Sources

New research shows how AMIE, our medical AI, could help manage health conditions. blog.google

Research in “Nature” shows our conversational AI system matches primary care physicians in complex disease management.

Introducing LifeSciBench, an expert-authored, expert-reviewed benchmark for evaluating how AI systems handle real-world life science research tasks and decisions.

A near-autonomous AI chemist improves a challenging reaction in medicinal chemistry openai.com

OpenAI and Molecule.one show how a near-autonomous AI chemist using GPT-5.4 improved a key drug-making reaction, advancing medicinal chemistry research.

Toward Generalist Autonomous Research via Hypothesis-Tree Refinement huggingface.co

An AI framework called Arbor enables autonomous scientific research by combining strategic coordination, isolated hypothesis testing, and a persistent knowledge tree to iteratively improve research outcomes across multiple domains.

On Subquadratic Architectures: From Applications to Principles huggingface.co

A survey of subquadratic architectures finds xLSTM outperforms Mamba-2 and Gated DeltaNet on sequence modeling, crediting stronger state tracking and memory dynamics. The paper traces gains to gating choices that help length generalization, with applications spanning code pre-training, distillation, and time-series foundation models.

Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code huggingface.co

Grammar constraints meant to enforce syntactic validity in code generation double as an attack surface. The authors introduce CodeSpear, which hides honeypot logic inside grammatically valid output to bypass safety filters, plus CodeShield, an alignment defense that restores semantic harmlessness without sacrificing structural diversity.

POISE: Position-Aware Undetectable Skill Injection on LLM Agents huggingface.co

POISE plants malicious triggers inside ordinary-looking skill instructions using YAML-header and body injections, evading scanners that only flag privileged tool calls. The position-aware attack reports high success rates against Codex and GPT-5.2 agents, exposing a blind spot in current static defenses.

Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMs huggingface.co

ModSleuth is an agentic auditor that reconstructs LLM dependency graphs from public artifacts, resolving contradictions in cards, configs, and weights. The system surfaces invisible base-model reuse and train-eval coupling, with implications for license compliance and contamination tracking across heterogeneous releases.

Large Language Models Are Overconfident in Their Own Responses huggingface.co

Calibration degrades sharply after instruction tuning, and chat templates worsen the effect through an ownership bias toward the model’s own outputs. Reframing those outputs as user input during confidence elicitation restores calibration without retraining, offering a cheap fix for downstream uncertainty estimates.

DeNovoSWE: Scaling Long-Horizon Environments for Generating Entire Repositories from Scratch huggingface.co

DeNovoSWE is a large-scale dataset and sandboxed workflow for teaching code agents to generate entire repositories from documentation alone. Using divide-and-conquer with critic-repair loops and difficulty-aware filtering, a fine-tuned Qwen3-30B-A3B posts strong gains on the new BeyondSWE-Doc2Repo benchmark.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning huggingface.co

EvoTrainer treats the training harness itself as a learnable artifact, evolving policies and infrastructure together using rollout-level diagnostics and backtests. The autonomous loop produces reusable agentic skills and beats handcrafted RL pipelines on complex reasoning and coding tasks.

Redesign Mixture-of-Experts Routers with Manifold Power Iteration huggingface.co

Researchers propose a novel router redesign for Mixture-of-Experts models that aligns router rows with the principal singular directions of expert matrices using Manifold Power Iteration to improve model effectiveness.

ICA Lens: Interpreting Language Models Without Training Another Dictionary huggingface.co

Independent component analysis (ICA) is revived as an efficient method for discovering interpretable directions in language model representations, offering a faster alternative to sparse autoencoder training while maintaining competitive performance in probing tasks.

InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning huggingface.co

InternVideo3 enhances long-horizon multimodal tasks through Multimodal Contextual Reasoning and efficient attention mechanisms, demonstrating strong performance on video understanding benchmarks and video agent capabilities.

