 5.12 seconds with slightly better accuracy, and it lets drafting use 32.6% fewer draft tokens than fixed-window drafting.
Right Order, Wrong Scale: Auditing LLM Judges for Occupational AI Measurement
LLM judges are increasingly used to decide whether AI outputs meet workplace requirements, but agreeing with humans on rankings does not mean agreeing on how many outputs are acceptable. The authors build O*NET-BENCH from 45,796 worker ratings and evaluate 33 judge configurations across six model families. Most judges rank responses reasonably well, yet a simple TF-IDF baseline nearly matches the best one, and judges estimate that anywhere from 3.0% to 97.9% of responses are acceptable versus 61.1% for workers. Calibration removes most of the mean bias but explains at most 8.5% of individual rating variance, so the authors conclude judges must be validated against the acceptance rates and aggregates they will be used to estimate.
Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting
Code generated by LLMs is usually judged on correctness, not on how much energy it uses when it runs. The authors screen 21 prompting strategies for energy-efficient code and evaluate the 8 most promising across 10 open-weight and proprietary models in Python and C++. Averaged across models, the best strategies reduce energy use by up to 25% for Python and 17% for C++. Individual models gain much more: up to 50% for Python and 56% for C++ with Granite-4.0-H-Small, and 39% for Python with Claude 4.5 Haiku. How well each strategy works varies widely by model and language.
Labels Override Definitions in Jev-Style Typed Decision Models
Typed decision models, used for routing, moderation, and triage, score a set of options, each defined by a short label and a written definition. The question is whether the scores follow the definitions or the labels. Across four open-weight models, eleven classification tasks, and a new routing suite called PolicyBench, the answer is mostly the labels. Deleting every definition leaves accuracy unchanged, and renaming the options to A and B raises accuracy by about 15 points. The authors trace the bias to a single prompt-rendering expression ("{label}: {definition}") rather than the model's constrained decision head: changing that expression removes the effect in laya models and creates it in von. They also give a two-call diagnostic practitioners can run on their own models.
Fisher-Guided Submodular Data Selection for Continual Pre-Training of Large Language Models
In continual pre-training (CPT) of LLMs, a poorly chosen domain corpus can overwrite what the model already knows. The authors show that selecting data by loss shrinks the Fisher information on exactly the parameters the pretrained model relies on most, which points to a parameter-space mechanism for catastrophic forgetting. Their selector splits each candidate's gradient into an "anchor" part, which perturbs committed directions, and a "frontier" part, which uses free capacity, then picks data with a streaming log-determinant submodular objective. In medical CPT on TinyLlama-1.1B and Llama-3.1-8B, 1B selected tokens beat a forgetting-aware replay baseline trained on 10B tokens on both domain adaptation and forgetting.
Activation Sparsity with Weight Approximation for Faster LLM Decoding on Offloaded Weights
When an LLM does not fit in GPU memory, decoding slows down because weights are repeatedly transferred from CPU RAM or flash storage. Activation sparsity reduces those transfers by skipping weights tied to near-zero activations, but quality collapses if too many are skipped. SpAx gives each weight three options instead of two. It skips weights for activations closest to zero, reads compressed approximate weights for small activations, and reads full weights for large ones; the small activations damp the approximation error. With weights offloaded to CPU memory, it speeds up decoding 3.86x on average with 16-bit weights and 2.06x with 4-bit weights, with at most a 10% increase in WikiText-2 perplexity. Offloading to flash yields 3.31x and 1.54x.
Context-Tower Conversion Preserves Generation While Freezing Retains Knowledge: Low-Budget AR-to-Diffusion Conversion of MoE LLMs
Converting a pretrained autoregressive (AR) model into a diffusion language model allows parallel generation, but published conversion methods have never been compared under a common protocol. The authors convert the same 30B Mixture-of-Experts (MoE) model two ways with the same data and token budget. One updates the parent's weights in place; the other conditions on a frozen causal copy of the parent through cross-attention. With 1B training tokens, the frozen-tower model scores 71.60 on HumanEval pass@10 versus 6.19 for in-place conversion, while retaining 95% of the parent's GSM8K score and 99% of its MMLU-Pro score. They also show that the evaluation protocol substantially shifts a published 500B-token conversion's scores, so diffusion models need to be compared under a shared protocol.
Large Language Continuous Diffusion Models
Discrete diffusion language models decode in parallel, but their non-smooth, high-dimensional space makes it hard to steer generation trajectories. Sigma is a large-scale (3B and 8B) continuous diffusion language model that denoises Gaussian-corrupted token embeddings blockwise along low-dimensional trajectories governed by ordinary or stochastic differential equations, while also learning the embedding geometry. It is warm-started from autoregressive model weights. With classifier-free guidance and score temperature, it matches discrete diffusion and autoregressive baselines on GSM8K, Minerva, HumanEval, and MBPP, and on MATH-500 and AIME after fine-tuning. Steering in embedding space controls the quality-diversity tradeoff for strong pass@k, and quality degrades gracefully with fewer function evaluations, which also enables efficient distillation.
Spend Teacher Tokens Where They Matter: Success-Referenced On-Policy Distillation
On-policy distillation (OPD) gives student rollouts dense token-level teacher feedback, but supervising every rollout is computationally expensive. SR-OPD (Success-Referenced On-Policy Distillation) targets prompts where the student produces both successful and failed rollouts, and prioritizes failed rollouts whose hidden-state trajectories diverge most from a successful reference while accounting for teacher-input cost. Across three teacher-student pairs and six math reasoning benchmarks, it uses only 3.46-5.02% of the teacher-input tokens of vanilla OPD while maintaining comparable reasoning performance.
Test-time Calibration Learning for Large Language Model Reasoning
Prior methods that train LLMs to verbalize calibrated confidence through reinforcement learning (RL) need ground-truth labels, which are unavailable at test time on new tasks. TTCL (Test-Time Calibration Learning) derives self-supervision for both correctness and confidence from multiple sampled responses, adapting accuracy and calibration on unlabeled target-task data, and is shown theoretically to be a bounded surrogate for the ideal calibration objective. On base models across eight math and factual question-answering benchmarks, it gives an average relative accuracy gain of 40.13% and a 70.80% reduction in expected calibration error (ECE), and it also helps already-calibrated models under domain shift.
MuonIO: Principled Norm-Aware Descent for Embedding Tables and Language Model Heads
Standard Muon optimizer implementations fall back to AdamW for the embedding table and language-model head instead of applying a norm-derived update to them. MuonIO gives both layers a single Muon-style rule, motivated by the 2-to-infinity operator norm for the head (from softmax output geometry) and the 1-to-2 norm for embeddings (from one-hot inputs), which reduces to column normalization for embeddings and row normalization for the head. In 1B LLaMA pretraining on C4, it halves input/output-layer optimizer state memory and cuts their update FLOPs by about 46% versus Muon while also improving validation perplexity.
WakeKV: Reactive, Reversible KV Residency for Heads That Change Their Minds
Most key-value (KV) cache compression methods classify attention heads once and keep that classification fixed, but measurements across three models and three long-context regimes show that most heads change their reading behavior at least once during generation. WakeKV therefore moves cooling heads to a recoverable CPU reservoir instead of freezing or permanently evicting their state, so they can be restored when needed. At matched memory it lowers miss rates relative to frozen classification, destructive eviction, SnapKV, R-KV, and ReasonAlloc, and a vLLM-based implementation on Mistral-7B improves throughput while retaining LongBench quality.
TPBench: A Turning-Point Benchmark for Dialogue Compression
Dialogue compressors can keep a conversation's facts but drop the turn where the user changed them, such as a corrected price or reversed choice. The authors call this turning-point eviction and note that a single retention score hides it. TPBench tests three targets separately at shared retention budgets: the user's initial goal, the current value of a revised slot, and both together. Answers come from existing MultiWOZ and SGD annotations. At a retained fraction of 0.30, every tested compression method falls below full context on the joint probe, and deleting the turn that carries the update sharply lowers current-value accuracy. On LongMemEval-KU and Chinese RiSAWOZ, full context is most accurate and simple recency is the strongest compressed method.
Dynamic LLM Routers are Often Misguided
Dynamic LLM routers aim to cut inference cost by sending each query to the cheapest model that can answer it. Across six commercial routers in 14 settings on an eight-category benchmark, none beats a router that randomly picks between two well-chosen models at matched cost, and some fall short by more than 10 percentage points. The authors trace the gap to four patterns: ignoring query difficulty, preferring shorter queries over longer ones, routing by a query's apparent source rather than its difficulty, and poorly chosen model rosters. They show the first three are rewarded by standard cost-accuracy Pareto evaluation. They propose an evaluation method that does not reward these patterns and a simple two-model router that avoids them, though its gain over random routing remains small.
Exact Memory-Time Optimization for Prefix-Cached Language Model Serving
Keeping cached prefix states when serving language models trades recomputation against storage time. Optimizing each cached block independently overcounts savings, because a block is only usable if its preceding prefix is also cached. Prefix-Certificate Retention (PCR) formulates timeout-based retention exactly as a maximum-weight closure problem that reduces to a single minimum cut, with graph size linear in the number of lookups. The authors also give a dynamic program for ordered timeouts. In a replay of 39,632 public Mooncake requests, ordered timeouts match the unrestricted optimum in 118 of 120 cases. The experiments measure usable cached blocks and storage time, not GPU latency.
Improving Atomic-Fact Recall via Focused Views in Unstructured Knowledge Editing
Unstructured knowledge editing (UKE) updates a large language model's knowledge from free-form passages instead of structured fact triples, but edited models often only reproduce the whole passage and cannot recall its individual facts without that context. The authors trace this to context-induced difficulty underestimation: under the standard passage-level objective, later facts sit on more ground-truth context, start with lower loss, and look easier to learn than they are. Their fix, FOVEATED, is a plug-and-play method that randomly shifts the Rotary Position Embedding (RoPE) positions of the preceding context's keys during editing, creating a "focused view" of each sentence. The perturbation is removed after editing, and the method yields consistent improvements across five editors, two LLM backbones, and three benchmarks.
PAPER2LLM++: Continual Self-Evolution of LLMs from Research Papers
Research keeps finding limitations in LLMs, but models do not automatically learn from published work about their own failures. PAPER2LLM++ treats the incoming research literature as a stream of supervision. For each paper, it extracts evidence-grounded findings, tests whether the reported limitation still appears in the current model, and if so turns the findings into candidate training signals. A try-evaluate-commit procedure keeps an update only if it improves the targeted behavior without substantially forgetting earlier fixes or hurting general capabilities. On a sequential stream of failures discovered by research, models progressively absorb new findings while retaining earlier gains.
BitNest: Bit-Nested Speculative Decoding for Memory-Efficient LLM Inference Acceleration
Speculative decoding speeds up generation by having a cheap draft model propose tokens that the full model verifies in parallel, but a separate draft model or weight copy costs memory that edge devices may not have. BitNest builds a strong low-precision model first and then recovers the higher-precision target through residual refinement, so draft and target share one physical set of weights. The same progressive-precision design is applied to the KV cache for long contexts. Across several 7B–8B models, it reaches an average 95.2% draft acceptance rate and a 1.48–1.61x end-to-end speedup over FP16 decoding while closely preserving full-precision quality.
iS-KV: Online Low-Rank KV Cache Compression via Block-Incremental SVD
Long chain-of-thought reasoning makes the KV cache grow linearly with output length, and token-eviction methods that control this growth can discard states that later reasoning needs. iS-KV instead compresses older states into a low-rank form with block-incremental singular value decomposition (SVD) during decoding, while keeping a recent window exact. The authors find that updating the basis without also re-expressing old tokens in it causes stored history to drift badly, so iS-KV synchronizes historical coordinates whenever the basis changes. On DeepSeek-R1-Distill-Llama-8B, it reaches 82.6% accuracy at 4.06x compression versus 83.6% uncompressed, reaches 89.2% on Qwen3-8B at 5.64x, and beats token-eviction baselines at matched memory budgets.
Gated Slot Attention-2: Two-Sided Associative Memory Correction in Linear Attention
Linear attention models are efficient but struggle to manage their fixed-size recurrent memory. Recent work improves this in two separate ways: delta-rule variants that precisely correct the value stored for a key, and slot-based designs such as Gated Slot Attention that keep separate key and value memories. The authors combine the two. They introduce a Gated Oja Rule (and an extended Gated Oja Rule-2) to correct the key side, and pair it with Gated Delta Rule-2 on the value side through shared latent slots in Gated Slot Attention-2 (GSA2), along with a hardware-efficient chunkwise algorithm for parallel training. GSA2 consistently improves over strong linear-attention baselines while keeping linear-time training and constant-memory decoding.
Evaluating LLM-as-a-Judge Beyond Score Alignment: A Psychometric Analysis of Residual Judging Difficulty
LLM judges are usually validated by how well their scores agree with human scores overall, which says nothing about whether humans and LLMs struggle with the same cases. The authors fit Many-Facet Rasch Models to human and LLM ratings on SummEval, which separate summary quality, rater severity, dimension severity, and rating-scale thresholds. From this they define residual hardness, a model-adjusted measure of how hard a case is to judge. Across 17 open-weight LLM judges, moderate agreement on summary quality does not mean agreement on which cases are hard, and the mismatch depends on the dimension: consistency is harder for LLMs, while coherence is harder for humans. Cases that are easy for humans but hard for LLMs can be partly predicted from observable properties of the source and summary, which points to more targeted human-LLM collaboration.
LUMOS: Tracing Parametric Knowledge from Training Data to Behavioral Outputs in LLMs
Studies of what an LLM knows usually look only at its outputs, so they cannot tell genuine generalization apart from memorization of training data. LUMOS traces knowledge from verified training-data exposure to behavior, using OLMo 2 because its training corpus is fully public. Models encode rare facts internally with high separability (84%) but express them correctly only 54% of the time, a gap that narrows with scale. When asked to judge their own answers, models are reliable on content they were trained on (83%) but fall to near-random accuracy (49%) on unseen content, and chain-of-thought prompting inflates their confidence rather than improving calibration.
Constraint-Aware Training
Constrained decoding for code generation already uses program analyses to block tokens that break syntax, scope, or typing rules, yet standard training still spends model capacity learning to suppress those same tokens. The authors define a constraint-aware training objective that leaves these analyses to the decoder, and they prove three theorems about the resulting savings in model size and data efficiency. In a controlled synthetic experiment, constraint-aware training gives lower prediction loss than ordinary cross-entropy training at the same parameter count and data. They argue that training objectives should incorporate the analyses used at generation time.
