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HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

HypoEvolve couples a generational genetic algorithm with specialized LLM agents to generate drug-repurposing hypotheses, beating six baselines on DepMap selectivity (0.171 vs 0.115).

HypoEvolve coordinates specialized LLM agents through a generational genetic algorithm in which scientific judgments and new proposals reshape a hypothesis population. Evaluation centers on drug repurposing, linking mechanistic explanations to target-level biological claims assessed via external measures adapted from DepMap and Open Targets. Across 34 cancer types, HypoEvolve scores highest against six baselines on both measures, with DepMap selectivity of 0.171 versus 0.115 for the strongest baseline, and gains generalize to held-out cancer types.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research 2 sources1

ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents

ScienceBuddy couples harness evolution with model reinforcement learning so scientific agents continually self-improve from researcher feedback in an interactive workspace.

The authors release ScienceBuddy, an interactive scientific research workspace that transforms researcher requests, feedback, and execution evidence into tasks and evaluation rubrics for continual learning. Its recursive-in-recursive self-improvement paradigm couples harness evolution (inner recursion, model fixed) with model reinforcement learning under the improved harness (outer recursion). Case studies span four scientific task families, and the system is released to the scientific community as a research product.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research 2 sources

Not All Relations Are Equal: Relation-Balanced and Calibrated Graph Learning for Provenance-Based Intrusion Detection

RECAL improves provenance-based APT detection with relation-balanced masked graph learning and calibrated errors, reaching 99.99% F1 on DARPA E3 datasets.

RECAL is an unsupervised framework for provenance-based intrusion detection that uses relation-balanced masked graph learning to capture rare interaction patterns, addressing statistical heterogeneity where relation frequencies differ by roughly 140,000X in CADETS. It calibrates reconstruction errors against each relation's benign error distribution to produce comparable anomaly evidence and reduce false alarms. On three DARPA E3 datasets, RECAL achieves F1 scores of 99.99%, 99.93%, and 99.99%, outperforming the best baseline on each dataset, and reduces mean false positive rate by approximately 105X, 4X, and 41X versus the lowest-FPR baseline.

arXiv cs.CR · 2d agoResearch

ActReview: Rebuttal-Guided Training Data and Rubric Rewards for Actionable Peer Review Generation

ActReview post-trains Qwen3-8B-Base on OpenReview rebuttals to generate actionable peer-review feedback with grounded revision suggestions, benchmarked on 1,000 curated instances.

The paper defines Actionable Peer-review Generation as diagnostic claim generation plus revision suggestion generation and introduces ActReview, a rebuttal-guided post-training framework. From OpenReview review-rebuttal threads the authors build ActReview-40K, aligning reviewer weaknesses with author responses grounded in localized paper evidence, and post-train Qwen3-8B-Base with multi-task SFT followed by GRPO using weakness-specific rubric rewards. They also release ActReview-Bench, a human-curated 1,000-instance benchmark, on which ActReview outperforms prior specialized review-generation models on actionability and grounding while remaining competitive with strong prompt-based LLMs. Human evaluation confirms improved revision usefulness but identifies a remaining gap in technical accuracy.

Hugging Face daily papers · 9d agoAI research

From Reweighting to Rewriting: Unlocking the Intervention Effects of Influential Samples in Training Data Attribution

Study shows rewriting responses of influence-selected training examples shifts LLM behavior more strongly than reweighting the same samples.

The paper examines training data attribution, arguing that influence functions identify high-leverage examples whose value goes unrealized under conventional weight-based reweighting interventions. It introduces influence-guided response rewriting, which replaces the responses of influence-selected examples with behavior-aligned or behavior-opposed supervision while keeping instructions fixed, tested across four open-weight LLMs using epistemic abstention as the primary testbed. Rewriting produces stronger, more persistent, and bidirectional behavioral shifts, including on safety refusal, while reweighting the same examples yields weak, inconsistent effects. The results motivate intervention-aware evaluation of TDA methods.

Hugging Face daily papers · 15d agoAI research

UniH^3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration

UniH3 unifies hierarchical homogeneity and heterogeneity modeling for all-in-one medical image restoration across modalities and degradation types.