World Model Self-Distillation: Training World Models to Solve General Tasks huggingface.co

A scalable framework combines self-distillation and reinforcement learning to transfer task-solving abilities from vision-language models to video diffusion models without requiring labeled task-video data.

Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency huggingface.co

PACI enables efficient asynchronous pipeline training by controlling forward/backward weight inconsistency through local gradient accumulation, achieving higher throughput and faster training time-to-accuracy without sacrificing stability or memory usage.

Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models huggingface.co

Embodied-R1.5 is a unified embodied foundation model that integrates embodied reasoning capabilities and achieves state-of-the-art performance on embodied vision-language benchmarks through a multi-task balanced reinforcement learning approach.

ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics huggingface.co

A new benchmark called ComBench is introduced to evaluate large language models’ combinatorial reasoning abilities through Olympiad-level problems that test both proof construction and explicit mathematical constructions.

Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks huggingface.co

A new benchmark and adapter protocol called Claw-SWE-Bench enables fair comparison of diverse coding agents by standardizing evaluation conditions and revealing the importance of adapter design for effective code generation.

Breaking Entropy Bounds: Accelerating RL Training via MTP with Rejection Sampling huggingface.co

Bebop addresses the efficiency bottleneck in reinforcement learning training of large language models by optimizing multi-token prediction techniques through entropy-aware sampling and novel training objectives that improve acceptance rates and inference throughput.

Beyond Scalar Rewards by Internalizing Reasoning into Score Distributions huggingface.co

A teacher-student framework decouples complex reasoning from efficient reward deployment in text-to-image training, achieving superior preference accuracy and optimization performance.

TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning huggingface.co

TRACE is a rollout allocation framework that improves reward contrast in multi-turn agentic reinforcement learning by dynamically distributing resources across tree-structured rollouts based on prefix-level informativeness.

ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction huggingface.co

ReVision improves computer-use agent efficiency by removing redundant visual patches from consecutive screenshots while preserving spatial structure, reducing token usage by 46% and improving success rates.

Reroute, Don’t Remove: Recoverable Visual Token Routing for Vision-Language Models huggingface.co

Vision-language models can improve grounding performance under aggressive token reduction by replacing irreversible visual-token pruning with recoverable routing that allows tokens to re-enter the processing pipeline at later stages.

Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training huggingface.co

ART enables parameter-efficient fine-tuning of frozen multimodal language models by optimizing raw visual input through gradient backpropagation, achieving performance comparable to LoRA while supporting pre-compiled computational graphs.

SparDA: Sparse Decoupled Attention for Efficient Long-Context LLM Inference huggingface.co

SparDA is a decoupled sparse attention architecture that improves long-context LLM inference by reducing KV cache bottlenecks and attention complexity through aForecast projection for lookahead selection.

Verifiable Environments Are LEGO Bricks: Recursive Composition for Reasoning Generalization huggingface.co

Recursive automated composition framework enables scalable reinforcement learning for language models by automatically combining verifiable environments through compositional operators.

World Pilot: Steering Vision-Language-Action Models with World-Action Priors huggingface.co

World Pilot enhances Vision-Language-Action models by incorporating dynamic scene evolution and trajectory priors from a World-Action Model, achieving superior performance in zero-shot out-of-distribution manipulation tasks.

RepWAM: World Action Modeling with Representation Visual-Action Tokenizers huggingface.co

RepWAM introduces a representation-centric world action model that uses semantic visual-action tokenization to improve robot manipulation performance through language-guided future state prediction and action modeling.

Reason, Then Re-reason: Cross-view Revisiting Improves Spatial Reasoning huggingface.co

A training-free framework for spatial reasoning from egocentric videos that enables revisiting conclusions through synthesized novel-view videos generated from predicted 3D geometry.

DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models huggingface.co

DRIFT is a framework that adapts pretrained vision-language models for continuous decoding tasks by combining coarse prediction with iterative refinement through flow matching, improving performance across perception and planning tasks.