Output Language Confusion under Multilingual Prompt Contamination
Multilingual Distractor Interference (MDI) is an evaluation protocol that tests how factual QA breaks down when an irrelevant foreign-language sentence comes before the question. It covers five instruction-tuned LLMs on TruthfulQA and TriviaQA under eight distractor conditions, 40,000 evaluations in total. The central finding is a metric confound: with a Hindi distractor, 58% of Llama-3.1-8B responses switch to Devanagari script, which inflates exact-match hallucination to 0.710, but manual review lowers the adjusted semantic hallucination rate to 0.470 because most script-switched answers are still correct. The other models respond by abstaining more rather than hallucinating more, and a paragraph-length English distractor causes near-universal abstention, a risk for multilingual retrieval-augmented generation pipelines.
Dynamic Expert Pruning for Multi-Agent Systems
Mixture-of-Experts (MoE) models activate only a few experts per token but still need every expert in accelerator memory. Existing expert pruning applies one fixed mask to every request, which fits poorly in multi-agent systems where a single backbone serves many tasks and roles. Dynamic Expert Pruning (DEP) builds on the finding that an agent's system and task prompts alone are enough to identify the experts it needs. A lightweight predictor, trained once on workflow transcripts, turns those prompts into a per-request mask in a single forward pass. DEP beats static pruning and merging baselines across tasks, model scales, and MoE architectures, generalises to unseen workflows, and its advantage is largest when only a few experts are kept.
SlimKV: Joint Token-Feature KV Cache Compression with Reconstruction-Free Beacon Attention
KV-cache memory limits long-context LLM serving. Token-wise compression risks losing information, and feature-wise compression often has to reconstruct full-dimension keys to apply rotary position embeddings (RoPE), which limits decoding speedups. SlimKV compresses long contexts into beacon memory states with low-rank latent KV representations and allocates rank per layer. It exploits a finding that removing key-side RoPE hurts beacon tokens much less than raw tokens, so attention can run directly in latent space without reconstruction. On LongBench, it outperforms baselines at 16x and 32x compression and retains over 96% of the uncompressed score at 4x and 8x. At 128K context it delivers up to 7.34x attention speedup and 3.38x end-to-end decoding speedup.
AvoKV-E: Payload-Aware KV Cache Eviction for Long Reasoning
In long-output reasoning, the KV cache (the stored attention keys and values) is dominated by the generated trace, and existing eviction methods mostly ask whether an old key is likely to be read again. AvoKV-E is a training-free eviction policy that first waits before allowing recent states to be evicted, since later queries may not have read them yet. It then ranks eligible entries by normalized read pressure, key redundancy, and the size of their value payload, so that rarely attended entries whose removal would change predictions are kept. At matched cache budgets, AvoKV-E matches or beats redundancy-aware, recurrence-based, and thought-adaptive baselines across models and datasets, with its largest gains under the tightest cache budgets.
Tailoring the Quantization Space for 1-Bit KV Cache Compression
Storing the key-value (KV) cache takes a lot of memory during long-context LLM inference, and existing vector quantization (VQ) methods lose much of their accuracy when compressed to 1 bit. TaSQ reshapes the space being quantized using three techniques: query-guided channel weighting, cross-head normalization, and covariance-aware channel grouping. These transforms are compatible with rotary position embeddings (RoPE) and can be merged into the projection weights and codebooks, so the standard VQ lookup is unchanged and serving overhead is negligible. Across general, long chain-of-thought, and long-context retrieval benchmarks, it beats other low-bit KV cache VQ baselines, and on a single RTX 6000 Ada GPU its SGLang implementation supports up to 14× larger batch sizes and 1.87× higher peak throughput than BF16.
The Geometry of Knowledge Accessibility in Large Language Models
LLMs often cannot reliably recall knowledge they contain. The authors study this property, which they call knowledge accessibility, through the model's representation of the query before any generation. They find a simple geometry: accessible queries lie closer to a center in representation space, and accessibility falls steadily with distance from it. This ordering transfers across datasets and tracks accessibility more closely than reasoning difficulty. The geometry also indicates which intervention helps: query rewriting for accessible queries, chain-of-thought near the boundary, and retrieval beyond it, which makes distance a usable signal for adaptive inference before generation.
HARPO: Hallucination-Aware Reinforcement Learning for Faithful and Creative Language Generation
HARPO is a reinforcement learning framework that trains LLMs to reduce hallucination without giving up creative writing quality. It uses a Hallucination-Aware Generative Reward Model (HA-GRM), trained with verifiable feedback, which judges both faithfulness and writing quality. A selective activation mechanism grants writing rewards only to outputs judged hallucination-free, and a curriculum shifts training from creative writing toward hallucination-focused tasks. The Qwen3-4B-based HA-GRM reaches 78.08% response-level F1 on RAGTruth, versus 66.37% for supervised fine-tuning. Applied to Qwen3-4B, HARPO cuts the hallucination rate on MultiHopRAG from 3.29% to 1.02% while raising the Arena-Hard-v2.0 creative-writing score from 16.95% to 27.54%, though the hallucination rate is itself judged by HA-GRM.
MintEval: Do LLMs Implement the Trading Strategy You Asked For? A Behavioural-Equivalence Benchmark for Natural-Language-to-Strategy Code
Large language models increasingly write the code that runs trading strategies, and a failure there is silent: the code runs and the backtest plots, but the risk logic is not what the trader described. MintEval builds reference strategies from composable building blocks, turns them back into informal trader instructions, and has models re-implement them. The generated and reference programs then run bar by bar on identical BTCUSDT data and are scored on whether their actions match, not on profit or code similarity. Low-cost models reach a mean ActionMatch of at most 0.544, and Claude Opus 5.5 reaches 0.889 but still fails silently on 27.5% of tasks. The LLM judge from an earlier strategy-generation benchmark accepted every one of these silent failures.
Zephon: Elastic Determinism for Online, Stateful Foundation Model Data Loading Pipelines
Ablations in foundation model training are only meaningful if every run sees the same sequence of training batches, even when the GPU topology, checkpoint-resume cycles or processing backends change. That is hard when tokenization, packing and data mixing happen online as stateful many-to-many transformations. Zephon is a data loader that splits the global data stream into topology-independent lanes, serializes ordering decisions while running stateless work in parallel, and checkpoints only the bounded in-flight state. On text and vision-language workloads it reaches competitive throughput while providing deterministic batch sequences across topology changes with resumption cost that does not grow with training progress, a combination no existing loader offers for online, stateful pipelines.
Emergent Structure in the Marginal Attention Space of Language Models
The authors average post-softmax attention weights over query positions to build a "marginal attention space" indexed by token and head, and they study it across more than 60 LLMs. Reduced over tokens, it gives a text-specific signal that is conserved across models, which the authors link to the network's input-output Jacobian and support with a proof under a smoothness assumption. Reduced over heads, it gives a model-specific signature that is stable across documents. In practice, a per-head key-value (KV) cache budget computed once offline on pretraining text, combined with a training-free token score, is competitive with eviction methods that recompute or train the budget for each document or target.
KV$^2$: A Self-Refining KV Cache
Key-value (KV) cache memory limits long-context models, especially when one prefilled context must serve many later queries and has to be compressed without knowing those queries. KV² uses a cheap proxy scorer to pick informative tokens, then reprocesses only that subset to compute final eviction scores, avoiding a costly pass over the full context. Its advantage over baselines grows as the memory budget shrinks: on RULER 16K at a 2% KV-cache budget it beats the next-best method by more than 40 percentage points. On LongBench it reaches the highest average across 2%–10% budgets while using less compression-stage runtime and peak memory than full-context reconstruction.
Predicting and Repairing Merge Collapse in Large Language Models
Merging fine-tuned LLMs by averaging their task vectors sometimes collapses performance far below the base model, and common merge operators give no warning beforehand. The authors show that interference, measured as the variance of task vectors across specialist models, yields a pre-merge score that separates destructive merges from harmless ones across 22 configurations in four model families, while sign-conflict statistics turn out to be anti-predictive. Of 14 merges predicted before evaluation, 12 predictions were correct. Their operator PRISM averages the task vectors and then soft-thresholds each layer according to its interference; without data or tuning, it keeps all five destructive merges within evaluation noise of the base model, where plain averaging drops at least 14.4 points.
Learning a Fact Is Not Learning How to Retrieve It
A model that learned "The capital of X is Y" may still fail to answer when the same fact is requested in a different form, such as "The capital of X:". In controlled two-stage training, a model that had earlier seen facts in five request forms later retrieved newly learned facts across forms much better than a model trained only on statements, even though both received identical training on the new facts. The authors trace this to the hidden state just before the answer, which they call the context state: steering it at test time can enable or block retrieval of facts the model already knows, and the effect transfers across facts and relations. Changing the context state only during training also changes later retrieval, showing that retrieval depends on earlier experience with request forms, not just on learning the fact.
Multi-Task Evolution for Zero-Shot Cross-Problem Generalization using LLMs
LLM-driven automated heuristic design for combinatorial optimization usually needs evaluation feedback from the target problem, which makes it hard to generalize zero-shot to new problem definitions. MECo evolves task-conditioned heuristic populations across multiple source problems and uses a cross-task transfer gap to decide how populations exchange and recombine heuristics. It then selects a compact heuristic set that is applied to target problems without further search. Across 32 vehicle routing and flexible job-shop scheduling variants, it achieves the lowest mean costs among eight baselines, beats the strongest baseline out of domain, and improves other heuristic-design methods when combined with them.
Preserving Mathematical Reasoning in Compressed Diffusion Language Models via Trajectory-Aware Low-Rank Approximation
Compression for diffusion language models (dLLMs) is usually calibrated on clean, fully visible activations, while inference actually passes through partially masked intermediate states. The authors define a low-rank compression objective averaged over corruption levels and masking patterns along the generation trajectory, and they estimate it with Traj-MC, a Monte Carlo method with exact sampled-state optimality and population consistency. At matched compression budgets, trajectory-aware calibration reconstructs generation states better and preserves substantially more mathematical reasoning than clean calibration on math benchmarks.
SyntaxBench: A Statistical Diagnostic Framework for Character-Level Reasoning in Large Language Models
Character-level reasoning in language models is usually judged by isolated probes and aggregate accuracy, so SyntaxBench offers a diagnostic benchmark with statistical tests in place of single scores. It has five core tasks: character counting, letter containment, palindrome detection, edit distance, and longest-string selection. Each uses paired English and length-matched random-string inputs, and a harder substring-extraction stress test called index_to_span is added. The authors evaluate eight open-weight models from 2B to 32B parameters in 11 reasoning-mode configurations, using paired McNemar tests, bootstrap confidence intervals, and tokenization analysis. Tokenization strongly shapes accuracy, and thinking mode can hurt: Qwen3.6-27B drops from 0.952 to 0.886 on palindrome detection. index_to_span remains largely unsolved, with a best exact-match accuracy of 6.75%.
Multilingual GSM-Symbolic: What determines capability transfer across languages?
To study what governs how model capabilities transfer across languages, the authors introduce Multilingual GSM-Symbolic, a dataset of 30,000 item-matched math question-answer pairs in 15 languages. Its symbolic templates can generate many variations to avoid saturation and overfitting. Jointly estimating the determinants of performance shows that model size, language resource level, reasoning, and typological distance matter most. The effects can be expressed in terms of each other: a 32B model evaluated in Marathi performs like a 10B model in English. Larger models and reasoning narrow the gap for low-resource languages but help little for typologically distant ones. The framework predicts a model's performance on an unseen language to within 6.0 percentage points.
Jumping the Line: Exploiting Length Predictions in LLM Scheduling
Large language model (LLM) servers increasingly use size-based scheduling policies such as Shortest Job First, which depend on predicted output lengths because true lengths are unknown before generation. JIL is an attack on the TRAIL scheduler that optimizes an adversarial suffix so a lightweight output-length probe underestimates a request's length, pushing the request up the queue. Across two datasets and four LLMs, it cuts predicted lengths by up to 83.4% and makes adversarial requests complete up to 1.53x faster in end-to-end serving, even though actual output lengths change much less, with varying costs to response quality. Grouping length predictions into coarse intervals on the scheduler side reduces the attacker's advantage and limits delays to benign requests.
OptiSelect: How does the Optimizer Shape Data Curriculum?
Online data selection speeds up LLM pretraining by training on the most valuable examples in each batch, but it usually ignores how the optimizer transforms gradients before updating parameters. OptiSelect formalizes optimizer-aware selection and proves that the gain from selection depends on how well the optimizer-induced utility scores discriminate between candidates. The sign-based and orthogonalizing updates of Lion and Muon suffer a discriminability collapse that caps the gains, while diagonal-adaptive optimizers such as AdamW and Sophia have strictly better upper bounds. The theory also gives the optimal candidate oversampling ratio, and pretraining runs at 124M and 720M parameters show that AdamW-style scoring remains the strongest choice even when Muon is the training optimizer, with benefits that hold under data rephrasing.
Divergence controls entropy in distillation
The authors study how the choice of divergence in a distillation objective determines the student's entropy. They prove that forward KL divergence always raises the student's entropy above the teacher's, and since cross-entropy training is a special case, they verify the resulting identity in pretraining and supervised fine-tuning. Reverse KL lowers entropy until the student-teacher gap becomes too large, and the lower entropy of on-policy distillation comes from token-level reverse KL, not from on-policy sampling. The divergence therefore acts as an implicit entropy regularizer, and in self-distillation the best hyperparameters are those that offset the entropy drop caused by conditioning on privileged information.
Objects Without Morphisms: What LLMs for Mathematics Do Not Represent
The authors test whether large language models (LLMs) really represent the relationships between mathematical theories. They use a task with no external checker: translating statements between the vocabularies of neighbouring subfields while keeping the level of generality correct. A blind coding instrument with a planted-positive control was applied to seven models from four families. When translating toward the more general framing, models widen the claim's scope in 60.6% of rewrites and never narrow it, and they state the hidden hypothesis that would prevent this only 21.6% of the time. The asymmetry appears in every model, holds on a pre-registered held-out split, and is not removed by prompting the model to state all its hypotheses.
FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs
Synthetic data for training natural-language-to-SQL (NL2SQL) models tends to be too simple and to ignore how ambiguously people phrase requests. FALCON generates realistic, ambiguity-aware NL2SQL pairs using compact open models. It seeds generation with SQL reserved words, uses persona-based prompting for structurally complex queries, and filters by alignment so that complex but valid examples are kept rather than discarded as errors. Models trained on its data increasingly outperform baseline-trained models as query complexity rises, and mixing in a little existing benchmark data recovers performance on simple queries. Because the pipeline is model- and database-agnostic, organizations can generate training data locally without external APIs.
Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models
In masked diffusion language models (dLMs), a few early token commitments during denoising sharply reduce uncertainty over the remaining positions, yet most post-training recipes do not target these tokens specifically. Pivot-SD is an offline self-distillation method that uses an information-gain metric to identify these high-impact commitments, which it calls pivots, and trains only on them. Pivots from successful trajectories get cross-entropy loss, pivots from failed trajectories get targeted unlikelihood loss, and the rest of each failed trajectory is left untouched. With only 200 questions and four rollouts each, it improves LLaDA-8B-Instruct on math and code benchmarks over full-sequence supervised fine-tuning and budget-matched diffusion reinforcement learning baselines.
