Monday, August 24, 2026

Hadith computation has been reshaped by transformer encoders, retrieval-grounded systems, and LLMs, but existing surveys mostly chart publication growth rather than judging which reported advances are methodologically durable. The authors run a critical narrative review that appraises representative primary studies against explicit evidentiary dimensions and argue the field should be assessed as an evidence-infrastructure problem — knowledge integration, provenance, expert supervision — rather than as isolated model performance.

• Searches across Google Scholar, Scopus, ACL Anthology, SpringerLink, ScienceDirect, and arXiv during late 2025 and early 2026 produced 42 shortlisted items narrowed to 32 records, with primary studies coded against six dimensions (corpus realism, evaluation setting, transfer, reproducibility, expert input, scholarly use) and deliberately not pooled into a single ranking because task definitions and metrics vary too sharply to compare.
• Progress is read through a three-level pipeline: Level 1 (isnad-matn separation) consolidated fastest into a reproducible benchmark area, Level 2 (narrator disambiguation, source verification, question answering) broadened without converging, and Level 3 shifted from isolated graph-building to pipeline-scale enrichment.
• Data work became a first-class contribution — Sanadset 650K covers 650,000 narrations from 926 books and exposes structural variation that canonical-only benchmarks hid, while the Rezwan LLM-assisted pipeline enriches 1.2 million narrations with multilingual layers and scoring by six domain experts.
• The structured appraisal is unflattering in aggregate: of the eight representative studies tabulated, only one tests cross-collection transfer affirmatively, one is fully open, and one carries full expert validation, and the narrator-disambiguation work shows a persistent gap between strong validation on artificial sanads and weaker performance on real test data.
• Remaining gaps are structural rather than architectural — concentration on the six canonical Sunni collections, near-absence of computational work on the commentarial layer (sharh, takhrij, fiqh al-hadith), weak linkage to Qur'an, seerah, and rijal literature, and word-segmentation fragility documented by the Noor-Ghateh benchmark — and the review itself concedes selection bias, underrepresentation of Arabic-only and gray literature, and an explicit interpretive preference for provenance and scholar-facing usefulness.

arXiv:2608.20364 PDF

Peer effects in LLM-agent populations can't be read off single-agent benchmarks, so PV-SST puts 12 persona-conditioned agents on a peer-voted social platform for six rounds and runs a preregistered, matched-exposure experiment across four topics, four seeds, and seven open-weight model variants. The headline finding is narrow but robust: a like-ranked feed makes agents write more alike, while coordinated multi-account attacks show no reliable stance-moving advantage once exposure is held fixed.

• Each trial has agents post JSON-structured messages under 180 characters, vote like/ignore/downvote on up to five peers, and feed those votes into the next round's ranking, with the paired model-topic-seed block — never the individual post — as the inferential unit across 448 trials and 112 complete blocks.
• Relative to a topic-only control, the peer-ranked feed raises final-round TF-IDF cosine similarity by +0.0082 (95% CI [0.0043, 0.0121], p=0.000105) in the four-family core panel and +0.0109 ([0.0069, 0.0151], p=0.000001) in the larger-variant extension, positive in every topic stratum and in six of seven models.
• The matched-exposure coordination test holds adversarial impressions fixed at 60 per trial for both one-source and four-source attacks, and the distributed-minus-single stance contrast fails the prespecified consistency criterion — +0.057 ([-0.009, 0.125], p=0.112) in the core panel but -0.040 ([-0.113, 0.035], p=0.332) in the larger variants.
• Minority suppression is model-dependent rather than general: opposite-side survival drops 3.9 percentage points in the core panel (p=0.0068), but that is concentrated in Qwen 3.5 4B at -12.5 pp with Gemma 4 E4B exactly null, and the larger panel is inconclusive at -1.0 pp (p=0.50).
• Both treatments are bundled — the feed arm mixes peer-post exposure with like ranking, and the four-source arm mixes account count with message-realization diversity — and an exploratory audit found keyword detectors missing paraphrases while two LLM stance judges agreed on only 43.6% of endpoint labels (Cohen's kappa 0.258), so the paper declines to rank misinformation interventions and claims nothing about human platforms.

arXiv:2608.20438 PDF

Subliminal trait transfer lets a student model absorb a teacher's behavioral disposition from data where the trait never appears semantically, but it has been unclear how that influence survives after the source data is removed or why its behavioral sign varies. Treating parameters and optimizer moments as one trainer state, an exact adjoint identity separates transport — observer-independent propagation of the source perturbation — from valuation, the sign and size that a particular future training path and readout assign to it.

