Sources

Using AI to help physicians diagnose rare genetic diseases affecting children openai.com

Researchers used an OpenAI reasoning model to help diagnose rare diseases, identifying 18 new diagnoses in previously unsolved cases.

Project Fetch: Phase two anthropic.com

MosaicLeaks: Can your research agent keep a secret? huggingface.co

MiniMax Sparse Attention huggingface.co

MiniMax Sparse Attention pairs blockwise sparse patterns with Grouped Query Attention and Top-k selection to keep tensor cores busy during both prefill and decoding. The design cuts attention compute at long context lengths while matching dense-attention quality, and the team has released the MSA implementation on GitHub.

MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling huggingface.co

MaxProof scales test-time compute for mathematical proof generation by combining a generative verifier, critique-conditioned proof repair, and population-level tournament selection. Built on the MiniMax-M3 series, the framework reaches competitive scores on high-level math olympiad problems where single-shot proof attempts typically collapse.

WEAVER, Better, Faster, Longer: An Effective World Model for Robotic Manipulation huggingface.co

WEAVER is a multi-view latent world model for robotic manipulation that swaps standard objectives for a flow-matching loss to keep long-horizon dynamics consistent. The architecture supports policy evaluation, policy improvement, and test-time planning, outperforming prior world models across each of these uses.

The Cold-Start Safety Gap in LLM Agents huggingface.co

Tool-calling LLM agents show a measurable cold-start safety gap: refusal and guardrail behavior strengthens only after a few prior task completions. Hidden-state analysis traces the shift to representation drift, and the authors propose Safety Over Depth as a deployment-time mitigation.

Getting Better at Working With You: Compiling User Corrections into Runtime Enforcement for Coding Agents huggingface.co

TRACE mines corrections that users issue to coding agents and compiles them into runtime checks enforced on later actions. The skill-layer pipeline sharply cuts preference violations on both in-distribution and out-of-distribution tasks without hurting overall task pass rates.

LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories huggingface.co

LabVLA targets scientific laboratory workflows with a two-stage recipe: FAST action-token pretraining on Qwen3-VL-4B-Instruct, then a flow-matching DiT action expert with knowledge insulation. Trained on simulator-generated trajectories, it tops prior VLAs on the LabUtopia benchmark of automated lab tasks.

WebChallenger: A Reliable and Efficient Generalist Web Agent huggingface.co

WebChallenger is a generalist web agent that represents pages through a structured PageMem abstraction over the DOM and runs a divide-and-conquer observation pipeline with compound action workflows. The framework reaches strong autonomous navigation scores using open-weight base models rather than frontier APIs.

ToolSense: A Diagnostic Framework for Auditing Parametric Tool Knowledge in LLMs huggingface.co

Parametric tool retrieval models show reduced performance and understanding when evaluated with realistic ambiguous queries compared to standard benchmarks, revealing a dissociation between knowledge retrieval and true tool comprehension.

WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces huggingface.co

WeaveBench presents a comprehensive benchmark for evaluating computer-use agents across multiple interfaces, revealing significant challenges in long-horizon task orchestration and highlighting the limitations of traditional performance assessment methods.

High-Fidelity Two-Step Image Generation via Teacher-Aligned End-to-End Distillation huggingface.co

A 2-step image generation model is developed through distillation from an 8-step teacher using distribution-aligned adversarial learning, step-decoupled parameterization, and end-to-end training with iterative regularization.

EvoBrowseComp: Benchmarking Search Agents on Evolving Knowledge huggingface.co

EvoBrowseComp is an evolving benchmark with 800 contamination-free questions synthesized through a three-agent framework that ensures temporal freshness and prevents parametric memorization in search agent evaluation.

Flash-GMM: A Memory-Efficient Kernel for Scalable Soft Clustering huggingface.co

Flash-GMM introduces an efficient fused Triton kernel for Gaussian Mixture Models that achieves significant speedup and enables processing much larger datasets on a single GPU.