Language Models that Play Chess and Explain Their Moves
Chess engines play at a superhuman level but cannot explain their moves, while language models (LMs) can explain but play weakly. Queen is a 4B-parameter model that connects a silent expert chess encoder to an instruction-tuned LM through cross-attention, trained first with a question-answering curriculum and then with iterative distillation that works like a natural-language Bellman update: the model analyzes the positions after its top candidate moves and folds that analysis into an explanation of the current position. Over seven iterations it gains over 900 Elo (1782 to 2697), reaching typical Grandmaster level and beating all frontier models on playing strength and puzzle accuracy despite having three orders of magnitude fewer parameters. LM-based evaluations rate its explanations as fluent and close to GPT-5.6-Sol (high) in coherence, and the authors propose the recipe for other domains with expert encoders, such as robotics and computer use.
LESSER: Post-Training Data Selection with Output-Layer Gradients
Gradient-based data selection for large language model post-training ranks candidate samples by how well their gradients align with a small validation set, but computing full-parameter gradients requires a backward pass on every sample. LESSER instead uses output-layer gradients, which need only a forward pass, as a drop-in wrapper for existing selection methods. It cuts feature-extraction compute by 9.7x for supervised fine-tuning (SFT) and 3.0x for reinforcement learning (RL) benchmarks while matching full-gradient selection on downstream tasks. The authors also find that even when the two gradient types rank individual samples differently, they select batches whose gradients are aligned.
10 more specialized papers
- HakemBench: A Turkish Benchmark of Typed Decisions Sait Furkan Teke (ufak AI)
- Student-Guided Teacher Distillation for Efficient LLM Task Routing: Positioning Against Jev-Style System-1 Classifiers Haifeng Wu, Srinivasan Manoharan, Jian Wan et al.
- A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs Mohammed Damom, Muneef Y. Alshawsh, Ashraf A. Naji et al.
- Evaluating Multi-Dimensional Generalization of Large Language Models in Temporal Extraction Tasks Fahmid Shahriar Iqbal, Ritam Dutt, Soumitra Das et al.
- Dense Mixture-of-Experts as a Reparameterized Wide FFN: A Granularity Sweep at Fixed Compute Vu Quang Hoang, Nghia Hieu Nguyen
- A Controlled Audit of Personal AI Memory for Rating Prediction Shivam Gupta
- Adaptive Mutual Distillation for Balanced Multi-Task Post-Training of Large Language Models Baohang Li, Xiaocheng Feng, Yichong Huang et al.
- ConvoDrift: A Multi-Turn Conversational Dataset for Modeling Stylistic Tone Evolution Vihindi Kotalawala, Pamoda Dilranga, Gayani Thoradeniya et al.
- LS-AR: Future-Predictive Latent Steering in Autoregressive LLMs Anubha Gupta, Eduardo Pignatelli
- Author Representation Strategies for Zero-Shot Authorship Attribution: A Comparative Study of LLM-Based and Embedding-Based Approaches Nudrat Habib, Tosin Adewumi, Sana Sabah Al-Azzawi et al.
Theory 45
Drive vs. Decay: On the Training Dynamics of Joint-Embedding Predictive Architectures
Joint-Embedding Predictive Architectures (JEPAs) are prone to representation collapse, which is usually avoided with empirical heuristics. Linearizing early training dynamics reveals a competition between a driving force and a decay effect, and their per-mode ratio predicts how many representation dimensions can emerge. The theory predicts a phase boundary that is confirmed across more than 800 Tabular-JEPA configurations, and it explains predictor scaling, masking ratio, and EMA (exponential moving average) as different ways of shifting this ratio. Guided by the analysis, the ResidualPred predictor biases attention toward the identity at initialization and improves effective rank and downstream accuracy on tabular benchmarks and in I-JEPA pretraining up to ImageNet.
Why Does Adaptive Batching Help LLM Pretraining? A Perspective from Unbounded Variance
LLM pretraining often increases batch size over the course of training, but the theory behind this lacks a solid foundation because standard analyses assume bounded gradient variance. Measurements in LLM pretraining show that variance grows with distance from initialization, which motivates a generalized BG-a noise model with a tunable growth exponent. The authors prove matching lower and upper complexity bounds, showing that increasing the batch size as the iterates move away from initialization achieves the optimal rate. Their adaptive batch scheduler, pretraining OLMo2 models of up to 1B parameters on C4, reaches lower validation loss than both small- and large-batch training while using under 10% of the iterations of small-batch training.
Learning Style, Forgetting Semantics: A Case Study of SFT and RFT on Classification Tasks
The paper asks why supervised fine-tuning (SFT) causes more forgetting than reinforcement fine-tuning (RFT) even when every teacher demonstration is semantically correct. It studies classification tasks where several tokens express the same answer in different styles. Using a tractable linear-softmax policy, the authors split each update into a semantic part and a style part. Both methods make parallel semantic updates, but SFT with a teacher that prefers certain styles drifts in style, while RFT keeps the style balance intact. Under explicit conditions, SFT forgetting has a strictly positive lower bound while RFT keeps zero semantic error, and simulations agree with the theory.
Harnessing LLMs as Agents: What Does It Cost?
LLM agents run inside harnesses that manage bounded context, persistent memory, tools, verification, and retries, but standard notions of model capability do not account for the compute these mechanisms consume. The Language Model Agent Machine (LAM) is a resource-bounded abstraction that holds the underlying model fixed and charges for harness-level resources. The authors prove that LAM execution is equivalent to red-blue pebbling, which carries classical I/O lower bounds over to context–memory traffic, and show asymptotic separations between memory-access interfaces. They also bound recomputation costs and derive a closed-form optimal verification interval via a Young–Daly-type checkpoint law. Experiments with GPT-6 Astra on chained MATH tasks test the communication and reliability predictions.
ENCORE: Exact Non-equilibrium COntrol with Replica Exchange for Diffusion Generation
Inference-time control steers a pretrained generative sampler toward a reweighted target distribution without retraining, using either sequential Monte Carlo (SMC), which is exact but needs many particles, or replica exchange (RE), whose existing corrections are biased. ENCORE (Exact Non-equilibrium COntrol with Replica Exchange) has each replica store its generation trajectory, so upward moves become truncations and the intractable time reversal never has to be simulated. The result is the first exact parallel control method, with proven target invariance. Analysis shows that unguided sequential and parallel control become unstable as time discretization is refined, while guided proposals stay stable. On synthetic targets, biomolecular Boltzmann sampling, and image generation, ENCORE delivers competitive accuracy and diversity and also works with distilled samplers.
Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data
Statistical generalization guarantees for flow-matching generative models are underdeveloped, and existing analyses rely on restrictive assumptions and ignore the low-dimensional structure of real data. The authors derive finite-sample error bounds in Wasserstein-p distance for distributions learned from n samples. The convergence rate depends on the data's intrinsic Wasserstein dimension rather than its ambient dimension, which mitigates the curse of dimensionality. These results require substantially weaker assumptions than prior work and help explain why flow matching succeeds on structured data such as images and molecular geometries.
Learning Query Encoders Can Be Hard Even When Vector Retrieval Is Geometrically Easy
Embedding-based retrieval needs both a document index whose geometry can return the right documents and a query encoder that places queries near those documents. The authors measure the maximum recall a frozen document index could support and find that learned single-vector query encoders often fall far below that ceiling on real benchmarks. They then construct a retrieval task where a small one-hidden-layer ReLU network achieves perfect recall, yet any statistical-query learner needs exponentially many queries to beat random recall. This suggests that learning the query encoder, not the geometry of the index, may be the real bottleneck.
Muon Learns Facts Better: Understanding the Role of Spectral Orthogonalization
The Muon optimizer orthogonalizes the spectrum of matrix-valued updates and performs well at scale, but how this changes feature learning is poorly understood. The authors study a tractable factual-recall task learned by a linear transformer, comparing continuous-time limits of gradient descent, Muon, and Adam. With S subjects and R relations, plain gradient flow learns subject information a factor of roughly √(S/R) later than relation information, while spectral gradient flow (the limit of Muon) shrinks this ratio to a constant and makes errors decay exponentially rather than as 1/(T log T). They also show that the sign-based dynamics corresponding to Adam depend on the choice of token embedding basis, which can remove, enlarge, or even reverse the feature-separation phase.
Training-Loss Guarantees for Muon with Finite-Step Newton--Schulz Orthogonalization
Convergence analyses of the Muon optimizer either assume exact orthogonalization or guarantee only stationarity, leaving open whether its practical five-step tuned Newton-Schulz iteration is enough to reach a target training loss. For a sufficiently wide two-layer ReLU network in the neural tangent kernel regime, the authors prove that Muon with momentum and the finite-step update reaches any target squared loss ε with high probability in O((1-μ)^-1 ε^-1/2) steps, with the required width independent of accuracy and momentum. The key mechanism is that the tuned Newton-Schulz map keeps the update aligned with the momentum buffer while bounding its spectral norm. Experiments below the theoretical width threshold confirm that alignment is maintained and that all runs reach the target loss.
Iterating Consistency Models: Stability, Error Bounds and Noise Schedules
Consistency models (CMs) can generate high-quality samples in a few steps, but adding sampling steps can help or hurt quality in ways existing theory does not explain. The authors model multistep CM sampling as alternating noising and approximate denoising operators and derive a non-asymptotic error bound that separates contraction of the initialization error, driven by large early noise levels, from accumulated approximation error, controlled by small late noise levels. They give explicit constants for strongly log-concave and semi-log-concave targets, plus a second guarantee whose assumptions can be estimated for a trained model. Experiments show the measured contraction and approximation profiles match the predicted functional forms, which suggests a practical way to design noise schedules.
Normal-Form Correlation in Markov Games
Computing normal-form correlated equilibria (NFCEs) in Markov games has been an open problem since Papadimitriou and Roughgarden; recent work handled only weaker equilibrium concepts. The authors give the first efficient algorithm for ε-NFCEs in finite-horizon Markov games with a fixed number of players, running in time polynomial in the game description and 1/ε. The algorithm runs backward induction over auxiliary stage games, solving at each stage for a constant-expectation correlated equilibrium. They prove this refinement is equivalent to NFCEs in Markov games. They also show PPAD-completeness, meaning computational equivalence to finding Nash equilibria, when there are many players or the required precision is exponentially small.
Planning to Learn
Classification can be framed as a policy-gradient problem with an exact, smooth gradient and none of the usual exploration or credit-assignment problems, yet exact policy gradient still loses to cross-entropy, even when both are measured on expected accuracy. The authors argue that exact policy gradient is myopic: it ignores that each update sets the starting point for the next. In this view, cross-entropy is the error an example would accumulate over an infinite learning horizon, while exact policy gradient is the zero-horizon limit. Truncating that horizon at the learning that remains gives the horizon loss, a one-line change that shifts from cross-entropy toward policy gradient as training ends. It improves top-1 accuracy over cross-entropy on MNIST and on ImageNet with ResNet-50, ResNet-101 and ViT-S/16, with larger gains under label noise.
33 more specialized papers
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- Flow Matching for Fast Posterior Sampling in Bayesian Inverse Problems Jan Blechschmidt, Oliver G. Ernst, Moritz Poguntke et al.
- Instance-Dependent Regret for CMDPs with Step-Wise Constraints Qian Zuo, Francesco Emanuele Stradi, Leyang Xue et al.
- High-Dimensional Asymptotics and Dataset Selection for Private Transfer Learning Filip Kova\v{c}evi\'c, Edwige Cyffers, Stefano Sarao Mannelli et al.
- Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning Carl M. Kadie
- Distributed Learning with Selective State Space Models: Architecture-Aware Convergence Analysis Adam Piaseczny, Md Kamran Chowdhury Shisher, Shiqiang Wang et al.
- Differential Privacy of Gradient Descent on Perturbed Objectives Austin Watkins, Raman Arora
- Nearly Optimal Fixed-Confidence Best-Arm Identification with 1-Bit Feedback Khang Luong, Dinh Thai Son, Hoang Ta et al.
- Hold-Out Scoring for Efficient Gaussian DAG Learning Donguk Shin, Byeongguk Kang, Inseol Lee et al.
- To Explore The Strange New World Beyond Data Distribution: System Behavior, Causality Tax, and Non-causal Base Model Xianzhi Zeng, Jiangneng Li, Gao Cong
- When Can We Trust the Matching Principle? Robust Deployment Geometry Under Finite-Sample and Model Uncertainty Vishal Rajput
- Do ResNets Route? Sparse Interaction Experts in Residual Networks Liang Yan, Siying Chen, Kaijie Chen et al.
- Temporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability Ambarish Moharil
- Light Entropic Optimal Transport on Riemannian Manifolds Xavier Aramayo-Carrasco, Petr Mokrov, Alexander Korotin
- Invariance of Clustering Operations in Causal Effect Identification Jani Nyk\"anen, Otto Tabell, Santtu Tikka et al.
- Predictively Oriented Gaussian Process Posteriors Callum Lau, Jeremias Knoblauch, Louis Sharrock
- Hamiltonian locality testing and certification do not achieve the Heisenberg limit Francisco Escudero Guti\'errez, Junseo Lee, Sebastian Zur
- Kernel Singular Value Decomposition with Extension to Multiple Data Sources Xinjie Zeng, Qinghua Tao, Johan Suykens
- Near-Optimal Convex Optimization with Lazy Second-Order Oracles Xinliang Zhang, Lesi Chen, Chengchang Liu et al.
- Information Limits of Low-Rank Approximation Certification Kang Liu, Bohao Qu
- Cordial Learning: Distributed Training with Correlated Data Sarah Shitrit, Ilai Bistritz
- Operator-informed initialization for Fourier features physics-informed neural networks Juan Molina, Paris Perdikaris, Mircea Petrache et al.
- Rethinking Epistemic Uncertainty in Node Classification through Information Growth Emma Meneghini, Francesco Ferrini, Bruno Lepri et al.
- Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity Tatsuya Yamada, Hiroshi Morioka, Yoshinobu Kawahara
- Generalization of Transformer-Based Neural Quantum States via In-Context Learning Zhen Qin, Qing Qu, Alfred O. Hero III
- When Is Accuracy Evidence? A Unified Theory of Generalisation, Validation, and Information Fusion JM Gorriz
- Metropolis-Hastings Dominates Importance Resampling for Policy Composition Alexey Kurennoy, Ramil Yarullin, Fergal Reid
- Get a GRIP, this will be a long TRIP: A Quantifiable Long-Range Framework for Verifying Over-squashing Ferran Hernandez Caralt, Simon Heilig, Adri\'an Bazaga et al.