• Training is modeled as a deterministic system over the complete state (w, m, v, τ), and discrete-time adjoint sensitivity analysis yields a path-integral identity in which total behavior change is the accumulated inner product of the backward "future value" with the forward source perturbation at the gradient port, with the forward tangent recurrence validated on Qwen2.5-0.5B at SSE/ZERO = 1.65×10⁻⁴ and correlation 0.9999 across 360 step-by-block cells.
• State surgery identifies AdamW's first moment as the causal carrier: an m-only transplant leaves parameters, hidden states, and outputs exactly unchanged at the cut yet grows a descendant under later source-free updates, while w+m reproduces the full physical descendant (SSE/ZERO 0.005–0.006) in 14/14 seed-route cells and the terminal behavioral response (0.0017–0.0079), consistent with an adjoint allocation placing 89.9% of the signed source contribution in the first moment at lag 24.
• Sending identical stored ancestry through matched engineered continuations flips the outcome — seed means of −0.658, +0.008, and +0.658 on Qwen under the S⁺, z_null, and S⁻ routes (7/7 seeds, sign-test p = 0.016) — and the same ordering holds in 12/12 Llama-3.2-1B seeds after eight updates with route-wise descendant norms nearly equal, with both contrasts growing in 9/9 seeds when the suffix is doubled to sixteen updates.
• A parameter-free full-horizon costate predictor recovers all 42 Qwen ordinary-route mean signs (against 29/42 for an always-positive rule) and all 21 resolved Llama signs, and observer-free transport replicates across Qwen2.5-0.5B, SmolLM2-135M, and Llama-3.2-1B at SSE/ZERO ≤ 3×10⁻³ as well as in non-LoRA MNIST MLPs and CNNs trained with AdamW and momentum SGD.
• Coverage is limited to 135M–1.1B parameter models under mostly LoRA fine-tuning, the compact predictor beats baselines only at or above 1024 source rows and degrades in the low-signal regime, and behavioral magnitude is system-dependent — CNN block allocations vary tenfold across seeds and TinyLlama shows opposing finite contributions with no stable behavioral sign — while exact attribution still requires the full trajectory plus one adjoint solve per route.

arXiv:2608.20442 PDF

Oversight practices like chain-of-thought monitoring and self-critique assume a model's verbal report tracks its actual computation, but no benchmark grades that report against a known internal reference. OWMI (Open-Weight Masked Introspection) supplies one: it perturbs a specific internal object — a residual-stream site, attention head, or sparse-autoencoder feature — then asks the model what changed, scoring the answer against sham runs, impact-matched random perturbations, and a text-only observer that sees only the visible output.

• Across over 78,000 measurements on eight open-weight models from seven labs (0.5B to 15B parameters) and twelve benchmarks including MMLU, GSM8K, and HumanEval, report discrimination over 11,216 paired trials reached AUROC ≈ 0.5007, and rather than merely failing to reject chance, an equivalence test bounds the effect below 0.15 percentage points of AUROC (p < 0.0001).
• Two sensitivity anchors close the obvious escape routes: a LoRA fine-tune of Qwen2.5-7B-Instruct trained to make exactly this report clears the identical pipeline at d′ = 5.15, AUROC ≈ 1.0 on 100 held-out directions, so the instrument is not blunt.
• A linear probe on the same layer-16 activations recovers intervention presence at 95.8% and 75.0% held-out accuracy in the two dose-calibrated models against 50% chance, with no permutation in 200 label-shuffled refits reaching either margin, and re-harvesting the probe at every deeper layer up to the last one before generation separates intervention from sham with zero held-out error — the information is available exactly where a report would have to be formed.
• One channel is not silent: in Qwen2.5-7B-Instruct the yes-or-no answer is constant across every scorable trial (AUROC exactly 0.500) while the attached confidence discriminates at 0.647, suggesting the signal reaches a graded quantity the model emits without reaching the words it chooses.
• The controls are unevenly realized — impact matching holds for one model, overshoots for a second, and is only unit-norm for the other six; the observer bound reported is the weakest same-model zero-shot variant; reconstruction went unscored because these interventions carry no ground-truth concept label; the spontaneous track produced no complete intervention-sham pair; and item counts are small (32 per cell for the dose battery, 2 for the eight-model sweep), so the claim covers the models, sites, and scales measured rather than closed-weight or frontier systems.

arXiv:2608.20569 PDF

Open-ended language-model benchmarks normally inherit an evaluator — a human panel, a model judge, or a brittle exact-match key — which entangles the grader with the systems under test and discards the difference between plausible wrong answers. FlavourBench replaces the judge with a versioned culinary runtime that enumerates and scores every possible answer before any model runs, turning open-ended culinary reasoning into a table lookup.