UniH3 introduces a Hierarchical Homogeneity Memory module that distills shared anatomical priors from high-quality images, injected via a Homogeneity-Guided Attention mechanism. A Hierarchical Heterogeneity Balancer mitigates inter- and intra-task conflicts during multi-task optimization. It achieves state-of-the-art on MedIR-2D-500K and MedIR-3D-3D benchmarks for both all-in-one and single-task restoration, with code released on GitHub.

Hugging Face daily papers · 7d agoAI research

VeriScene: Reconstructing Crime Scenes from Legal Evidence via World-Model Agent

Researchers present VeriScene, a world-model agent that reconstructs crime scenes from forensic photos and witness statements with traceable, physically plausible output.

VeriScene orchestrates a world model to fuse forensic photographs and witness statements of varying reliability into cited narratives and physically plausible re-enactment videos. On a 25-scenario benchmark with planted unreliable testimony, it reaches 0.9014 evidence coverage and 0.7217 factual consistency on 20 test scenes. It outperforms an end-to-end multimodal-LLM baseline by 20.35% in factual consistency and 34.88% in temporal coherence at USD 1.82 per scene.

arXiv cs.CR · 8d agoAI research

What We Learned by Reproducing 2,200 papers from ICML

Hugging Face shares lessons from openly reproducing 2,200 ICML 2026 papers, examining reproducibility and open implementation practices in machine learning research.

Hugging Face published a retrospective on its open reproduction effort covering 2,200 papers from ICML 2026. The post summarizes lessons learned about reproducibility and building open, community-driven implementations of published machine learning research. No detailed article text was available in the feed.

Hugging Face Blog · Aug 13, 2026AI research

Anchoring Clinical Events in Time: UID-Preserving Multimodal Reconstruction and Source-Grounded Adjudication

UID-preserving multimodal framework plus GAVEL LLM judge improves clinical timeline reconstruction, boosting event recovery 43% over prior matching.

The paper introduces a UID-preserving framework linking each narrative clinical event to its source span through text-only estimation, structured-evidence retrieval, timestamped source-row grounding, and joint revision. GAVEL, an LLM judge, compares UID-aligned timelines against narrative and structured records. Across six open-weight models and 40 mixed-critical-care summaries, GLM 5.2 multimodal revision improved temporal agreement without reducing event recovery and performed competitively with clinician annotations, while DeepSeek V3.2 did not benefit from multimodality. The pipeline achieves 43% increased event recovery with occurrence-level provenance.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research1

ReCite: Agentic Reasoning for Faithful Citation

ReCite is an agentic citation framework using claim-level reasoning and verification, outperforming large generative models in strict citation accuracy.

ReCite is a decoupled agentic framework that orchestrates location perception, intent-aware query planning, and reflective verification for citation recommendation. Trained on synthesized reasoning trajectories, the agent verifies claim-evidence consistency and triggers self-correction loops when retrieved candidates lack logical support. Experiments show the lightweight framework outperforms state-of-the-art massive generative models in strict citation accuracy, addressing misattribution where cited papers are real but logically unsupportive.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research

What Else Needs Fixing? Exploring Cost-Effective Test-Time Compute for Revision Propagation in Artifacts Generated Through Conversation

A new benchmark shows LLMs reach 68.3-93% accuracy propagating local revisions across conversationally generated artifacts, with parallel-sample selection most cost-effective.

The paper introduces a benchmark for revision propagation: when users request a local change, LLMs must identify dependencies and update all affected parts of an artifact generated through conversation, where context lives in the chat history. Nine revision methods, including sequential reflection and parallel sampling variants, were evaluated on gpt-oss-20b/120b, gpt-5.4-mini, and qwen3.5-9b/27b/122b. Baselines scored 68.3-93% accuracy, and selecting among three parallel samples via LLM-based or medoid selection improved accuracy by 2.2-9.7% as the most cost-effective test-time compute strategy. Code and dataset are released.

Hugging Face daily papers · 14d agoAI research

Mi-Ripple: Restoring Images Degraded by Iterative AI Editing

Mi-Ripple is a diagnosis-guided restoration workflow that removes digital ripple artifacts introduced by iterative AI image editing while preserving structure.