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- From Mixing to Tearing: Graph Decomposition in Decentralized Optimization via Message Passing Kuangyu Ding, Gesualdo Scutari
Safety & Alignment 39
Keep It CALM: Analyzing the Limits of Global Unsafety in Text-to-Image Generation
Training-free safeguards for text-to-image models often remove a single global unsafe direction or toxic subspace from every prompt. A controlled geometric analysis reveals a consistent coverage-selectivity trade-off: compact unsafe subspaces miss varied unsafe meanings, while broader ones increasingly distort benign prompts that sit close to unsafe topics. The proposed CALM (Counterfactual Adaptive Local Modulation) uses matched unsafe-benign anchor pairs to route each prompt to its active unsafe categories. It then minimally edits only the offending token representations toward the safe side, and the authors report better suppression of unsafe content while preserving benign image quality.
Intent-Hiding Jailbreaks: An Information-Theoretic Framework for Compositional Attacks
Compositional jailbreaks hide a harmful request inside a bundle of benign tasks, and this work formalizes them with information theory. Each task gets an estimated probability of being judged harmful, and auxiliary tasks are chosen so that the bundle's average matches the prior over all tasks, a condition the authors call prior-posterior matching. Exact matching under a bundle-size limit is shown to be computationally hard, and an optimal water-filling solution is derived for fractional weights. Experiments on several open-source models show that compositional queries can elicit target behaviors beyond direct requests, but target preservation tends to drop as bundle size grows.
PowerBench: Measuring Language Model Bias in Power-shifting Requests
PowerBench measures whether language models help some users gain power more readily than others. It separates self-empowerment, disempowerment of others, and power grabbing, and varies the domain, context, scale of the affected party, user nationality, whether the user is an AI agent, and request language across 24 US and Chinese models. Models refuse power grabbing most and self-empowerment least, and refusal of power grabbing rises as the affected party grows from an individual to a whole society. The models are biased toward helping others take power from the US and against helping US users take power from others, and they refuse more often when the requester is an AI agent.
Hop-Decayed Influence: New Vulnerabilities of Structural Auxiliary Indexing in GraphRAG Pipelines with LLM
GraphRAG pipelines build auxiliary structures during offline indexing, such as semantic summaries, hierarchical edges and precomputed scores, that decide what gets retrieved at query time; earlier attacks targeted only individual nodes, edges and triples. The authors identify these schema-level structures as a new attack surface, organize exploitation into a 3S framework (Semantics, Structure, Scoring), and present the Hop-Decayed Influence (HDI) attack, which uses query-aware influence propagation to find high-impact targets and corrupts them after indexing. Against Microsoft GraphRAG and HippoRAG2 on HotpotQA and 2WikiMultiHopQA, HDI reaches 88–94% attack success while modifying as few as 0.016% of auxiliary structures, with each edit affecting up to six queries. The tampered structures remain fluent, system-generated text and evade perplexity and paraphrase defenses more than 99% of the time.
Social bot detection in the age of ChatGPT: Challenges and opportunities
This survey reviews social bot detection now that capable AI chatbots can produce human-like conversations and behavior. It covers current detection techniques and the most prominent real-world applications, and identifies gaps created by AI-generated content. It proposes four research directions: using generative agents to create synthetic data and to test detectors, multimodal and cross-platform detection based on coordination signatures, extending detection to non-English and low-resource languages, and privacy-preserving federated detection models shared across platforms.
Prompted to Discriminate: Generalizing Malicious-Input Probes in the Wild
Activation probes, which read an LLM's hidden state to flag prompt injections, jailbreaks, and unsafe requests, are often sharpened by appending a short classification instruction after the user's turn. The authors test whether the wording of that suffix matters and whether the benefit carries over to attack types the probe never saw in training. They use strict leave-one-dataset-out (LODO) evaluation across 13 safety benchmarks and three model families (Llama-3.1-8B, Qwen3.5-9B, Gemma-4-12B). A classification suffix improves out-of-distribution detection by up to about 4 AUC points, and the gain comes from the classification format itself: even content-free labels match a real malicious/benign criterion, while off-topic suffixes help little. The benefit extends to production pooling probes, though which suffix works best depends on the model and the readout, and it can be served cheaply through a KV-cache fork.
Mitigating Private Data Leakage in LLMs with Whiteout
LLMs often memorize and repeat personally sensitive information (PSI) such as birth dates, phone numbers, and home addresses. Machine-unlearning fixes tend to remove too much, hurt utility and safety, and can be undone by attacks. Whiteout handles an individual's removal request by overwriting their genuine PSI with carefully designed obfuscation samples. Tested on LLMs of several sizes and makers, including a widely used OpenAI model, it prevents disclosure of the targeted PSI with negligible impact on utility and safety and outperforms existing alternatives. It also holds up against jailbreaking, relearning, and quantization attacks.
Evaluating and Improving the Robustness of Large Language Models to Input Sequence Variations
This thesis studies how to measure and improve LLM robustness to adversarial input changes, including prompt injection, backdoors, and manipulation of LLM-as-a-Judge systems. It proposes a generative robustness metric based on Jensen-Shannon divergence and an evolutionary black-box attack, ASA, that reaches up to 73.8% attack success rate (ASR) against LLM judges. It also finds that committees of 5-7 heterogeneous models cut ASR by 47-55 points. For agents built on the Model Context Protocol (MCP), AttestMCP attests tool calls with HMAC-protected packets at under 0.1 ms per call and, together with a Commit Boundary isolation pattern, reduces average ASR on MCPBench from 53.7% to 12.4%.
How to Have a Sensitive Debate: An Instance-Optimal Protocol for AI Debate
AI debate aims to let a limited human judge oversee powerful AI systems by having two AIs break complex questions into simple, directly checkable claims, and complexity-theoretic work has designed debate protocols with correctness guarantees. For the same class of problems with stable decompositions, this paper gives a new protocol that is correct in the worst case rather than on average, and makes honesty a dominant-strategy equilibrium for both debaters rather than a Stackelberg equilibrium. Black-box lower bounds show the protocol is instance-wise optimal: no protocol limited to black-box queries of human judgments can do better. The results come from linking stable decompositions to fractional block sensitivity in query complexity.
Mitigating Social Sycophancy via Pluralistic Preference Optimization
Language models are sycophantic in personal advice: they affirm users far more often than humans do, and there is no ground truth to train against. Pluralistic Preference Optimization (PlurPO) has the model identify and simulate the other stakeholders in an interpersonal conflict. It then trains the model to prefer responses that all of those stakeholders would find acceptable, using only the model's own judgments of its outputs. Across four datasets and four model families, PlurPO cuts endorsement of stated intent to cause harm by 89% on average. It also halves the gap to human endorsement rates on general advice (from 17.8% to 8.0%). Preference data built with an 8B model transfers to a 32B model.
What Is Lost in Post-Training? Default Collapse and the Loss of In-Context Steerability Across Diverse Perspectives
Post-training is known to narrow the views LLMs express by default. The authors show it also weakens their ability to be steered in context toward perspectives they were not trained to favor. In controlled experiments, they fine-tune models toward one side of cultural-value disagreements and track checkpoints throughout training. The favored side increasingly dominates, while the ability to recognize and faithfully adopt the opposing view declines. As an alternative, they propose maximizing reward subject to a prescribed distribution over expressed perspectives, implemented as stance-distribution matching.
CuBEs: Culturally-Situated Behavioral Evaluations and the Limitations of Culture-Blind LLM Judges
Behavioral evaluations of LLMs, for traits such as sycophancy, self-preference, and overconfidence, usually ignore the user's cultural context. CuBEs (Culturally-situated Behavior Evaluations) extends an automated testing pipeline to inject cultural context into test scenarios and their grading, and pairs it with a human-labeled dataset covering 12 cultures. Evaluating 13 open and closed LLMs shows that adding cultural context changes significantly whether a behavior appears. For example, Western-framed political-bias tests capture the American conservative-progressive divide, while non-Western framings surface different axes such as religious and colonial issues. The authors conclude that culture-agnostic evaluations, including those graded by culture-blind LLM judges, miss these shifts.
A GHOST in Long-Horizon Agents: Governance Hazard from Overlooked Safety Constraints across Turns
Long-horizon agents can take an action that violates a safety constraint the user stated many turns earlier, even when nothing adversarial is happening. The authors call this failure mode GHOST (Governance Hazard from Overlooked Safety Constraints across Turns) and measure an 11.5% occurrence rate on GPT-5.5 under benign conditions. They also prove that if the per-step risk of violation does not shrink fast enough, a violation eventually happens almost surely. Their defense, STAR-Guard, restores relevant earlier constraints into context and adds a deterministic audit before each action executes, and it eliminated GHOST events in their GPT-5.5 experiments.
Silent Dissent: LLM Agents That Yield to the Majority Still Represent Their Original Premise
In multi-agent debate, LLM agents often give in to a unanimous majority, and the authors ask whether such agents actually change their internal belief or only their stated answer. Using two-hop factual questions whose never-stated intermediate entity (the bridge) can be read from the residual stream with the Jacobian lens (J-lens), pre-registered tests on four open-weight models found that agents that conformed still internally represented their original bridge in layers below the output, which the standard logit lens rarely detected. Hiding the agent's earlier answer changed conformity sharply, with Qwen3.5-4B giving in on 89% of questions instead of 8%, and steering along the bridge direction restored original answers only in the two Qwen models. Stated consensus in multi-agent debate can therefore overstate real agreement.
On the Chain-of-Thought Monitorability of Looped Language Models
Looped language models (LoopLMs) apply shared transformer layers repeatedly to gain computational depth without adding parameters. This is the first systematic study of whether that extra latent computation makes chain-of-thought (CoT) monitoring less reliable. Across eight MonitorBench tasks, the authors vary loop depth within one model family and compare against non-looped models matched on size, layer count, or effective depth. Deeper loops reduce monitorability on some logic, science, and engineering cue-answer tasks under stress tests, and task difficulty and output length do not fully explain the drop. There is no evidence that looped architectures are systematically less monitorable than matched non-looped models.
RMCW: A Deletion-Robust Watermark Based on Reed--Muller Codes for Language Models
Watermarks for LLM-generated text are fragile under post-processing, and deletion attacks are especially damaging because they shift token positions and break alignment with the embedded pattern. Reed–Muller Code Watermarking (RMCW) embeds Reed–Muller code structure into generated text through a secret-keyed vocabulary partition. At detection time, instead of recovering the whole codeword, it searches for surviving local structure by testing short subsequences for Reed–Solomon consistency with Berlekamp–Welch tests. On C4 and ELI5 with OPT-1.3B and Llama-3.1-8B-Instruct, RMCW keeps strong detection on clean text and matches or beats baselines under deletion and rewriting attacks.
MLCommons Jailbreak Benchmark v1.0
MLCommons Jailbreak Benchmark v1.0 is an end-to-end method for measuring how well language models hold up against single-turn, text-based jailbreak prompts. It pairs baseline and adversarial evaluations of eight open-weight systems, using 264 seed prompts across eleven hazard categories and attacks drawn from the MLCommons Jailbreak Taxonomy. Responses are scored under the AILuminate Assessment Standard v1.4, with human annotation and a calibrated automated evaluator. Across all systems the unsafe-response rate rose from 11.08% to 18.65% under attack, an average Resilience Gap of 7.57 percentage points, and attack effectiveness varied widely by attack category and hazard.
Bounded Reachability & Jailbreak Detection via Contraction-Constrained State Space Models
Safety heads are lightweight classifiers attached to language models to flag harmful inputs before generation, but their formal robustness has received little study. For heads built on state space models (SSMs), the authors prove that certifying a prediction against bounded embedding perturbations with interval bound propagation hinges on one contraction condition, the state transition matrix having an infinity norm below 1; when it fails, the bounds grow exponentially with sequence length. Enforcing contraction with a hinge penalty raises the certified fraction on toxic comments from 41% to 59%, and a contraction-regularized S4 head trained on JailbreakBench transfers zero-shot to AdvBench and HarmBench with detection rates near 0.99. A plain logistic regression on Mamba-130M embeddings detects jailbreaks just as well, so the head's value lies in the formal certification rather than better discrimination.
Misinformation Without Triggers: From Factual Answers to Downstream Decisions
False documents in training data can change a language model's factual answers without any trigger, but it was unclear whether those direct answers predict what the model decides downstream. Using a controlled decision game, Guess the Capital, and a real misleading claim about the 2019-20 Australian bushfires, the authors compare false training with matched truthful controls across eight models. They find an audit gap in both directions: direct probes report the injected fact 95.8-100% of the time while decisions shift by only 1.7-14.4 points, and facts that pass the direct probe still bias decisions. Truthful correction restores the fact but not the decisions built on it, and in the bushfire case models keep asserting arson arrests even after losing the inflated number, showing that a correct factual answer does not guarantee a correct downstream decision.
Frequency Is Not Sensitivity Identifying Safety-Sensitive Experts in Sparse MoE LLM
Switching off a few routed experts in a sparse Mixture-of-Experts (MoE) language model can weaken its safety behavior without any retraining, which makes choosing which experts matter a security question. The usual choice is the most frequently activated experts, but the authors instead rank experts by router-gradient sensitivity, meaning how much the sequence loss depends on the gate weights that select each expert. Across five MoE architectures and two suppression budgets, gradient-based selection reduces refusals more than activation frequency in 24 of 25 conditions, and more than random selection in all 25. In OLMoE, refusals drop from 34 to 9 of 100 malicious prompts with no degraded outputs, which indicates the model genuinely complies rather than producing broken text.
HASTE: Evolving Agent Harnesses Against Emerging Attacks Using Sparse Evidence
Agent harnesses enforce safety constraints on LLM agents, but new attacks appear faster than people can update them by hand, and the available evidence is often just a short threat report or a few attack examples. HASTE is a multi-agent framework that evolves the harness from this sparse evidence by pitting two processes against each other: one generates safety specifications that guide harness updates, and the other generates attack cases that probe for remaining weaknesses. Evaluation results feed back into both processes, so the harness can generalise beyond the attacks it first saw. Across several backbone models, attack types, and forms of evidence, HASTE consistently reduces attack success rates while preserving utility on benign tasks.
Positive-Unlabeled Learning for Agent Safety False Alarm Auditing
Safety monitors for tool-using language-model agents often raise many false alarms, and separating these from genuine alarms takes a lot of manual review. The authors treat false-alarm auditing as a positive-unlabeled (PU) ranking problem, where a few verified-safe trajectories the monitor did not flag are the positives and the alarms are unlabeled. Their framework handles the mismatch between the two groups in three steps: Trust-aware PU Supervision, Reliability-gated Rank Distillation from several PU reference models into one student, and Consensus-guided Structural Refinement. It needs no safety labels on alarms and leaves the monitor unchanged. It reaches a macro AUPRC of 0.6444 and recovers 33.3% more false alarms at a 5% review budget than PULDA, the strongest of the PU baselines tested.