• The Epicure runtime (1,790 ingredients in a 300-dimensional space) compiles into three-of-eight ingredient selection tasks across substitution, pairing, and constrained composition, with all 56 portfolios per task scored on a frozen 0–100 map so a parsed FINAL_SELECTION line resolves to a score without post-hoc interpretation and without exposing Epicure as a tool.
• The leaderboard is a complete rectangular matrix — 27 endpoints × 534 identical tasks = 14,418 cells — where task eligibility is decided score-blind from parser validity alone before any selection or score is loaded, so no model is advantaged by answering a different subset, and inference runs 50,000 anchor-cluster bootstrap replicates for simultaneous 95% bands plus 100,000 sign-flip draws with Holm correction across all 351 pairs.
• Grok 4.6 takes the top point estimate at 65.1 (simultaneous 95% CI 61.0–69.2), just ahead of Gemini 3.1 Pro at 65.0 and GPT-5.6 Sol Pro at 64.2, with Command R+ last at 47.9 — but only 101 of 351 paired contrasts survive multiplicity correction, so most adjacent ranks are explicitly unresolved rather than merely close.
• Almost all the between-model separation comes from the constraint family, where infeasible portfolios score zero and the spread runs 32.0 to 58.2, versus a compressed 56.7–69.0 on substitution and 54.9–73.7 on pairing against exact chance baselines of 45.2 and 45.0; two independently compiled panels reproduce the ordering at r = 0.89 (rank ρ = 0.80).
• The score measures agreement with one published Epicure release rather than universal human taste, within-task min–max normalization discards absolute utility magnitudes, the tasks cover constrained ingredient selection rather than recipe generation or kitchen planning, and the proposed use of the dense reward map as a training signal is released as an interface but never tested for transfer to human cooking outcomes.

arXiv:2608.20574 PDF

Agent training environments are usually built around the tasks or benchmarks they are meant to support, which limits how much genuine world diversity you get by adding more tasks. AgentMercury inverts that ordering: a "Planet" role compiles a high-level business scenario into a persistent executable world — entities, services, tools, state schema, seeded initial state, and cross-service invariants — and only afterwards instantiates tasks, rubrics, and trajectories from it.

• Worlds are factorized into company identity, service graph, state schema, seeded state, and an invariant set that is deliberately not enforced by the transition function, so the agent must satisfy cross-service constraints itself while deterministic SQL-based checks over the final database state supply the reward; the visible view of an invariant appears as in-world policy documents the agent has to discover, the hidden view is the grader.
• The released collection is 4,783 executable environments across 14 industries and 50 countries, yielding 43,300 task instances used as reinforcement learning substrate with GRPO and a single-rollout asynchronous variant (SAO).
• On the in-domain EnterpriseOps-Gym benchmark, Qwen3.5-4B goes from 12.3 to 15.7 average with GRPO and Qwen3.5-35B-A3B from 24.8 to 28.1 (28.3 with SAO), while out-of-domain transfer is the more striking claim: AIME26 45.9 → 56.0, HMMT 28.5 → 35.4, LiveCodeBench 36.6 → 44.0, and SciCode 22.6 → 25.7 for the 4B model, plus BFCL 31.1 → 42.1 at 35B, all from environments built without sight of these benchmarks.
• Environment construction itself is shown to be learnable — fine-tuning Qwen3.5-35B-A3B on 29,823 construction-trace samples raises the rate of authoring worlds that pass all 12 structural validators on 30 held-out briefs from 3.3% to 83.3%, matching strong API models, though prompting that same fine-tuned model with an explicit construction recipe collapses it back to 10.0%, with 27 of 30 failures on the cross-service check.
• Caveats worth weighing: gains are not uniform (CSM drops 9.2 → 5.6 at 4B, tau-3 Telecom is flat), the tau-3 results carry enormous run-to-run variance even after training (65.5 ± 23.8), SAO was unstable at 4B and excluded from the main table, the authoring evaluation rests on only 30 briefs, and Table 3's numbers do not reconcile with the surrounding text — it describes five API models with a 66.7–90.0% range and an 80.7% mean while listing four models topping out at 83.3% with a 78.3% mean.

arXiv:2608.20634 PDF

Real-world image search queries are compositional — "find this shirt in pink" names an entity to keep, an attribute to change, and context to ignore — yet re-rankers either collapse all of that into one opaque embedding or into free-form chain-of-thought that silently drops constraints. EviRank recasts multimodal re-ranking as semantic constraint satisfaction, parsing any query into a typed evidence package and reducing ranking to verification against it.