Iterative reference-conditioned image editing can introduce grid-like and granular textures known as digital ripple. Mi-Ripple separates periodic lattice artifacts from content-entangled granular texture, then applies selective spectral notching, structure-aware smoothing, and cleaned-reference regeneration. In fourteen notch-only executions, whole-image residual standard deviation was 0.08-0.44 in CIELAB lightness units, and reference cleaning reduced output debris density by 45% in a paired example.

Hugging Face daily papers · 7d agoAI research

DynSHAP: Towards Explainable Dynamic Survival Analysis

DynSHAP extends SHAP explainability to dynamic survival analysis, treating time-feature pairs as Shapley players for longitudinal clinical predictions.

DynSHAP adapts marginal SHAP estimators to dynamic survival analysis by treating time-feature pairs as players in the Shapley game, handling longitudinal irregular inputs and functional survival outputs. Temporal DynSHAP learns linear feature dependencies over time and addresses them with conditional sampling. On synthetic data with ground-truth attributions it recovers temporally dependent features more accurately than marginal estimators, and it produces faithful attributions on two real-world clinical datasets across two DSA architectures.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research

Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillation

OPRD distillation enables weak-to-strong generalization by amplifying verifier-supported policy updates, outperforming existing RL and distillation methods with fewer student updates.

On-Policy Reverse Distillation (OPRD) evaluates a weak teacher's policy shift relative to its reference policy on student rollouts and amplifies the verifier-supported component of the student's policy gradient. This rescaling preserves the stationary points of policy optimization while letting the student learn beyond the teacher's capacity ceiling. In successive model transfer and multi-teacher distillation, OPRD achieves higher performance with fewer student updates than existing RL and distillation approaches, and response-style analysis shows students remain closer to verifier-RL-trained models than to their weak teachers.

Hugging Face daily papers · 9d agoAI research

When Models Edit Too Much: On the Fidelity of Minimal Code Edits

A 400-task BigCodeBench evaluation shows frontier LLMs widely over-edit code; a preservation instruction cuts excess edits and raises Pass@1 by 2.3 points.

Researchers built an evaluation framework from 400 BigCodeBench problems with injected AST-level corruptions, each with a known minimal patch, to measure over-editing in LLM code repair. Even strong models like GPT-5.5 produce unnecessarily large edits despite high Pass@1. Adding a preservation instruction reduced average excess Levenshtein distance from 0.195 to 0.131, cut added cognitive complexity by 26.6%, and raised Pass@1 by 2.3 points. Reinforcement learning post-training gave the best out-of-domain edit-fidelity trade-off, while supervised fine-tuning overfit to seen corruption patterns.

Hugging Face daily papers · 14d agoAI research1

Agora: Git as Shared Memory for Collective AutoResearchnew

Agora records multi-agent research as an append-only Git DAG; 13 LLM workers ran nearly 12 days on a weight-transfer problem.

Agora stores every result, hypothesis, and verification as an immutable commit in a Git-stored DAG, with a derived index exposing the frontier and verification status of claims. In a nearly 12-day run, 13 language-model workers with no assigned tasks or central planner published 1,703 contributions on initializing a frozen 119.6M-parameter attention-SSM hybrid from 141 donor models. They improved the evaluator from 3.39 to 1.899 bits per byte, closing 62% of the gap to a trained GPT-2 124M, with 165 independent reproductions posted and none failing.

Hugging Face daily papers · 1d agoAI research

ActReview: Rebuttal-Guided Training Data and Rubric Rewards for Actionable Peer Review Generation

ActReview post-trains Qwen3-8B-Base on 40K rebuttal-derived instances with rubric rewards to generate actionable, grounded peer-review feedback, plus a 1,000-instance benchmark.