OLMo-Detect: A Multi-Stage, Confounder-Controlled Benchmark for Membership Inference on Large Language Models
Membership inference attacks (MIAs) try to determine whether a given text was part of an LLM's training data, but existing benchmarks cover few training stages and do not properly align or filter non-member samples. OLMo-Detect builds on the fully open OLMo 2 pipeline to cover pre-training, mid-training, and post-training, aligns members with non-members on three axes, and filters non-members against the training corpus using infini-gram. Across 15 unsupervised and 3 supervised MIAs, the best attacks reach an AUC of only 0.68. Detectability peaks at mid-training, a pattern driven by curated math data rather than by the training stage itself. Scores improve from 1B to 13B parameters but plateau at 32B, no unsupervised attack holds up under distribution shift, and the findings carry over to OLMo 3 and non-OLMo models.
Verifiable, Articulable, and Tacit Components of Preference
AI models are mainly improved through articulated rubrics, constitutions, and verifiers, but qualities such as a gripping story or an elegant proof are partly tacit and resist being written down. The authors release CreativePreferences, with 2.8M texts labeled by 317M human preference judgments across seven domains, and compare models built from executable programs, rubric banks, and densely trained predictors. They use capture-recapture to estimate the value of criteria nobody has written down yet. Articulability and verifiability gaps appear in every domain, including math and software engineering, are largest for peer review and creative writing, and grow as more people contribute to the judgment. Articulating preferences also pulls them away from their tacit component, much as Goodhart's law predicts, and the authors give recommendations on which judgments can be prompted and which should stay with humans.
hacktrace: behavior-supervised detection of reward hacking during code generation
Coding agents can reward-hack, for example by deleting a failing test instead of fixing the bug, and detecting this requires catching attempted shortcuts even when they fail. The authors release 173,561 annotated multi-turn coding trajectories from Qwen3-8B and introduce HACKTRACE, a monitor trained on shortcut behavior rather than exploit success. It reads hidden states the agent already computes while generating code. Combined with static features of the final files, it reaches a mean per-problem AUC of 0.997 with 8 ms of overhead, beating monitors that query the model again. As a penalty during GRPO reinforcement learning, it cuts the share of cheating among passing solutions from 82–91% to 1–5% while keeping honest correct solutions.
Securing Computer-Use Agents Against Branch Steering Attacks
Computer-use agents (CUAs) that operate graphical interfaces are exposed to indirect prompt injection from every page they render. The Dual-LLM defense, which fixes a plan before the agent reads untrusted data, breaks down here because plans must branch on web content the agent has not yet seen. The authors define branch steering attacks, in which crafted data pushes the agent down a harmful branch the plan already approved. On their STEER-Bench (101 tasks across 9 domains), these attacks succeed 94.4% of the time against standard CUAs and 89.5% against vanilla Dual-LLM CUAs. Their COBRA architecture adds ahead-of-time limits on the parameters and destinations each branch may use, and it reduces attack success to 0% while retaining 97% of benign utility.
Collective Bias Mitigation via Model Routing and Collaboration
Asking an LLM to correct its own biases is limited by what that single model knows. Collective Bias Mitigation (CBM) learns how different LLMs behave and then selects and organizes several of them so they can share knowledge, including through Debating and Committee collaboration setups. Both setups substantially reduce bias. In the top-7 setting, Committee cuts the age bias score from 0.25 to 0.10 and offers the best balance between bias reduction and inference cost.
Passing the Test You Trained On: Re-evaluating Prompt-Injection Detectors for LLM Agents
LLM agents often screen tool outputs with small prompt-injection detectors chosen by their public benchmark scores, and the authors test whether those scores predict behavior inside an agent. They replay ground-truth tool calls from AgentDojo and tau-bench to get tool outputs that are benign by construction, label injected outputs by differential replay, and evaluate fifteen detectors, including Prompt Guard 2, plus two task-aware LLM judges, alongside the BIPIA benchmark. Rankings transfer poorly: the best detector on BIPIA catches only 2% of AgentDojo injections at a 1% false-positive rate, while false-positive rates on tool outputs range from zero to over 90%. The training data explains much of this, since the best detector on both agent benchmarks was trained on agent-style inputs. The authors recommend evaluating on the agent's own tool outputs, reporting detection at low false-positive rates, and auditing what each detector was trained on.
A Near-Zero Monitor Readout Is Not Evidence of Behavioral Control
Post-training with verifiable rewards can lead to reward hacking, so monitors are increasingly placed inside the training objective rather than used only for offline auditing. In a code-generation environment where the main exploit is available from the start of the reasoning trace, the authors train policies against three monitors that pass the same offline check: an activation probe and two penalties on how early the policy commits to its answer. Monitor readouts near zero coexisted with outcomes ranging from mostly honest behavior to near-pure reward hacking, depending only on the random seed. Text analysis shows policies padding their output with generic planning to push the exploit past the monitored prefix without removing it, and the authors argue that an independent behavioral check is required.
Detect and Suppress: A Mechanistic Defense against Adversarial Patches in VLA Models
Adversarial patches placed in a robot's camera view can make Vision-Language-Action (VLA) models fail, and the internal mechanisms behind these failures are poorly understood. Using a sparse autoencoder (SAE), the authors find a feature in the VLA's representations whose activation tracks the presence of an adversarial patch. At inference time they suppress this feature only when a linear probe detects an attack, so the VLA needs no fine-tuning. On LIBERO-10, this conditional intervention improves task success under intermittent attacks, whereas applying the same suppression all the time substantially degrades the policy, which shows that the timing of the intervention matters.
Certified Mechanistic Edits: Behavioral Guarantees for Skill Removal and Preservation
Mechanistic edits such as ablations, weight edits, and activation steering are used to remove harmful capabilities from models, but their effects are normally checked only by testing. The authors formally certify the behavioral effect of an edit: disabling a circuit provably removes one skill and preserves another for every input in a continuous region, which is a non-interference guarantee in the information-flow-security sense. They demonstrate this on networks ranging from toy ReLU models to a small softmax and LayerNorm transformer, and sound bound propagation lets them handle about 9x the input dimension an exact solver can. They also prove that no finite deterministic black-box test can certify removal, exhibiting an edit that passes exhaustive testing but still fails on an arbitrarily small region of inputs.
Efficient Reasoning Training Does Not Always Harm CoT Faithfulness and Monitorability
Training models to reason in fewer tokens could make chain-of-thought (CoT) less faithful to how the model actually reaches its decisions, which would weaken CoT as a tool for oversight. The authors fine-tune several models with three length-pressure methods: a fixed generation budget, a per-example length target, and a group-relative length reward. They then measure faithfulness (whether the CoT reflects the model's decisions on related inputs) and monitorability (whether the CoT reveals when an input intervention changed the answer). Faithfulness drops in most settings, mainly because the trained models become less consistent, but monitorability holds up: models keep acknowledging what influenced their answer even with much shorter CoTs.
Threat-Preserving Representation Sensitivity in Agent-Security Benchmarks
Security benchmarks for LLM agents report an attack success rate (ASR), but that number may depend on how the scenario is presented to the agent as well as on the agent's robustness. The authors define threat-preserving representation sensitivity (TPRS): how much ASR changes when only the agent-visible representation changes, such as tool names, while the task, harmful action and evaluation stay fixed. On Agent Security Bench, replacing threat-suggestive tool names with neutral ones raises ASR by 11.67 points on GPT-5-mini and 13.21 points on Claude Haiku 4.5. On MCPTox, the opposite change lowers ASR by up to 11 points, and most of that shift is reproduced by a neutral name matched only in length and casing. The authors argue that robustness claims should be reported across a set of equivalent representations rather than a single score.
6 more specialized papers
- From Mathematical to Executable Certificates for Machine Unlearning Ziyu Zhao, Xinyu Wang, Xiaowen Chang et al.
- DNAlign: Dynamic Null-Space Safe Alignment for LLMs Jisheng Dang, Yushuo Zhao, Dewei Liu et al.
- Ontological Instability and Statistical Amplification: The Paradox of "Humanizing" LLM-Generated Text Claudiu Creanga, Liviu Dinu
- To Jev or Not? Evaluating the Accuracy and Efficiency of Structured Decision Models for Hate-Speech Moderation Demetris Paschalides, George Pallis, Marios D. Dikaiakos
- Single-Pass Uncertainty Heads for Claim-Level Hallucination Detection in Persian Medical Language Models Mehrdad Ghassabi, Pedram Rostami, Hamidreza Baradaran Kashani et al.
- A Path Integral Surrogate for Multi-Step Gradient Inversion in Federated Learning Agnivo Ghosh, Saumik Bhattacharya
Reinforcement Learning 28
Co-design Gym: A Unified Benchmark for Embodiment-Policy Co-optimization
Most benchmarks for learning control policies treat the agent's physical design (its embodiment) as fixed, even though design and policy shape each other and optimizing them separately often gives poor results. Co-Design Gym is a benchmark suite for optimizing embodiment and policy together, spanning robotic manipulation and locomotion, multi-robot cooperation, soft-body dynamics, video games, electricity grids, wireless networks, F1 racing, warehouses and optimal control. It provides 20 environment families with more than 85 co-design presets, along with a systematic evaluation of representative co-design algorithms that maps out the current state of the art.
Tropical Reinforcement Learning
Standard reinforcement learning for LLMs maximizes expected return, which sums the probabilities of all successful trajectories. The authors argue this fits compositional reasoning poorly: it does not record which solution worked, and reinforcing one solution can push down another that was never shown to be wrong. Tropical Reinforcement Learning takes the maximum over alternative solutions instead of their sum (the tropical semiring), so a state's value becomes the log-probability of its best verified solution together with a replayable path, and a best prefix and best suffix from different rollouts can be joined. The resulting algorithm, TROPIC, targets deterministic, resettable environments with verifiable outcomes and beats the strongest on-policy baselines by up to 16 percentage points on Sokoban, Countdown, FrozenLake, and WebShop.
Multi-Fidelity Policy Gradients Stabilize Data-Scarce Reinforcement Learning
On-policy policy-gradient methods become unstable when scarce, expensive target-domain data gives noisy gradient estimates. The multi-fidelity policy gradient (MFPG) framework uses cheap but biased low-fidelity data, for example from a simplified simulator, only as a control variate that reduces variance without biasing the gradient, but it had only been shown with REINFORCE on small tasks. The authors show that naive extensions to PPO lose cross-fidelity correlation or inflate variance. Their MFPG-PPO redesigns sampling, advantage estimation, and control-variate construction, monitors estimator uncertainty, and adds a budget-aware variant that splits samples between data sources. In simulated locomotion it matches PPO trained with 16× more high-fidelity data on the hardest task, and it learns on a physical Franka arm with only 4 real episodes per update.
Reward Inflation: A Healthy Stimulus for Reinforcement Learning
Reinforcement learning (RL) usually keeps reward magnitudes fixed during training. The authors propose reward inflation, which gradually scales rewards up over time. Theoretically, this creates an implicit recency weighting that favors recent transitions and keeps gradients alive as the policy saturates, which suppresses dormant neurons and preserves plasticity. Experiments on ALE games and MuJoCo tasks show that a suitable inflation level helps across a wide range of tasks, and an adaptive variant, Fed, which tunes inflation on the fly, often beats fixed schedules.
Test-time Multi-agent Coordination by Decomposed Value Gradient Flow
Offline multi-agent reinforcement learning (MARL) faces a trade-off: generative policies capture multi-modal coordination but ignore value, while value-optimized policies collapse to a single mode and can break joint coordination. SCOUT trains a flow-matching behavioral prior and a decomposed value function separately, then at test time moves behavioral samples toward high-value regions with Stein variational gradient descent. The number of transport steps provides adaptive test-time scaling in place of a fixed regularization coefficient. Under the individual-global-max principle, the authors prove a KL bound on the joint soft-value gap that vanishes as transport converges, and SCOUT achieves the best average performance across discrete and continuous offline MARL benchmarks.
Prospective Hindsight: Self-Calibrating Reinforcement Learning via Prediction-Reality Gaps
In reinforcement learning for long-horizon agents, credit is assigned only after outcomes are observed, so what the agent believed when it acted never reaches the gradient. Prospective Hindsight (PH) has the policy predict its outcome before feedback and treats the gap between that prediction and the actual evaluation as a per-rollout surprise signal. Rollouts with high surprise get more weight in the advantage. Because the predictor shares the policy's parameters, calibration improves as a byproduct of optimization. Applied on top of GRPO, on-policy distillation, and their combination, PH improves both task performance and calibration on single-turn verifiable tasks and a multi-turn personal-agent task, across model scales. It corrects overconfident failures in the single-turn setting and underconfident successes in the multi-turn setting.
OPD Before RL: Warm-Starting Rubric-Based RL with On-Policy Distillation
Rubric-based reinforcement learning (RL) scores open-ended responses against explicit criteria, but the reward arrives only after a full response, so it does not show which individual decisions earned the score. The authors propose a two-stage recipe. First, rubric-privileged on-policy distillation (RP-OPD) trains a student that cannot see the rubric to match the token-level distributions of a rubric-aware teacher on the student's own generations. Then standard rubric-reward RL pushes past the plateau that distillation reaches. Across HealthBench, ResearchQA, and RubricHub Science with open-weight models, the two-stage pipeline scores highest among the methods tested, and it shows limited reward hacking, while an SFT + RL baseline learns to claim rubric compliance without providing the required content.
VIGOR: Zero-Shot Visual Generalization via Latent-Space Consistency in Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) plans inside learned latent dynamics and is sample-efficient, but unseen visual distractions such as background, lighting, or camera changes push encoder outputs off-distribution, and those errors compound over planning rollouts. VIGOR addresses this with three linked components: asymmetric weak-to-strong augmentation, a consistency loss that makes predicted latent transitions invariant to augmentation, and an encoder stabilization term that prevents drift. It generalizes zero-shot to unseen distractions and beats the second-best baseline by 3.4% on the DeepMind Control Suite and 43.6% on Robosuite. Ablations show the robustness comes from latent-space consistency rather than from any particular augmentation.
MetaRubric: Learning to Reward for Rubric-Based Reinforcement Learning
In rubric-based reinforcement learning, judges can award credit for a criterion even when the response lacks the required information. The authors call this failure Vacuous Credit and show it can flip the sign of a response's GRPO advantage. MetaRubric alternates two steps. In policy optimization, it grants credit only when the response contains sufficient evidence, checked against counterfactual prompts in which one task-relevant fact has been changed. In rubric adaptation, it revises criteria and their weights based on current policy responses. Across multiple backbones, MetaRubric improves PubMedQA accuracy by 6.00–20.40 percentage points over static-judge GRPO, with further gains on HealthBench-Hard and two multimodal medical benchmarks.