• An MLLM teacher normalizes text-only, image-only, or composed queries into one modality-agnostic Evidence Frame — six slots (entities, attributes, actions, relations, scene, key details), each carrying required, forbidden, and ignorable statements — and candidates are then scored by a deterministic rubric (forbidden penalty β=0.75, ignorable statements acting as an invariance mask rather than a scored term) followed by an evidence-grounded listwise pass over the top M=5.
• Both stages are training-free and cost a fixed two MLLM calls per query, while the same run emits calibrated scores, self-assessed confidence, and teacher-flagged hard pairs that distil into a Qwen3-VL-2B-Thinking student needing only the raw query and candidate at inference.
• Across five benchmarks the method sets state of the art: 95.6% R@1 on Flickr30k with BLIP-2 (+6.3 over CoTMR), 69.5% on COCO with CLIP-ViT-L/14 (+9.6 over CoTRR), and 91.5% / 86.9% R@1 on SoP / CUB-200 (+7.7 / +8.6 over LoCoRE-base), with the distilled student retaining over 90% of teacher capability at ~800ms per query.
• Ablations attribute the gain to the structure rather than the prompting — removing all evidence costs up to 6.7 points R@10 on FashionIQ Toptee, structured evidence adds +6.5 R@1 on COCO against +0.5 for free-form augmentation, and rubric plus listwise beats either alone by +5.6 R@10 on FashionIQ.
• Extraction is stable across repeated runs, prompt perturbations, and four different teachers (Kendall's τ ≥ 0.89, Top-1 agreement ≥ 91%), and notably a cheaper Gemini-3-flash teacher loses 4.6 R@10 in ranking quality while leaving evidence stability intact.
• Evaluation covers only English still-image benchmarks with no multilingual, video, or production user-study results, and the strongest variants still depend on a proprietary frontier MLLM at test time.

arXiv:2608.20886 PDF

Deploying an 11B-parameter vision-language model on a phone requires sub-3-bit weights, but aggressive quantization usually wrecks multimodal quality and the original training data needed to repair it is rarely available. Llama-Mobile pairs a vector-quantized 2.7-bit weight format co-designed around the Arm SIMD decode path with quantization-aware distillation on data the model generates for itself, compressing Llama 3.2 11B Vision Instruct from 21.3 GB to 3.7 GB.

• The S3D8 format packs three weights into one byte as a shared 5-bit index into 32 Lloyd-Max centroids plus three per-weight sign bits, with the signs folded into permuted 64-entry lookup tables so that five SIMD logical instructions produce the indices for three TBL lookups that decode 48 elements straight to INT8.
• Because the original training recipe is assumed unavailable, quantization-aware training distills the bfloat16 teacher onto its own generations over ImageNet images, sampled from a pool of 495 generic comprehension prompts with randomized instruction blocks and length constraints — an ablation shows a single fixed prompt ("Describe the image:") yields substantially worse downstream accuracy, since diverse responses are what exercise instruction-following behavior.
• At 2.68 bits per parameter with 8-bit activations, the quantized model averages 0.661 across VQAv2, ChartQA, DocVQA, and AI2D versus 0.744 for bfloat16, while size-matched scalar baselines under the identical QAT procedure collapse to 0.565 (student-t), 0.436 (lloyd-max), and 0.347 (block-scaled INT) — roughly 22% extra compression at equal task performance.
• The quantization procedure matters as much as the format: with GPTQ instead of QAT, S3D8 scores only 0.340 at the same rate (against 0.018 for rate-matched INT), and a custom C++ Arm implementation reaches 3.8 tokens/s decoding on a Pixel 8a — a configuration where INT8 weights do not fit in memory at all — and 36.8 versus 26.4 tokens/s against INT8 on Graviton4.
• The gains are confined to memory-bound decoding, with S3D8 slightly slower than plain INT8 on compute-bound text and vision prefill, and the evaluation covers a single model on Arm CPUs across four VQA benchmarks, with mixed precision offering no real help — keeping the output projection and vision encoder in INT8 costs 944 MB for a 0.006 average gain.

arXiv:2608.21134 PDF

LLM agents have accumulated a stack of engineering paradigms — prompt, context, harness, and loop — that all optimize a single agent's behavior, but tasks requiring heterogeneous expertise, parallel subtasks, independent verification, and persistent state exceed what one agent's context and control loop can organize. The proposed answer is Graph Engineering: making the relationships among tasks, agents, and runtime state into explicit graph structures that the system can schedule, coordinate, and repair against, a capability the authors call System Intelligence.