Demystifying Hidden-State Recurrence: Switchable Latent Reasoning with On-Policy Reinforcement Learning huggingface.co

A switchable latent reasoning framework uses explicit boundary tokens to enable trainable and interpretable latent reasoning through recurrent hidden states.

EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery huggingface.co

Environment engineering enhances autonomous scientific discovery by designing structured agent environments that optimize behaviors like exploration and collaboration while mitigating issues such as reward hacking and human oversight friction, as demonstrated by the EurekAgent system that achieves state-of-the-art results across multiple domains with low computational costs.

Risk Under Pressure: Compute-Aware Evaluation of Adversarial Robustness in Language Models huggingface.co

Compute-aware evaluation framework using FLOPs and risk-compute curves reveals non-monotonic effects of alignment training and varying attack costs across different harm categories.

Revisiting Articulated Parts Perception in Robot Manipulation huggingface.co

A new geometric representation called Geometric Primary Structure (GPS) is introduced for articulated parts perception, enabling efficient data collection through VR annotation and achieving high manipulation success rates without fine-tuning.

VIA-SD: Verification via Intra-Model Routing for Speculative Decoding huggingface.co

VIA-SD introduces a multi-tier speculative decoding framework that uses intra-model routing to reduce verification costs by employing slim submodels for medium-confidence token validation, achieving significant speedups over traditional approaches.

EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments huggingface.co

EvoArena benchmark and EvoMem memory paradigm address the challenge of dynamic environments in LLM agents by modeling progressive updates and structured memory evolution, showing improved performance on evolving tasks.

Evoflux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents huggingface.co

Evoflux enables compact language models to execute tool workflows more reliably by using evolutionary search to repair failed plans during inference, significantly improving execution feasibility compared to traditional fine-tuning methods.

SpatialClaw: Rethinking Action Interface for Agentic Spatial Reasoning huggingface.co

SpatialClaw is a training-free framework that uses code as an action interface to enable flexible, stateful spatial reasoning in vision-language models, achieving superior performance across diverse 3D/4D spatial reasoning tasks.

FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents huggingface.co

A framework for creating shortcut-resistant training data for deep search agents by identifying and mitigating four shortcut risks in data synthesis processes.

TreeSeeker: Tree-Structured Trial, Error, and Return in Deep Search huggingface.co

TreeSeeker is an inference-time framework that uses tree-structured search with branch-and-return control to manage exploration and exploitation in deep search tasks, improving performance through systematic trial-and-error decision making.

HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness huggingface.co

Learnable harness controller called HarnessBridge is introduced to parameterize agent-environment interfaces through bidirectional projections, achieving performance comparable to specialized harnesses with reduced computational overhead.

See What I See, Know What I Think: Dense Latent Communication Across Heterogeneous Agents huggingface.co

Heterogeneous multi-agent systems can effectively transfer knowledge through aligned KV-cache communication, achieving better performance than text-based methods with reduced computational costs.

N-GRPO: Embedding-Level Neighbor Mixing for Enhanced Policy Optimization huggingface.co

N-GRPO, a novel exploration strategy within GRPO framework, enhances mathematical reasoning in large language models through semantic neighbor mixing that maintains semantic consistency while injecting diversity.

InterleaveThinker: Reinforcing Agentic Interleaved Generation huggingface.co

InterleaveThinker enables interleaved generation capabilities for image generators through a multi-agent pipeline with planner and critic agents, achieving performance comparable to state-of-the-art models while enhancing reasoning benchmarks.

Visual Para-Thinker++: A Single-Policy Multi-Agent Framework for Visual Reasoning huggingface.co

A multi-agent framework with shared MLLM policy and role-specific training methods improves visual reasoning by reducing hallucinations and enabling efficient parallel processing.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers huggingface.co

HYDRA-X presents a unified multimodal model that integrates image and video tokenization within a single Vision Transformer, addressing spatiotemporal reconstruction and semantic awareness through causal temporal attention and hierarchical compression.

Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding? huggingface.co

Robust-U1 enhances multimodal large language models’ robustness against visual corruptions through self-recovery capabilities that improve both visual quality and reasoning performance.

VideoMDM: Towards 3D Human Motion Generation From 2D Supervision huggingface.co

VideoMDM trains 3D human motion priors from 2D poses using a diffusion framework with 2D reprojection loss and 3D motion regularizers, achieving near-3D supervised performance without requiring 3D ground truth.

SG-OPD: Sign-Gated On-Policy Distillation via Sign-Consistency Gating and Phased Teacher Sampling huggingface.co

Sign-Gated On-Policy Distillation improves upon standard on-policy distillation by incorporating a binary verifier to filter teacher signals, resulting in better performance on mathematical reasoning tasks.

MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training huggingface.co

Token-subset representation alignment method called MaskAlign improves diffusion transformer training by reducing reliance on complete token sets and maintaining stable alignment behavior under perturbations.

Rethinking Psychometric Evaluation of LLMs: When and Why Self-Reports Predict Behavior huggingface.co

Psychometric assessments of LLM behavior reveal that specific behavioral frameworks like Theory of Planned Behavior show better coherence with actual responses than broad personality traits, particularly within shared conversations.

Surflo: Consistent 3D Surface Flow Model with Global State huggingface.co

Surflo compresses unposed RGB views into latent tokens and decodes 3D surface points through flow matching, enabling flexible resolution output and efficient processing compared to existing methods.

IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder huggingface.co

Representation autoencoders using deep learning frameworks can improve image reconstruction quality by combining shallow and deep visual feature representations for better semantic richness and visual fidelity.

From 2D Grids to 1D Tokens: Reforming Shared Representations for Multimodal Image Fusion huggingface.co

A multimodal image fusion approach uses a 1D token interface from a pretrained image tokenizer to enhance global appearance coherence while preserving local details through selective token editing.

Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback huggingface.co

Structured Defect Grounding (SDG) addresses limitations in text-to-image model diagnosis by modeling defects as structured sets and using vision-language models for detection and reward-based alignment.

MoVerse: Real-Time Video World Modeling with Panoramic Gaussian Scaffold huggingface.co

MoVerse generates real-time interactive video from single images by creating 360° panoramas and 3D Gaussian scaffolds, enabling efficient rendering through diffusion-based techniques.

MuJoCo-Drones-Gym: A GPU-Accelerated Multi-Drone Simulator for Control and Reinforcement Learning huggingface.co

A Gymnasium-compatible multi-drone simulation environment built on MuJoCo physics engine that supports flexible physics models, action interfaces, and observation spaces for reinforcement learning applications.

ArogyaSutra: A Multi-Agent Framework for Multimodal Medical Reasoning in Indic Languages huggingface.co

ArogyaBodha dataset and ArogyaSutra framework enhance multilingual medical reasoning in low-resource settings through diverse data integration and actor-critic multi-agent reasoning.

A Stationary (and Therefore Compatible) Representation is All You Need huggingface.co

Stationary representations learned through d-Simplex fixed classifiers ensure model compatibility during sequential fine-tuning and updates, enabling continuous retrieval services without reprocessing.

PianoKontext: Expressive Performance Rendering from Deadpan Context huggingface.co

PianoKontext generates variable-length piano performances by aligning MIDI scores with audio in latent space using DTW and DiT blocks.

Leveraging Morphology for Historical Script Metrological Analysis huggingface.co

A transformer-based architecture with prototype learning enables scalable paleographic measurements from historical documents using only line-level transcriptions, demonstrating its effectiveness on a 160-page codex with minimal training data requirements.

On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance huggingface.co

Large language models exhibit limited ability to correct zero-shot errors through prompting, with model performance more strongly linked to definition-specific familiarity than text-level memorization metrics.