All Work And No Play Makes Jack a Dull Boy: Understanding and Preventing Catastrophic Strategy Collapse in RLVR
When LLMs are trained with Reinforcement Learning with Verifiable Rewards (RLVR), GRPO-style algorithms can collapse late in training, and probing shows this comes from losing access to distinct reasoning strategies rather than from harmless pruning. The authors prove that common RLVR objectives push probability mass onto a single strategy, while keeping accuracy up requires a minimum number of usable strategies, and the conflict between the two explains the collapse. They propose the Mirrored Entanglement Index as a cheap online early-warning signal, along with Mesh Learning, which exposes the model to multiple reasoning strategies so none dominates training. On AIME, MATH-500, GPQA, and LiveCodeBench, Mesh Learning beats strong baselines on Qwen and Phi models by up to 13.4 percentage points.
Understanding Enrichment in Reinforcement Learning
With sparse rewards, RLVR training often adds hints or intermediate guidance so more rollouts succeed, but this 'enrichment' biases policy-gradient updates unless it is corrected with importance weights, which existing methods usually omit or truncate. The authors show that omitting or truncating the correction implicitly reweights the reward, and they break the resulting gradient error into scale, rotation, and variance components. They also introduce a sequential Monte Carlo correction that, under stated assumptions, makes variance grow additively rather than exponentially with sequence length. A fine-tune of Qwen3-1.7B on a sparse subset of OpenMathReasoning illustrates how both corrected and uncorrected enrichment help training avoid collapse.
Turnover-Orthogonal Credit Assignment for Open-Team Multi-Agent Reinforcement Learning
In open-team multi-agent reinforcement learning, agents can join, leave, or be replaced mid-episode, and standard shared critics end up crediting or blaming surviving agents for turnover they did not cause. Turnover-orthogonal credit assignment (TOCA) splits the team value into action effects, pure turnover effects, and their interaction, using a permutation-invariant critic over variable-size agent sets plus tokens marking turnover events. It provides a per-agent counterfactual credit signal and a softly weighted variant, TOCA-beta, for noisy environments. TOCA beats event-aware MAPPO-style critics in diagnostic tests, TOCA-beta achieves the best mean return at high turnover rates on the Dynamic Spread benchmark, and removing the interaction credit hurts performance substantially.
Probe the Harness: Setup Checks for Stale-Data RL Comparisons in Language Models
Methods that train language models on stale samples are usually judged against importance-corrected baselines, and this work shows that details of the experimental harness can reverse which method looks better. In a comparison between the behaviour-free method SAN and truncated importance sampling (TIS), run both on verl and in a single-GPU trainer, SAN first came out ahead in both. Four hidden harness flaws caused this: the PPO ratio was computed against the learner's own recomputed probabilities, the data seed never reached the TIS arm, the replay queue reused its first batch for 33 updates, and two loss normalisers did not match their description. In every case the logged metrics looked healthy. Once the harness was fixed, TIS matched SAN on verl. The authors release the PTH (Probe The Harness) checklist along with reference results for TIS and uncorrected GRPO under sampler lag.
How to Find and Reuse Policies for Continuous Adaptation in Lifelong Reinforcement Learning
In lifelong reinforcement learning, the knowledge relevant to a new task may be spread across several earlier policies, and which ones matter can change as the learner gains experience. AMSC estimates task similarity online using non-parametric Wasserstein embeddings of state-action-reward samples. A normalized sparsemax over these similarities then periodically selects and weights earlier policies to form a prior for the new task. On CT-graph and MiniGrid it achieves higher performance and forward transfer than modular composition baselines with no forgetting, while results on Continual World suggest that layer-specific tuning may be needed. Measured pairwise transfer between tasks correlates positively with embedding similarity.
AdaStep: Adaptive Step Credit Weighting for Agentic Reinforcement Learning
Long-horizon LLM agents are usually trained with sparse rewards for the final outcome, and estimates of credit for individual steps are noisy because returns also depend on later actions and environment randomness. AdaStep treats the weighting of step-level advantages as a mean-squared-error estimation problem and, under an explicit sampling assumption, derives an optimal per-state shrinkage coefficient. That coefficient keeps local credit when return variation comes from the chosen action and suppresses it when downstream randomness dominates. The method needs no critic, extra rollouts or additional model inference, and it gives consistent improvements over baselines with three backbone models on ALFWorld, WebShop and ScienceWorld.
Follow the Winners: Conservative Policy Improvement with the Cross-Entropy Method for Critic-Free RFT
Group-based critic-free reinforcement fine-tuning (RFT) methods like GRPO need repeated rollouts from the same state, which is impractical for agents acting in stateful environments such as live services or security sandboxes. Follow the Winners (FTW) adapts the cross-entropy method to RFT, replacing group rollouts with an ordinal filter that keeps the top-returning samples from a replay buffer. Deriving it through a control-as-inference lens also recovers GRPO and DPO as special cases. This shows GRPO is risk-neutral, while DPO and FTW share a bounded risk-seeking offset that FTW controls. In agentic LLM post-training, FTW matches GRPO and PPO on Sokoban and Search-R1 while replacing a value model or group rollouts with CPU memory.
Mixture-of-Experts for Cryptocurrency Order Execution: Training Stability, Tail Risk, and Failure Modes
Deep reinforcement learning policies for order execution vary widely across training seeds, which can make architectural gains look larger than they are. Using 5-minute BTC/USDT limit order book data from Binance, the authors compare vanilla Double Deep Q-Learning (DDQL), K-means-partitioned mixtures of DDQL experts, and parameter-matched dense networks. No learned configuration beats DDQL or simple baselines like TWAP (time-weighted average price) on mean implementation shortfall. The mixture of experts appears to prevent policy collapse, but annealed exploration alone eliminates collapse (12/100 runs to 0/100), so the expert partitioning was masking a training-specification problem rather than adding a real benefit. The attribution of the failure mode even reverses between 30 and 100 seeds, which underlines the need for repeated-seed evaluation.
Bidirectional Voronoi-biased Exploration Curriculum for Reinforcement Learning
In goal-conditioned reinforcement learning, long-horizon sparse-reward tasks are hard to explore, and automatic curricula typically expand from only one side. Inspired by bidirectional RRT (rapidly-exploring random tree) planning, BVER grows start states outward from the goal and goals outward from the initial states at the same time. It biases both toward unexplored space, steers them toward each other, and trains a single goal-conditioned policy on both. On point-mass mazes, quadruped box climbing, and robot-arm ring-on-peg transfer, it learns faster than other curricula that need no demonstrations. On box climbing, it reaches 95% success in about 65% fewer iterations and is the only such curriculum to learn to climb a 0.7 m box.
Single or Multiple Policies for Phase-Structured Reinforcement Learning?
Many reinforcement learning (RL) problems split into phases, each with its own dynamics and rewards, and prior work has found, without a clear explanation, that separate per-phase policies can beat a single policy whose state includes the phase. The authors prove that a shared state-augmented policy can in theory match any multi-policy solution, so practical differences come from function approximation, optimization, sample efficiency, and discontinuities at phase switches. They propose a regime-based decomposition that compares the length of transient dynamics with the length of quasi-stationary periods to predict which design will work better. Experiments support four hypotheses: long phases favor multiple policies, heterogeneity between phases strains a single policy, multiple policies need enough data per phase, and the dynamics of transitions between phases affect which design is preferable.
Mastering Atari 2600 Games with Discovered Options
Options are reusable multi-step behaviours in reinforcement learning (RL) that are meant to speed up exploration and credit assignment, but methods that discover them automatically have rarely helped in large, high-dimensional domains. Wayfarer is a domain-agnostic online deep RL agent. It discovers options by learning Laplacian representations directly from raw observations and then uses those options for control. The options improve exploration, credit assignment and generalisation, and Wayfarer reaches state-of-the-art performance among single-stream agents on the hardest Atari 2600 games. Its largest gains come on long-horizon exploration games such as Montezuma's Revenge and Private Eye.
IDRF: Inverse-Distilled Reward Fine-tuning of Masked Discrete Diffusion Models
Reward fine-tuning of masked discrete diffusion models is complicated by intractable sequence likelihoods, which make the usual KL-divergence regularizer hard to compute. IDRF replaces that penalty with an inverse-distillation regularizer, which the authors prove upper-bounds the sequence-level KL divergence under an optimal auxiliary denoiser. It treats few-step generation as a finite-horizon Markov decision process and optimizes reward with a clipped policy-gradient objective over the student's own trajectories, without rollouts from the reference model. Across DNA, image and text generation, IDRF reaches high reward with up to 32× fewer denoising steps than the reference while limiting reward hacking and preserving sample quality.
7 more specialized papers
- Bandits via Additive Quantized Representations Ami Tavory, Noam Touitou, Tal Sarig et al.
- Bellman Error Minimization Via Linear Programming Normalization Haining Yu
- FSPO: Policy-Consistent Risk and Pareto-Feasible Control for Budgeted LLM RL Post-Training Miaobo Hu, Shuhao Hu, Xiaobo Guo et al.
- Learning Transferable Policies from Action-free Time Series Through Dynamical Embeddings Niklas Emonds, Georgia Koppe
- EVOL: Simulator-Guided Evolutionary Expert Synthesis for Deployment-Free Learning Path Recommendation Geonwoo Bang, Dongho Kim, Moohong Min
- When a Correct Reward Is Not Enough: Diagnosing and Guiding PPO in an Analytically Solved Broker-Trader Game Siu Tung Wong (Institute of Finance and Technology, University College London), Carlo Campajola (Institute of Finance and Technology et al.
- When May a Bandit Leave Its Anchor? E-Process-Authorized Thompson Sampling under Non-stationarity Mayand Gulati, Kerong Wang, WeiChen Au
Multimodal 26
Slow-Fast Multi-Teacher On-Policy Distillation for Capability Preservation
Multi-teacher on-policy distillation (MOPD) merges domain expertise from several teachers into one multimodal student, but the student drifts from its starting point and loses general capabilities. SF-MOPD pairs a fast model (the student being updated) with a slow model, an exponential moving average of the student that serves as a moving capability reference. For each teacher's update in log-probability space, it removes only the component that pushes the fast model further from the slow one, keeping aligned and orthogonal components. Across several model scales, it reduces degradation on general-capability benchmarks while improving specialized multimodal skills, consistently beating vanilla MOPD.
VisAudit: Evaluating Multimodal Agents for Visual Diagnosis and Repair
Multimodal agents can generate charts but often fail to review them on their own: they may not notice an error, decide what to change, repair it without breaking correct parts, or confirm that the fix worked. VisAudit benchmarks this full loop, giving an agent a rendered chart plus optional source data, an intended summary and plotting code, and having it diagnose problems, edit and run the code, inspect the output, and decide when to stop. It contains 1,900 flawed instances across 21 chart types and 10 flaw categories, plus 300 correct charts, organized into tracks for diagnosed repair, autonomous repair and open-world verification. The strongest model tested fully recovers only 47.4% of flawed charts in autonomous repair.
Batched Speech Decisions Without Decoding: Single-Token Supervision Lets a Frozen LLM Hear Beyond the Transcript
Full-duplex voice agents make many small multiple-choice decisions, which current systems answer through slow autoregressive decoding. DuplexJev passes speech-recognition (ASR) encoder hidden states through a small connector into a frozen LLM and reads each answer as a single-token distribution over the options, so nothing is decoded; an 8-GPU node answers 80 decisions about eight utterances in about 0.1 seconds. Decision tasks are trained with cross-entropy on the answer token instead of transcript distillation. A cross-attention connector raises gender and emotion accuracy to 90% (from 55% and 28%) while spoken question answering drops by only one point. The authors release the weights, training recipe, a batched serving pipeline, and a bilingual spoken QA dataset.
LatticeSMC: Where to Spend Inference-Time Compute in Chunked Sequence Generators
Generators for long music, motion, and video produce output chunk by chunk, with each chunk created by iterative denoising, while the rewards used to steer them are defined over the whole sequence. Existing steering methods act on only one axis (final best-of-N, across denoising steps, or across chunks) and are often compared at unequal compute. LatticeSMC is a sequential Monte Carlo sampler built on a Feynman-Kac model over the two-dimensional lattice of chunk index and denoising step, and the authors prove that it can resample exactly at chunk boundaries using prefix scores with no extra denoiser calls. At matched compute with 32 particles, it raises beat alignment in music-to-dance generation from 0.234 to 0.441 (best-of-N reaches 0.354) and lifts text-to-music prompt adherence from 0.470 to 0.560, and human raters preferred its outputs in 60–77% of pairwise comparisons.
Text-Centric Post-Training for Omni-Modal Reasoning
Improving joint audio-visual reasoning in omni-modal LLMs usually requires costly multimodal data and training. The authors find that perception and reasoning are partly decoupled: models fail multi-hop questions even when they answer every single-hop part correctly. Training on text alone, with supervised fine-tuning followed by RL, raises Qwen2.5-Omni-7B's geometric mean across nine reasoning scores by 25.83% while using 56.6% fewer GPU-hours than the full audio-visual pipeline, but it degrades perception. They therefore propose text-centric post-training: text-only training for reasoning, then a small audio-visual RL stage that uses about 90% fewer input tokens, restores perception above the base model, and keeps 93.5% of the reasoning gain.
Recursive Self-Improvement in Unified Multimodal Models
Unified multimodal models (UMMs) can both understand and generate images, but existing self-improvement methods only use image understanding to judge image generation. Recursive cross-capability self-improvement (RSI) has the model generate images, find its own shortcomings, and write programs targeting them. Executing those programs verifies each result against its specification, which provides ground truth from outside the model so that errors do not compound across rounds. On chart generation, evaluated with the new BasicChartBench, four rounds raise the score on differently worded requests from 45.7% to 60.2%, compared with 46.3% for continued training. Verified program construction accounts for most of the gain, and targeting the model's own failures adds another 3.5%.
Foresight: planning future perception in streaming VLMs without retraining
Streaming vision-language models (VLMs) process video with a fixed amount of computation, even though upcoming events may need different kinds of perception. FORESIGHT runs two copies of the same model that share weights, encoders and KV cache: one keeps processing the incoming stream, while the other runs ahead to anticipate upcoming content and plan when to reason, what to check and how densely to sample frames. It applies these plans online through schema-guided decoding and lightweight diff-based updates, without any retraining. With a frozen Qwen3-VL-8B, it beats the strongest trained baseline on OmniPro by 9.5% and improves the backbone by 6.7 points on StreamingBench and 15.4 points on OVO-Bench.
Architecture-Dependent Fusion Pathways in MLLMs
How multimodal large language models (MLLMs) combine visual and textual information across layers is not well understood, especially across different architectures. The study compares concatenation-style models with native multimodal models using alignment decoupling, attention routing and entropy, intrinsic dimensionality, and causal interventions, plus a visual CKA check of the Platonic Representation Hypothesis. It finds two distinct fusion pathways: concatenation models process text first and integrate vision later, while native models show earlier visual-textual co-adaptation and reorganize their feature spaces.