Building Social World Models with Large Language Models huggingface.co

Social World Model framework captures evolution of social beliefs in response to events through temporal pattern mining and evidence lower bound optimization without explicit human annotations.

DyCo-RL: Dynamic Cross-Modal Coordination for Visual Reasoning huggingface.co

Dynamic cross-modal coordination is integrated into reinforcement learning with verifiable rewards to improve visual reasoning in multimodal large language models by measuring attention shifts and aligning token roles during chain-of-thought reasoning.

APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations huggingface.co

Network-native transformer model APEX demonstrates superior forecasting performance for wireless network telemetry compared to existing foundation models and traditional methods.

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application huggingface.co

Large language model agents require specialized environments for training and evaluation, which can be categorized by their engineering lifecycle stages and evolved through various paradigms including neural and symbolic approaches.

Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models huggingface.co

SKIM is an adaptive multi-resolution soft token compression framework that efficiently compresses procedural skills while maintaining task performance and enabling lightweight offline compression for frequently updated community skills.

Can Generalist Agents Automate Data Curation? huggingface.co

Automated data curation using generalist coding agents shows promise but requires structured scaffolding to achieve superior performance compared to traditional methods.

Towards Diverse Scientific Hypothesis Search with Large Language Models huggingface.co

Evolutionary framework for hypothesis generation that improves diversity and quality through multi-temperature sampling and information exchange across search levels.

τ-Rec: A Verifiable Benchmark for Agentic Recommender Systems huggingface.co

A benchmark for agentic recommender systems is introduced that uses verifiable rewards and controlled dialogue constraints to evaluate conversational agent reliability, revealing significant performance gaps among leading models.

i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models huggingface.co

A comprehensive experimental study of text-to-image diffusion models reveals key design choices and training insights leading to the development of i1, a 3B-parameter model that matches leading performance while maintaining full openness.

Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation huggingface.co

A lightweight approach combining a frozen pretrained time-series foundation model with a simple regression head achieves superior RUL prediction performance compared to various baseline methods on industrial sensor data.

Distilling LLM Feedback for Lean Theorem Proving huggingface.co

Feedback Distillation improves post-training of reasoning models by using self-distillation with token-level supervision and privileged feedback from language models, offering better diversity and complementary benefits when combined with GRPO.

TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders huggingface.co

TRL-Bench establishes a standardized benchmark for evaluating tabular representation learning models across multiple granularities, revealing that encoder performance varies by task type and requires capability-specific assessment rather than single leaderboard rankings.

FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching huggingface.co

FlowLet is a conditional generative framework that synthesizes age-conditioned 3D MRIs using flow matching in an invertible 3D wavelet domain, improving brain age prediction performance for underrepresented age groups.

Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay huggingface.co

Continual Instruction Tuning enables effective fine-tuning of large language models for low-resource language translation, achieving superior performance compared to standard instruction tuning and multilingual models.

References

Digital Health Wire (BIDMC feasibility pilot) digitalhealthwire.com

PCPs still outperformed AMIE on the practicality and cost-effectiveness of management plans; 100 patients interacted with the AI up to five days before primary care appointments, with AMIE’s differential including the correct diagnosis in 90% of cases and zero safety interventions required.

Pharmaphorum (Topol/Pope methodology critique) pharmaphorum.com

The text-only constraint effectively handicapped human physicians by stripping away non-verbal communication, tone of voice, and physical examination—elements that are fundamental to human diagnostic accuracy.

Google Research blog on g-AMIE research.google

g-AMIE maintained 90% compliance with safety guardrails versus ~72% for human clinicians, and caught 64.2% of ‘red flag’ symptoms compared to only 40.0% by human PCPs; senior doctors reviewed g-AMIE outputs 40% faster than conducting traditional consultations.