References

Glen Rhodes blog (independent recap) glenrhodes.com

humans aided by an earlier model (Opus 4.1) took 264 minutes, the unassisted Opus 4.7 finished in 12 minutes and 7 seconds, utilizing only 1,045 lines of code compared to the human team’s 10,309

KuCoin News flash kucoin.com

Claude 4.7 Completes Robotic Tasks 37 Times Faster Than Human Teams

Benzinga benzinga.com

Anthropic Fetch: Claude Operates Robots Faster Than Humans

The Guardian theguardian.com

Anthropic disable advanced AI models [following] US government order

PR Newswire / industry report on robot simulation training prnewswire.com

the field has only 0.05% of the data needed for true generalization… causing models to ‘hallucinate’ physical possibilities, such as requesting motor torques that exceed hardware limits

Medium (Joe Njenga) medium.com

Meet Anthropic’s AI-trained robot dog — is this the start of a new AI race?

medRxiv benchmark study (5,000+ cases) medrxiv.org

Exomiser achieved a ‘top-1’ accuracy of approximately 35.5%, whereas the best-performing LLMs (including GPT-4 variants) reached only 23.6%

iHeart/AP coverage of Kyra Benton case kool108.iheart.com

After a 19-year diagnostic odyssey, the o3 system flagged a frameshift variant in the HSPB8 gene, confirmed as a rare form of myofibrillar myopathy — a diagnosis received one week before her 28th birthday

R&D World — OpenAI Rosalind Biodefense launch rdworldonline.com

GPT-Rosalind is accessible only through a ‘trusted access program’ requiring institutional safety reviews; early government partners include Lawrence Livermore National Laboratory and Johns Hopkins APL

LifeSciBench coverage (letsdatascience.com) letsdatascience.com

In the 2026 LifeSciBench — 750 expert-authored tasks — GPT-Rosalind passed only 36.1% of challenges, though it outperformed all other frontier models

Google Research — AMIE real-world clinical study research.google

AMIE was safe and matched doctors on differential diagnosis quality, [but] human physicians still surpassed the AI in management plan practicality and cost-effectiveness

Hoodline — Boston Children’s AI infrastructure report hoodline.com

BCH reclaimed approximately 60,000 hours of labor and redeployed over $7 million in costs through 50+ AI automations, moving from narrow pilots into a ‘common, secure AI foundation’

Dark Reading — Copilot SearchLeak attack darkreading.com

attackers could use server-side fetches of image URLs within the search interface to exfiltrate MFA codes, password reset links, and private organizational files

Beam.ai — AI Agent Security Breaches 2026 beam.ai

an attacker utilized Claude Code and GPT-4.1 to exfiltrate 195 million taxpayer records from Mexican government agencies… automated the execution of over 5,000 remote commands, accelerating the breach by an estimated 10x

arXiv — Whisper Leak / streaming side-channel paper arxiv.org

analyzes packet size and timing patterns in encrypted streaming responses to infer the topic of a conversation with over 90% precision, bypassing TLS/HTTPS protections

arXiv — PrivacyChecker (contextual-integrity mitigation) arxiv.org

PrivacyChecker uses a ‘privacy agent’ to intercept and reformulate queries locally, removing out-of-context information before it reaches external APIs… reduce leakage significantly (e.g., from 36% to 7% on models like DeepSeek-R1)

hyper.ai independent write-up of MosaicLeaks hyper.ai

producing high-quality, verifiable data points for the benchmark still requires substantial human intervention, limiting the current dataset scale… initial dataset is restricted to a few specific company contexts

r/machinelearningnews discussion on enterprise agent benchmarks reddit.com

high benchmark scores often fail to translate to ‘boots on the ground’ utility, noting that models like Qwen3 Coder frequently enter ‘death spirals’ of bad tool calls in real-world scenarios

Sources

References

Jack Sun, writing.