From Patching to Pruning Visual Computation in Vision Language Models
Vision language models (VLMs) spend much of their inference compute running every visual token through every decoder layer. Patch-to-Prune (P2P) is a training-free method that turns activation patching, an interpretability technique, into a way to skip computation. It uses layer sweeps on validation data to find decoder regions where visual-token projection outputs can be replaced with fixed proxy activations within a user-set accuracy tolerance, without removing tokens or changing weights. On four Qwen2.5-VL and LLaVA models across seven benchmarks, P2P cuts FLOPs by 55% while retaining about 94% of dense accuracy. Layer-wise analysis suggests that most task-relevant visual integration happens in intermediate layers.
Corrupted but Correct: Why Vision-Language Models Lie to Themselves Internally
A targeted adversarial image perturbation can drive a vision-language model's (VLM's) teacher-forced loss on a target caption to near zero, yet the model still produces the original, correct caption when it generates freely. Studying this train/inference gap in Qwen2.5-VL-7B-Instruct with a two-stage projected gradient descent (PGD) attack on 200 COCO images, the authors find that image pixel statistics do not predict which images get corrupted. Using the logit lens, they localize the gap to a single decoding step. Layer-by-layer tracking shows that the visual encoder corrupts all images by a similar margin, while the language decoder amplifies the corruption for susceptible images and suppresses it for resistant ones. They conclude that adversarial robustness in autoregressive VLMs depends largely on the language decoder's prior rather than the visual encoder.
World Embedding Benchmark
The World Embedding Benchmark tests how video representations encode physics. It contains 8,000 controlled simulation cases from 80 families across fluid mechanics, solid mechanics, dynamics and optics, each paired with physical annotations, and supports text-video retrieval, physical-property regression and video-description pair classification. Pre-trained omnimodal embedding models retrieve poorly and score near chance on within-family pair classification, yet lightweight probes can recover physical properties from their frozen embeddings. Continual contrastive training on physics video-text pairs improves alignment but degrades property regression, revealing a trade-off between the two. Using the embeddings to retrieve reference videos for retrieval-augmented generation with MiniMax-H3 improves the physical fidelity of generated videos.
15 more specialized papers
- Toward Controlling Biology with Language:Offline Learning of Prompt-Conditioned Interventions for Cells, Organoids, and Biobots Nam H. Le, Douglas Blackiston, Michael Levin et al.
- Learning When to Commit from Partial Speech for End-to-End Simultaneous Speech Translation Hieu Hoang, Amittai Axelrod
- No-Free-Graph: Learning When Multimodal Data Should Be Graphified Zekai Chen, Kai Hu, YuXin Zeng et al.
- AMBER: Multi-View Adaptive Budget Allocation for Listwise Vision-Language Reranking Wenteng Chen, Jiachen Zhu, Rong Shan et al.
- How Robust Is Multimodal Claim Verification to LLM Rewriting? Yun-Ang Wu, Xanh Ho, Andre Greiner-Petter et al.
- Evaluating VQA in Vision Language Models using Cooperative Principles Monika Shah, Sudarshan Balaji, Somdeb Sarkhel et al.
- Toward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach Xunkai Li, Chenxi Wan, Yinlin Zhu et al.
- Revealing Epistemic Uncertainty in MLLMs via Causal-Invariant Masking Haoyang Luo, Linwei Tao, Jie Gui et al.
- Relevant Evidence Decoding for Audio-Visual Hallucination Mitigation Hyunjae Ra, Aecheon Jung, Jungin Park et al.
- OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination Huiqiang Rong, Haoran Luo, Hui Feng et al.
- Balancing Multimodal Learning via Functional Progress Zhongjing Gu, Fengqiang Wan, Yiming Cui et al.
- Beyond Single Videos: Benchmarking and Active Evidence Seeking for E-Commerce Cross-Video Reasoning Jinghan Zhao, Yiman Hu, Liang Wu et al.
- CLIMB: Confidence-Guided Complementary Evidence for Multimodal Retrieval-Augmented Generation Hang Gao, Wujiang Xu, Zhixing Zhang et al.
- Measure Less, Know More: Self-Supervised Test-Time Feature Acquisition Eeshaan Jain, Linus Bleistein, Bart Deplancke et al.
- ZeroMAG: Zero-Shot Multimodal Adapter Generation for Plug-and-Play EEG Foundation Models Yubo Wang, Jingying Ma, Xinliang Zhou et al.
Vision 23
EditHero: A Benchmark for Long-Horizon Part-Level 3D Editing and Vibe Modeling
3D editing methods are usually evaluated on a single edit, but real assets go through long sequences of revisions, each of which must change only what was requested. EditHero is a benchmark for long-horizon, part-level 3D editing with natural-language instructions and exact target geometry and texture produced by a deterministic assembly engine. It compares non-agentic methods that regenerate the whole object against LLM and vision-language model (VLM) agents that edit the mesh through code. The agents follow instructions more closely and all preserve unedited parts better, but each of their edits takes minutes, while non-agentic methods often miss the change and disturb regions that should stay fixed.
Correcting Guided Diffusion Trajectories with Spectral Alignment
Classifier-free guidance (CFG) drives conditional image generation but has no explicit criterion for whether a guided sampling trajectory is on track. The authors show that the frequency spectrum of intermediate diffusion states can be compared against the spectral evolution expected from the forward process. They use this to build Spectral Correction Guidance, a training-free method that corrects deviations from an analytic reference spectrum during sampling and works across backbones. It gives consistent gains in preference-based metrics over baseline guidance in text-to-image generation and better quality than CFG on ImageNet, across guidance scales and with fewer denoising steps.
Post-Training Frontier Text-to-Image Models by Composing Preference and Rubric Rewards
No single reward captures the full range of human preference when post-training text-to-image models. The authors combine a Bradley-Terry preference reward, trained on large-scale human preference data, with rubric-based rewards that check prompt faithfulness and other properties and help guard against reward hacking. They show that a naive weighted average of the rewards optimises poorly and propose a simple composition strategy that balances the two better. On the Arena text-to-image leaderboard, the RL-trained Flux2dev gains 69 Elo over its base model, and the post-trained Ideogram-4 surpasses every open-source model with an Elo of 1223.5. The authors release Arena-T2I-Training, a 1K-example subset of the training data.
NegT2IBench: When Negation Changes the Picture. A Polarity Benchmark for Text-to-Image Models
Text-to-image (T2I) benchmarks check whether requested content appears, but rarely whether the model respects negated constraints such as "a non-red cup." NegT2IBench has 4,800 prompts in which the number of positive and negated statements varies independently, which separates the effect of negation from the effect of prompt complexity. Scoring uses object detectors and agrees with human annotators about as well as vision-language judges up to 30x larger. Across eleven T2I models and 211,200 images, nine models score lower on a single negated statement than on a single positive one, and 41.5% of failed statements render exactly what the prompt forbids.
Contextual Flow Matching: Adaptive Step Selection in Flow Models for Efficient Visual Generation
Flow Matching models produce high-quality images and video, but sampling needs many sequential network evaluations, and existing speedups often require extra training, lose quality or use the same step count for every input. COFLOW is a plug-and-play inference-time method that picks the number of sampling steps for each generation from the prompt's features, using a policy trained online with an unsupervised reward that balances speed against fidelity. It needs no retraining of the generator, works for both image and video generation, and achieves over 2.5x speedup while preserving perceptual and semantic quality. The authors also prove an O(1/K) error bound for forward-Euler discretization under standard regularity conditions.
AREX: Affine-Residual Exponential Integrator for Few-Step Sampling in Flow Matching
AREX is a training-free sampler that makes few-step generation work better with pretrained flow matching models. The authors show that the velocity field of a Gaussian matched to the target's mean and covariance is the best affine (L2-optimal) approximation to the true marginal velocity field. AREX therefore splits the sampling dynamics into an affine part, which it integrates exactly with a matrix-valued propagator, and a neural residual, which is the only part it integrates numerically. Scalar exponential integrators, by contrast, can only handle isotropic linear dynamics analytically. On image and text-to-image generation, it consistently improves sample fidelity at low step counts without retraining.
Getting Your Guidance Weights Right in diffusion and flow-matching posterior sampling
Training-free Plug-and-Play methods use pretrained diffusion or flow-matching models to solve inverse problems, but the guidance weights that balance the learned prior against measurement consistency are usually tuned by heuristics. The authors observe that at each time step the conditional score-matching or flow-matching objective is a least-squares problem. Fitting the guidance weights therefore reduces to a two-dimensional linear least-squares problem, which can be solved offline from a single minibatch of sampling trajectories without retraining the model. Combined with the standard Tweedie-based consistency term, the optimized weights achieve state-of-the-art reconstruction and let diffusion samplers cut sampling from 1000 to 50 steps without significant quality loss.
Weave Forcing: Compositional Memory Routing for Interactive Long Video Generation
Interactive storytelling with autoregressive video generation often requires a new shot to combine characters and backgrounds from different earlier shots, and neither retrieving whole prompts nor combining all past memories handles this well. Weave Forcing is a training-free framework in which an LLM splits each prompt into character and background slots and picks a suitable historical reference for each. Masked memory weaving then uses contrasting attention maps to expose only the relevant tokens from compressed historical key-value (KV) memories. A coverage-adaptive RoPE adjusts temporal offsets depending on how fully the references cover the current shot. Experiments show improved cross-shot consistency of subjects and backgrounds while visual quality and text alignment stay competitive.
Rethinking What to Cache in Few-Step Diffusion Transformers: Solver-Aware Target Selection
Diffusion Transformers (DiTs) can be sped up by caching, which reuses a tensor from an earlier sampling step. After step distillation, adjacent steps are farther apart, so the choice of which tensor to reuse matters more. AutoTarget makes that choice for each model, solver and reuse schedule. It measures the error of each candidate tensor on a few runs without caching and picks the one with the lowest error. Experiments on distilled image and video models such as PixArt-LCM and FLUX.1-schnell show that the best cache target changes with model, resolution and solver. AutoTarget reduces both model evaluations and cache storage while keeping quality close to uncached sampling.
Depth as Time in One-Step Generative Models
The authors ask what becomes of the multi-step denoising trajectory of diffusion when generation is compressed into a single forward pass. They describe an effect they call depth as time: decoding a one-step generator's intermediate layers with the model's own output head recovers a denoising progression across network depth. How this computation unfolds depends on the transport task the flow map was trained to solve. MeanFlow shows both denoising and renoising within one evaluation, while generators trained without a time-indexed transport task, such as drifting models, show no depthwise denoising. Treating the layerwise computation explicitly as a flow, a single time-conditioned block can replace the stack, compressing a MeanFlow SiT-L/2 model by 16.6× in parameters.
LoGo: Local-Global Rewards for Consistent Long-Horizon Video Generation
Long-horizon, camera-controlled video models suffer from 3D inconsistency: as the camera moves, objects lose permanence and scene structure shifts. Post-training that assigns one scalar reward to the whole video handles this poorly. LoGo combines spatially localized rewards, which provide fine-grained credit assignment for 3D consistency, with a global reward that preserves camera following and video quality. Across three base models, LoGo performs clearly better on DL3DV and on TrajectoryBench, a new benchmark for long-horizon generation under complex camera control, reducing object shifts, artifacts and global scene changes.
Less Decoder is More Encoder: Geometric Representation Learning from Novel View Synthesis
Novel View Synthesis (NVS) should require reasoning about 3D structure, yet encoder-based NVS methods learn poor geometric representations. The authors attribute this to two design choices: highly expressive decoders that take over work from the scene encoder, and pixel-space reconstruction targets that hinder feature learning. Their self-supervised encoder-decoder transformer, SNAP, addresses both with a pose-conditioned local decoder and a latent-space reconstruction objective. It is competitive with geometry-supervised and self-supervised methods across visual localization, pose estimation, point correspondence, depth estimation, and robot manipulation, and its patch features show emergent viewpoint invariance approaching heavily supervised models while degrading more gracefully under camera shifts.
11 more specialized papers
- Rank-Aware Speculative Sampling for Diffusion Draft Trees Marcello Bullo, Yanxiao Liu, \"Oyk\"u S{\i}la G\"uner et al.
- Traversing the Satisfaction-Diversity Frontier in Text-to-Image Diffusion Kevin Zhai, Siva Rajesh Kasa, Soumya Roy et al.
- Geometry-Aware Time Reparameterization for Flow-Map Distillation F\'elix Dedek, Makoto Yamada
- DAGS: Disentangled Appearance-and-Geometry Steering of a Frozen Image DiT for Temporally Stabilized Generative Rendering Karthik Mohan Kumar, Damian Andrysiak, Pedro Antonio Pena et al.
- Revisiting Visual Representation Enhancement of VLMs via Kernel Canonical Correlation Analysis Peilin Yang, Xiaoyu Liu, Jian Sun et al.
- Controlling Polar Exposure to Delay Memorization in Diffusion Models Xuanchen Wang, Heng Wang, Weidong Cai
- TACD: Distilling Efficient Text-to-Motion Models via Terminal Amplification Control Wei-Jin Huang, Yuan-Ming Li, Kun-Yu Lin et al.
- Safeguarding Mutual Correction in Source-Free Domain Adaptation via Cut Statistics Seongjun Lee, Changhee Lee
- OmniAct3D: Leveraging Foundation Geometry and Evidence-Grounded Reasoning for Panoramic 3D Detection Runtong Wu, Fei Teng, Di Wen et al.
- Consecutive Posterior Fusion for Diffusive Recovery of Unobservable Image Structures Elena Morotti, Davide Evangelista, Elena Loli Piccolomini
- ForestQuery: Boundary-Aware and Spatially Anchored Query Learning for Unified Forest Point Cloud Segmentation Zhihao Zhan, Le Tao, Yifei Tian et al.
Unclassified 20
The Fragility of Trigger-Tag Mechanisms for Misuse Detection in Open-Weight LLMs
No summary available — see the abstract on arXiv.
ParaGeo: Decomposing Paralinguistic Variation into a Shared Latent Geometry
No summary available — see the abstract on arXiv.
Coverage You Can Steer: Online Conformal Calibration for RL-Driven Hardware-Aware NAS
No summary available — see the abstract on arXiv.
Trading Strategy Optimization via Textual Gradient
No summary available — see the abstract on arXiv.
Benchmarking Literature Retrieval for a Model Organism: A Dictyostelium Case Study
No summary available — see the abstract on arXiv.
Safe Streaming Flow Planning by Aligning Sampling Dynamics with Execution Dynamics
No summary available — see the abstract on arXiv.
Page-EntroKV: Hardware-Aligned, Entropy-Weighted KV-Cache Eviction under Grouped-Query Attention
No summary available — see the abstract on arXiv.
Investigating the Role of Reasoning-Language Alignment in Monolingual Retrieval-Augmented Generation
No summary available — see the abstract on arXiv.
Keeping JEPA World Models Plannable When Little of the Frame Moves
No summary available — see the abstract on arXiv.
TSGuard: A Real-Time Framework for Detecting and Imputing Missing Data in Streaming Time Series
No summary available — see the abstract on arXiv.