The framework builds ActReview-40K from real OpenReview review-rebuttal threads, aligning reviewer weaknesses with author responses and grounding feedback in localized paper evidence. Qwen3-8B-Base is post-trained with multi-task supervised fine-tuning followed by GRPO using candidate-aware, weakness-specific rubric rewards. Experiments show improved actionability and grounding over prior specialized review-generation models, supported by ActReview-Bench, a human-curated 1,000-instance evaluation set. Human evaluation confirms better revision usefulness while noting a remaining gap in technical accuracy.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research

How Does mHC Use Its Residual Streams? Selective Routing and Near-Identity Mixing

Analysis of DeepSeek-V4-Flash shows four-stream mHC residual blocks use only about two streams effectively, with late-layer mixing providing little benefit.

The study examines the four-stream residual pathway of DeepSeek-V4-Flash, finding typical attention or FFN sites effectively use about two streams and that residual mixing is modest, occurring primarily in early layers. Replacing late mixers with identity increases C4 perplexity by only 1.9% while replacing early mixers raises it by 41%. Retaining the three largest routing weights per token increases perplexity by at most 2.7%, showing the model uses only part of the flexibility afforded by the four-stream design.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research

LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction

LongAgent autonomously searches variable sets and temporal windows to predict longitudinal medical outcomes, beating the strongest non-agent baseline on synthetic data.

The paper proposes LongAgent, an agent-based method that searches over combinations of variable sets, temporal windows and aggregation functions for outcome prediction on heterogeneous medical longitudinal data. It uses a history memory of previous searches and numerical evidence to guide exploration. On synthetic data it achieves mean RMSE 1.7376, improving over the best non-agent baseline by 0.0151 (95% CI [0.0045, 0.0260]; p=0.0273), and performs comparably to the best baseline on a real clinical dataset.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

MInTRL: Off-policy Intervention can boost On-policy RL

MInTRL injects sparse judge corrections into on-policy RL rollouts, expanding exploration beyond on-policy sampling while preserving learnability on math and code benchmarks.

Minimal Intervention Reinforcement Learning periodically has a judge-intervention policy replace erroneous suffixes of the current policy's output with short corrections, then returns control, keeping trajectories largely on-policy. Training uses a sequence-level advantage-regression objective that removes the need for importance sampling. Across math and code benchmarks it consistently beats standard on-policy and off-policy baselines, remains effective with self-intervention, and performs best at moderate intervention intensity.

Hugging Face daily papers · 6d agoAI research

Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability

A controlled study finds agent memory portability varies sharply: fixed-schema knowledge graphs survive model swaps while compressed notes degrade.

The study compares preserving an agent's history as raw long context, RAG chunks, compressed natural-language notes, or fixed-schema knowledge graphs across model upgrades, using 48 synthetic histories and two open-weight sub-10B-parameter models. Fixed-schema KG accuracy changed by only +0.0004 ± 0.0020 after a writer swap, while compressed NOTES shifted asymmetrically by +9.91 or -13.28 percentage points depending on migration direction. Mixed 50/50 embedding migrations captured only 4.96 of an 11.90-point RAG re-embedding gain; 80% of the NOTES deficit came from information lost at construction, and 81% of the RAG deficit from retrieval failures. Store-only repair of NOTES failed to reach 90% recovery in all 48 cases, while retaining raw histories enabled recovery in 34 of 48 for one direction.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research1

Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research1

Forgetting Without Restarting: Execution-State Unlearning for Stateful LLM Agents

Researchers propose provenance-guided selective replay letting LLM agents forget revoked information without restarts, matching full reset behavior.

The paper formalizes execution-state unlearning for stateful LLM agents, requiring that agents behave as if a revoked memory record was never observed across transcripts, compressed memory, tool plans, and KV caches. It proves exact unlearning requires at least T-τ+1 recomputed transitions and that Provenance-Guided Selective Replay attains this bound via a provenance graph, KV cache cropping, and sanitized replay. In audits across three agent suites, nine baselines, and three model families, memory deletion left leakage unchanged, instruction-based forgetting collapsed under elicitation (Leak@probes = 1.00), and selective replay matched full resets at up to 9x fewer recomputed tokens.

arXiv cs.CR · 12d agoAI safety & security1

DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents

Researchers release DianShi-RxnDB, a database of roughly 24 million organic reaction instances extracted automatically from USPTO and EPO patents since 1976.