• The survey frames an agent as Loop(LLM + Harness) and a system as the tuple of agent team, shared resources, environment, coordination mechanisms, and system state, then argues the jump to system intelligence needs three graph views rather than simply more agents.
• Task Organization covers goal-decomposition graphs that expose dependencies for parallel scheduling (HuggingGPT, ReWOO, LLMCompiler's dataflow DAG, Plan-over-Graph) and workflow optimization that treats the executable graph itself as a search target (GPTSwarm, ADAS, AFlow, MermaidFlow, VFlow), plus runtime-adaptive variants like DyFlow, EvoFlow, and QualityFlow that rewrite the workflow mid-execution on feedback.
• Agent Coordination splits into capability modeling (DyLAN, MasRouter, AutoAgents, SkillGraph, MaAS's agentic supernet), team topology, and communication graphs, with the authors noting that most current capability representations are per-task scores or routing policies rather than persistent, reusable relational structures.
• Runtime State Management records events, dependencies, and state transitions as an auditable graph so partial progress can be recovered and the faulty stage localized — the paper's diagnosis being that a single agent's context is not organized state, so an early error stays hidden until a long-horizon task fails at the end.
• The main limitation is that this is a position-and-taxonomy survey with no benchmark numbers or empirical comparison of graph-engineered systems against single-agent baselines, and the authors themselves concede that persistent System Evolution — structures that improve across runs — remains rare in the applications they review, with open problems in ontology engineering and graph-native agent operating systems left as future work.

arXiv:2608.21156 PDF

Safety tuning is usually applied globally, so the same refusal-shaped parameter update that blocks jailbreaks also perturbs benign prompts and costs reasoning accuracy. CLEAR reframes alignment as input-conditioned control: a small hidden-state gate predicts a continuous risk score that scales a safety LoRA adapter on top of a frozen backbone, so benign inputs stay close to the original model while harmful ones get a strong safety update.

• A two-layer MLP gate reads prompt-only hidden states from three mid-to-late transformer layers, emits a score g(x) ∈ [0,1], and each adapted projection computes h' = (W + g(x)ΔW)h with the same scalar reused for prefill and every decoding step — the adapter is trained only on unsafe WildJailbreak completions, while subtype-weighted cross-entropy plus a hard pairwise margin loss push adversarial-benign and adversarial-harmful prompts apart in gate space.
• On Llama-3-8B-Instruct, HarmBench attack success rate drops from 32.25% to 0.50% while GSM8K accuracy holds at 73.46%, roughly 7 points above the 66.34% and 66.72% of globally applied SFT and always-on LoRA; on Gemma-2-2B-it the method reaches 0.00% ASR with 96.00% unsafe-prompt refusal and recovers GSM8K from ~38% to 41.62%.
• The internal gate needs only 664K parameters versus 279M for PromptGuard and 8B for Llama Guard 3, and gate separability improves with scale across Qwen2.5-Instruct — ROC-AUC rises from 0.78 at 0.5B to 0.96 at 7B, with the 7B model preserving GSM8K exactly at 82.18%.
• Under adaptive latent perturbation attacks injected at embedding, middle, and final layers, CLEAR records ASRs of 0.0%, 7.1%, and 18.4% against 54.0%/48.0%/31.0% for standard LoRA and 47.0%/35.0%/22.0% for full SFT, suggesting conditional routing makes harmful latent transitions harder to induce.
• The gains are contingent on gate reliability: over-refusal on safe XSTest prompts rises to 9.60% on Gemma-2-2B-it (from a 4.80% base), always-on LoRA still edges out CLEAR on raw Llama ASR (0.00% vs 0.50%), MMLU drops about 3 points, and the evaluation covers only single-turn text safety on small open-weight models, leaving unseen jailbreak styles and distribution shift untested.

arXiv:2608.21278 PDF