Ahmed Zayed, MD (physician blog) zayedmd.com

Rubrics used to measure empathy may merely be capturing ‘exhaustive politeness’ rather than a genuine clinical connection—LLMs can provide scripted, perfectly worded empathetic responses without the fatigue or time constraints facing a real PCP, effectively gaming evaluation axes.

KevinMD on MIT bias study kevinmd.com

Models were 7–9% more likely to recommend home self-management over clinical care when patient messages contained ‘messy’ features—typos, dramatic phrasing, or non-standard dialects—effectively punishing patients with lower digital literacy or those using African American English.

Harrison PLLC Substack (legal/oversight analysis) harrisonpllc.substack.com

AMIE remains proprietary and closed-source, unlike competing systems like MIRA, which limits independent verification of its safety claims; physicians using AI assistants are sometimes perceived by peers as having lesser clinical skill—a ‘competence penalty.’

MarkTechPost (LifeSciBench coverage) marktechpost.com

GPT-Rosalind reaches 36.1% pass rate vs GPT-5.5 at 25.7% and Gemini 3.1 Pro at 23.6%… no model passed more than 40% of the tasks, particularly struggling with wet-lab troubleshooting and multi-step experimental optimization.

OpenAI/Molecule.one TEMPO paper (PDF) cdn.openai.com

Average yield rose from 16.6% to 25.2%; reactions achieving >30% yield increased from 15.6% to 37.5%, across 10,080 reactions run on Molecule.one’s Maria Lab automated HTE platform.

Reddit r/AIGuild discussion of AI chemist reddit.com

The reported yield improvements have not yet been reproduced by independent laboratories… the ‘near-autonomous’ label downplays the critical role of human experts who still prepare materials, operate parts of the physical lab, and validate results.

Digital Health Insights on Novo Nordisk deal dhinsights.org

Novo Nordisk expanded its 2024 partnership into a sweeping agreement to integrate OpenAI technology across its entire value chain, including manufacturing and supply chain management, with full integration targeted for the end of the year.

DrugPatentWatch on GPT-Rosalind drugpatentwatch.com

Gated deployment through the Trusted Access Program limits Rosalind to vetted U.S. enterprise organizations… shares of established AI-drug discovery firms like Recursion Pharmaceuticals and Schrodinger fell as OpenAI moved into their territory.

Digg tech roundup on LifeSciBench access digg.com

Specific external hosting plans—including direct GitHub or Hugging Face repository links—remained ‘not yet confirmed’ at the time of the initial announcement… critics have raised questions about whether the highly specialized nature of the Ph.D.-level tasks might inadvertently lead to model ‘overfitting’ on the specific rubrics.

AIDE.ml (Weco) aide.ml

AIDE frames ML engineering as a code optimization problem… its ‘solution tree’ represents distinct code versions as nodes, with edges representing refinement steps like debugging or feature engineering

I-MCTS GitHub (Introspective MCTS for AutoML) github.com

I-MCTS demonstrated a 6% performance gain over standard agents by using introspective node expansion

Harrison AIX blog — Arbor write-up harrisonaix.com

default configuration uses 20 coordinator cycles with a maximum tree depth of two… 48-hour wall-clock limit to ensure consistency with other autonomous agents like AIDE

Medium — ‘The Recursive Self-Improvement Loop Tightens But Does Not Close’ (Adnan Masood) medium.com

coding agents still produce fabricated or invalidated experimental results in roughly 80% of open-ended research cases

AIModels.fyi — Arbor paper summary aimodels.fyi

a ‘one-pass plateau’ — most agents manage a single meaningful structural change but fail to build on it in subsequent iterations… agents can identify a leverage point but cannot generalize why it worked

OpenAI MLE-bench GitHub github.com

evaluates agents across 75 Kaggle competitions… AIDE paired with o1-preview achieved a Bronze medal or higher in 16.9% of tasks

Sources

References

Jack Sun, writing.