EvoRiskBench: An Evolving Benchmark for Runtime Security Risks in Workspace Agents
No summary available — see the abstract on arXiv.
Does Physics Live in the Activations? Localizing Physical Quantities in Video Diffusion Models
No summary available — see the abstract on arXiv.
Multimodal reasoning for broadly neutralizing antibody discovery from label-free human B cell repertoires across virus families
No summary available — see the abstract on arXiv.
Landscape-Dependent Performance of Photonic Quantum Solvers in QUBO Feature Selection for Financial Risk Detection
No summary available — see the abstract on arXiv.
Predicting Steering Vectors and Adapter Weights for Few-Shot Author-Style Transfer
No summary available — see the abstract on arXiv.
Sample complexity of variance-reduced policy gradient: weaker assumptions and lower bounds
No summary available — see the abstract on arXiv.
LiBRA: Detection-Aware Image Watermark Removal via Bidirectional Latent Optimization
No summary available — see the abstract on arXiv.
Hindsight-Guided Rationale Distillation for Rare Disease Diagnosis
No summary available — see the abstract on arXiv.
Gains and Collapse in On-Policy Distillation:A Reinforcement Learning Perspective
No summary available — see the abstract on arXiv.
Not Until the Evidence Says So: Teaching LLM Investigators When to Close a Case
No summary available — see the abstract on arXiv.
Robotics 18
GlanceWAM: Sparse Test-Time Imagination for World-Action Models
World-action models (WAMs) use video generation as a physical prior for robot control, but generating video in step with the control loop is too slow, while skipping visual imagination lowers task success. GlanceWAM separates the two on a single shared video diffusion-transformer backbone: an asynchronous proposer imagines one lookahead frame seconds into the future on a slow clock, while an action head decodes action chunks in latent space at control rate without waiting for it. A non-interfering attention mask and training that tolerates stale lookahead frames make this work. Trained only on demonstrations, it reaches 72.2% on the 24-task RoboCasa benchmark (versus 67.1% for synchronous Cosmos Policy) and 99.0% on LIBERO, cuts per-chunk control latency 24× to 48 ms on one A100, and outperforms π0.5 on real single-arm and bimanual robots without any robot-data pretraining.
SoTa: Soft Tactile Skins for Dexterous Manipulation
Visuo-tactile robot data is scarce because dexterous demonstrations require robot teleoperation, while cheaper human demonstrations need tactile sensors whose signals correspond between human and robot hands. SoTa is a soft capacitive tactile skin, costing under $10 in materials, that covers the full hand with a shared layout of 202 tactile sensing points on both humans and robots. The skin keeps over 97% of its response after 10,000 loading cycles, and the shared layout allows co-training on human and robot data without learning a mapping between sensors. With a fixed robot demonstration budget, adding human demonstrations raises mean success from 22.8% to 45.9% across eight evaluation conditions.
NEEDLEWORK: Offline Rewriting of Robot Data with Verified Local Stitches
Robot demonstrations often contain useful behavior even when an episode is inefficient or fails, but stitching trajectories together is hard with high-dimensional observations. NEEDLE is an offline data-augmentation method that adds short, verified action bridges between recorded observations. These bridges bypass detours, broaden action coverage, and reuse failed trajectories, using only RGB images, proprioception, and episode outcomes. A sampling scheme folds the accepted bridges into policy training without synthesizing images or discarding the original data, and on real-robot tasks this improves success by an average of 21 percentage points over the strongest baseline.
World Action Modeling with Progressive Visual Planning
World action models (WAMs) control robots by predicting both future video and actions, but generating dense video rollouts is slow, and models that predict only a single future frame give little guidance on how to progress toward the goal. ProWAM predicts actions together with an ordered sequence of sparse visual sub-goals. The sub-goal predictor can learn from large action-free video datasets, and a single forward pass of the video backbone caches sub-goal features so replanning needs only lightweight action denoising. It sets new results on LIBERO-Plus (85.8%) and randomized RoboTwin (75.7%), and in zero-shot real-world tests in novel scenes it reaches 70.0% success versus 55.0% for the strongest baseline.
CriticHack: Evaluating Visual Rewards Under Robot Policy Optimization
Learned visual reward models used to optimize robot policies can score an execution that acts on the wrong object as highly as a real success. When the authors fine-tune a diffusion policy against Robometer on a drawer task, task success rises by 10.2 points but wrong-object failures rise by 10.9 points, so the usual monitored signals look healthy. Training on the simulator's true completion signal shows no such amplification. A tilt model explains the effect: under KL-regularized optimization, any outcome whose expected reward under the initial policy is above the population average becomes more frequent, and Robometer scores wrong-object failures slightly above successes (AUROC .37). The model predicts outcome shifts across 26 constrained settings (Spearman .89), and a frozen outcome verifier steers the same optimization back toward the requested task.
Localized Conformal Safety Monitoring with Vision-Language Models for Autonomous Driving
Checking planned driving trajectories for collision risk is hard because other road users react to the ego vehicle. Vision-language models (VLMs) can reason about such consequences but are too unreliable for safety-critical use. SLLCP (Split Label-Localized Conformal Prediction) adds a post-hoc calibration layer over frozen VLMs. It produces safety prediction sets with label-conditional coverage guarantees and weights past experience by how relevant it is to the current scene. On 15k CARLA trajectories from unseen scenarios, SLLCP flags 89.6% of collision-causing trajectories with a Qwen backbone versus 4.6% for the raw VLM, and 88.4% versus 39.1% with Cosmos.
FastOPD: On-Policy Distillation for Lightweight VLA Deployment
Large Vision-Language-Action (VLA) robot models are increasingly costly to run on real hardware. FastOPD distills a large VLA into a compact student through on-policy distillation, combining a flow map that supervises the student from the teacher's single states with a self-consistency objective. The authors prove the student can match the action distribution of an ideal few-step teacher. On LIBERO, the student keeps 84% of pi-0.5's performance with only two inference steps while cutting latency by 78.1%, and a student distilled from MolmoAct2 was deployed on a real robot.
MobiAgent: Dual-Loop Recursive Policy Self-Improvement for Long-Horizon Mobile Manipulation
Long-horizon mobile manipulation suffers from compounding errors and interference between locomotion and arm control, and Vision-Language-Action models lack the hierarchical reasoning needed for multi-stage tasks. MobiAgent uses two loops. In the inner loop, vision-language models plan over a receding horizon and reflect on visual feedback to compose atomic skills, which are executed by flow-matching experts sharing one backbone. In the outer loop, the system segments, verifies, and clusters its own deployment rollouts to discover skills and fine-tune them without human labels. It beats π0.5-TA by 22.5 percentage points on BEHAVIOR-1K, and recycling its own data raises success from 7.5% to 27.5% on RoboCasa and from 32.5% to 57.5% on a real Astribot S1 robot.
XGenAct: Geometry-Enhanced World Action Models through Cross-Task Generation
World action models (WAMs) predict how a robot's observations and actions evolve over time, but predicting RGB frames and actions does not explicitly teach the spatial understanding that manipulation requires. XGenAct encodes RGB frames, robot actions, metric depth, surface normals, and functional segmentation as RGB videos through deterministic codecs. A single video diffusion transformer with one training objective then learns to predict across all of these, with no modality-specific heads. On held-out RLBench tasks, the extra perception training improves closed-loop success, and the model reaches 52% success versus 26% for the strongest baselines. It also predicts future depth and segmentation better than pipelines that generate RGB first and then apply a frozen perception model.
UniIntervene++: An Adaptive Intervention Agent for Efficient Real-World Reinforcement Learning
Robots learning through online reinforcement learning (RL) in the real world need assistance, and the right kind of help changes as the policy improves. UniIntervene++ treats the RL policy, human trajectory correction and a task-structured CodePolicy as options in one semi-Markov decision process, and learns online which one should have control. It periodically lets the RL policy act unassisted to measure its competence, and assisted experience feeds back into training the policy. Across five real-world manipulation tasks, it reaches an average 89.67% success rate while cutting human intervention to 0.77%, at least 94.6% less than the best baseline.
EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras
EyeRobot 2.0 performs fine-grained bimanual manipulation with a single stereo camera and no wrist cameras by actively turning two eye viewpoints toward a 3D fixation point and processing the images foveally, with more visual tokens at the image centers. Gaze is controlled hierarchically by a low-level gaze-servoing policy and a target selector that picks fixation goals; both are trained with RL on real-world data, and the selector is co-trained with a behavior-cloning gripper policy that acts in a fixation-relative frame. Across more than 1,000 physical and 1,800 simulated trials, it beats passive stereo by 40% in the real world and 20% in simulation, roughly matches ego-plus-wrist-camera policies when wrist views are clear (69% vs. 64%), and more than doubles their success when grasped objects block the wrist cameras (48% vs. 22%).
7 more specialized papers
- Network-in-the-Loop at Scale: GPU-Batched 5G Simulation for Massively Parallel Robot Learning Zifan Zhang, Mingzhe Han, Kannan Athreya et al.
- From Fragments to Global Maps: Learning Vectorized Map Aggregation with Large Language Models Ziwei Li, Yi-Tang Chen, Xiaoqi Wang et al.
- Mind the Refinement Gap: When Safe High-Level Robot Plans Produce Unsafe Executions Stabak Das, Priyesh Ranjan, Xiangfang Li et al.
- Permutation Robustness Is Not Enough: Action Collapse in Multi-Agent Transformer Policies Amit Thakur, Mukesh Singhal
- RIFAR: Reliability and Forgetting-Aware Replay for Continual Robot Learning Zirong Song, Zheng Lu, Haoran Liao et al.
- Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation Lik Hang Kenny Wong, Yiyao Ma, Xiu-Shen Wei et al.
- Autonomous Robotic Navigation for Endovascular Brain-Computer Interface Access Harry Robertshaw, Weijie Qi, Nikola Fischer et al.
Reasoning 10
Counterexample Generation via Per-Theorem Symbolic Verifiers: When Imitation Hurts and Reinforcement Repairs
LLMs that can prove a theorem often fail to disprove a closely related false one. The authors treat counterexample generation as producing a witness that a deterministic Python verifier written for each theorem can check, and release SymCE: 4,707 false undergraduate algebra and real-analysis conjectures, each with an executable verifier that doubles as the reward function. Training Qwen3-4B with counterexample-only SFT collapses its ability to recognize true theorems from 0.27 to 0.00, and RL with a sparse outcome-only reward repairs this and lifts it to 0.66. The collapse replicates across seeds and on Gemma-3-4B. The resulting 4B model beats every evaluated 7B open-weights math specialist and stays competitive with frontier commercial APIs.
Online Verification of Language Model Responses Under Cost Constraints
Checking every step of an LLM's multi-step reasoning with an expensive ground-truth oracle is impractical online, and prior work instead escalates from one fixed weak verifier to a strong one. OMVV (Online Multi-Verifier Verification) keeps a pool of weak verifiers that differ in cost and accuracy, and picks one each round using an online score combiner and an exponential-weights routing policy. It comes with a distribution-free, finite-time guarantee on false-accept and false-reject rates, and with sublinear regret against the best fixed verifier in hindsight. On reasoning benchmarks, OMVV reaches higher accuracy at lower verification cost than any single fixed verifier across a range of budgets.
HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning
Long thinking traces improve LLM reasoning but add substantial decoding latency. HyperThink replaces the trace with a lightweight hypernetwork that reads the question and predicts updates to a small subset of the base model's parameters. A vector-quantized decoder limits these updates to a finite set of reusable patterns. The hypernetwork is trained end-to-end on the base model's own outputs. After one hypernetwork pass, the adapted model writes a concise solution with no intermediate trace, and it improves the low-latency end of the accuracy-latency trade-off on math and general reasoning, with the largest gains near the non-thinking regime.
ULTRADISCOVERY: Abductive Exploration in an Interconnected, Epistemically Open Universe
ULTRADISCOVERY is an interactive world of five domains for testing abductive scientific discovery: an agent must revise a theory that initially works and predict the outcome of an unseen cross-domain intervention. A 2x2 design separately controls whether the representation needed for the explanation is left open or disclosed, and whether the evidence is scattered or aligned. Across eleven models, agents in the open condition often abandon the axiom they were taught, but none introduces the unobserved entity or rewrites the variables a replacement theory requires. No system makes the exact prediction within 200 paid actions, which points to composing evidence into a new representation as the main difficulty.
Learning from Repaired Reasoning: Root-Cause-Guided On-Policy Distillation
On-policy self-distillation (OPSD) supervises a student's own reasoning with reference solutions, but a reference solution explains the correct answer without addressing why the student's particular attempt went wrong. RC-OPD (Root-Cause-Guided On-Policy Distillation) locates the earliest substantive error in each failed attempt, writes a local fix, and tests that fix by letting the student continue from it. This diagnose, repair, and continue loop repeats within a fixed budget. Successful repair chains supervise the erroneous segments using the failure diagnoses, and supervise the valid prefixes with reasoning anchored to the repaired intermediate results. Across several datasets and model scales, the method mitigates reasoning mismatch and over-constraint of valid reasoning and yields substantial gains.
Reasoning Models Are Accurate but Unsound on Identification
When asked whether a causal effect can be recovered from observational data, a reasoning model can either refuse an identifiable query or give a formula for one that is not identifiable, and the second error is worse because no data can expose it. CERTID uses the sound and complete ID algorithm to certify whether each query is identifiable. It then checks returned formulas against structural causal models whose interventional distributions are known exactly, so equivalent formulas are graded correctly. On 1,200 certified instances evaluated with Gemini Flash, Gemini Pro, and GPT5.5, accuracy proves a poor proxy for soundness: the rate of false claims on non-identifiable queries varies seventeen-fold across models, even though identifiability decisions reach 97-100% accuracy.
Recursive Harness Self-Improvement for Frontier Reasoning Data Synthesis
Pipelines that synthesize harder and harder reasoning problems usually reuse generated problems as new seeds but never change the generation procedure itself. The proposed task-harness co-evolution framework also improves the procedure. During generation it turns solver failures into reusable skills, and after each batch it revises skills, prompts and workflows, keeping a change only if it yields harder valid tasks at bounded extra cost. Model weights and verifiers stay fixed. Over fourteen rounds across math, coding and science, mean solver accuracy on the generated tasks falls from 100% to 54.8%. A 27B student fine-tuned on 10K synthesized math examples reaches 62.5% on APEX, competitive with selected frontier models.
3 more specialized papers
- Hesitation Has a Geometry: Entropy-Trained Hyperbolic Probes for Sparse Activation Steering Zeyong Zhang, Tung Sum Thomas Kwok, Tengfei Ma et al.
- Learning from Evolving Errors: Adaptive Iterative Repair for On-Policy Distillation Rui Li, Liyang He, Zheng Zhang et al.
- Learning to Revise Reasoning with Segment-wise On-Policy Distillation Yuxiang Zhang, Ding Cao, Shuting Cui et al.