DianShi-RxnDB is built by a fully automated pipeline integrating patent text, images, and reaction schemes, yielding about 24 million reaction instances, of which 14.8 million (61.7%) pass automated qualification checks. Manual evaluation of 1,300 sampled instances showed 92.95% field-level accuracy, and comparisons with Pistachio found advantages in deduplicated record counts and granularity. The platform offers a web research workbench and a Model Context Protocol (MCP) service enabling AI agents to perform composable structured retrieval.

Hugging Face daily papers · 11d agoAI research

[AINews] 10% worse, 100x cheaper, 10000x faster: Why Simulation is taking over

Latent Space argues AI training pipeline stages—rewards, data, teachers, curricula, environments—are flipping from human-made to model-made simulation.

Latent Space's AINews essay traces how each component of AI training has turned synthetic since 2022: reward models (InstructGPT, RLAIF), synthetic pretraining data (Microsoft Phi, NVIDIA Nemotron-4 340B), model teachers (Alpaca, DeepSeek-R1 distillation), and self-generated curricula (Self-Rewarding Language Models, SPIN). In 2026 it highlights Karpathy's autoresearch loop—700 experiments yielding 20 kept improvements, cutting GPT-2 training time from 2.02 to 1.80 hours—and Z.ai's GLM-5.3 fully synthetic RL environment, judging, and verification stack. It frames these shifts as 'simulation': 10% worse but 100x cheaper and 10,000x faster than human equivalents.

Latent Space · 25d agoAI industry

A Princeton Researcher Proposes Recurrent Looped Transformer (RLT) that Carries Decoder State across Every Token, Fixing 96 Blocks per Token with Unbounded Temporal Depth

Princeton researcher Yifan Zhang proposes Recurrent Looped Transformer, carrying full decoder state across every token for unbounded temporal depth.

Yifan Zhang's technical report defines the Recurrent Looped Transformer (RLT), pairing a causal encoder with a recurrent decoder whose final output and layerwise sliding-window attention cache carry into every subsequent token with no prompt-response boundary reset. The reference configuration ties 48 encoder and 48 decoder layers, executing 96 logical blocks per token while the state path grows to 48t blocks after t tokens at fixed per-token compute. The report details RL replay contracts that rebuild all states under current parameters and exact prefix snapshots for multi-turn serving, but explicitly reports no measured efficiency, reasoning quality, or scaling results.

MarkTechPost · 3d agoAI research1

TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription

TART pipeline transcribes guitar audio to tablature with fingering and technique labels, outperforming prior baselines by up to 8.5 Tab F1 points.

TART is a modular four-stage pipeline for audio-to-tablature guitar transcription, combining an audio-to-MIDI transcription model, an expressive technique classifier, an audio-conditioned T5 encoder-decoder for string-fret assignment, and an automated tablature generator. In zero-shot evaluation on GuitarSet, EGDB, and two noisy augmented benchmarks, it averages 81.35% audio-to-MIDI F50 (+6.67 over the best baseline), 71.8% string-fret Tab F1 (+8.5), and 54.08% end-to-end Tab F1. The authors state it is the first framework to generate tablature with both fingering and expressive technique annotations directly from guitar audio.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research2

Evidence-Grounded Agentic Formulation Development in an Autonomous Laboratorynew

Andromeda 2, an agentic laboratory system, reaches a 50% high-performance hit rate for paclitaxel SEDDS formulations versus 17% for its predecessor and 2% for DoE.

Andromeda 2 is an agentic system that reasons over structured in-house experimental evidence and invokes computational and experimental tools to design and execute successive formulation batches for self-emulsifying drug delivery systems (SEDDS). For paclitaxel it achieved a 50% high-performance hit rate versus 17% for Andromeda 1 and 2% for a wet-lab DoE campaign, identifying 12 formulations meeting all four target product profile objectives versus 6 and 0. A selected full-TPP formulation reached approximately 19% w/w apparent paclitaxel loading, about 3.3-fold higher than a published paclitaxel S-SEDDS, and an ablation showed structured evidence access increased mean AUC by 34%.

arXiv cs.AI / cs.LG / cs.CL · 10h agoAI research