Agent research

Stories

• Agent-driven Model Development for RNA 3D Structure Prediction (Semantic Scholar)

  paper · Story page

  Under a fixed budget and human supervision, an LLM agent proposed, implemented, trained and evaluated changes to an RNA structure model 297 times, taking it from a random baseline to an 8.9M-parameter trunk the authors say matches RhoFold+ and NuFold within noise at a fraction of their inference cost. Read it for the loop design.

• OpenAI reports Navier-Stokes singularity find, a contender for second ever Millenium Prize awarded, overshadowing Cognition's $48B Series E, Mistral's $24B Series D, Meta's Muse agent, and GPT Image 2.5 (AINews (smol.ai))

  newsletter item · Story page

  AINews on OpenAI's proposed 10,000-agent Navier-Stokes proof and its disputes

• Why are AI agents lying, cheating and coordinating? (Yoshua Bengio)

  HN thread · 348 points on HN · Story page

  Yoshua Bengio on why agents lie, cheat and coordinate

• OpenAI says a group of ~10,000 agents on an unreleased model beyond GPT-6 Astra produced a Navier-Stokes Millennium Prize solution in 88 hours (@OpenAI)

  X post · Story page

  OpenAI says around 10,000 coordinating agents on a next-generation model it calls significantly more capable than GPT-6 Astra produced an analytical proof and a Lean formalization that a Navier-Stokes fluid can develop a finite-time singularity, in 88 hours. It's OpenAI's own claim; the thread does not say the proof has been independently reviewed.

• Evolving transition-state search with agentic large language models (Semantic Scholar)

  paper · Story page

  An evolutionary LLM loop rewrites transition-state search for unsolved reactions

• What Does Multi-Harness RL Learn? Credit Assignment and Portability in Coding Agents (arXiv)

  arXiv, 25 pp · Story page

  The authors replay frozen task-harness records from Aider, OpenHands, Qwen Code, and SWE-agent from one Qwen3-8B warm start and score each checkpoint on a sealed SWE-bench Verified oracle. Across 24,000 evaluations the evaluation harness moved mean solve rate from 2.14% to 9.27%, a factor of 4.3, while pooling rewards across harnesses moved the held-out result by 0.25 points with an interval spanning zero.

• At Equal Inference Cost, Multi-Agent Structure Does Not Beat a Single Frozen Agent (arXiv)

  paper · Story page

  With the total number of model calls fixed, an evolved planner-executor-critic team scored 0.769 on ALFWorld against 0.754 for an evolved single executor, p = 0.80, while using 1.8 times more evaluation calls. Leave-one-in analysis traces the realized gain entirely to the executor.

• Reviewer Capability Governs Rejection Targeting, Not Repair Skill: Evidence from LLM Execute-Review-Revise Pipelines (arXiv)

  paper · Story page

  Across 100 olympiad math problems, a cross-family mid-tier reviewer lifted final accuracy from 52 to 64 percent with zero damaged answers. Same-model self-review caught more errors, 0.85 recall, yet produced no significant gain, rejecting 2.1 times as often and falsely rejecting 35 percent of its own correct answers.

• Meta's AIRA₃ autonomous research system wins Kaggle gold fine-tuning a 30B Nemotron model (@AIatMeta)

  X post · Story page

  Meta says AIRA₃ placed 8th of about 4,000 teams in a live NVIDIA-run Kaggle competition to fine-tune a 30B Nemotron model, a gold medal graded on a private test set. The gold entry was an ensemble of GPT 5.5 with OpenCode and Claude 4.8 with Claude Code, run as many long-running agents coordinating through a shared forum and filesystem.

• Agentic Artifact Creation: Systems, Evaluation, Principles, and Opportunities (HF Daily Papers)

  paper · Story page

  a 230-system survey of agentic artifact construction

• Feedback That Backfires: Why Small Language Model Agents Repeat the Call They Just Watched Fail (arXiv)

  paper · Story page

  Small models tend to repeat the tool call that failed

• The Interaction Tax: When Communication Erases Diversity in Multi-Agent Teams (arXiv)

  paper · Story page

  Under matched budgets on 11 verifier-scored optimization tasks, agents that read each other's complete outputs converged within one round and lost the diversity that justified running several models. Independent proposals were the stronger default; critique helped only when the violated rule was straightforward to find and fix.

• SkillGate: Training In-Policy Skill Selection in Long-Horizon Agents (arXiv (via papers.cool))

  paper · Story page

  Agents now choose which instruction skill to read mid-episode, but outcome-rewarded RL punishes a correct pick whenever later execution fails, a failure the authors name selector credit starvation. SkillGate routes outcome credit to execution tokens and a separate local advantage to the skill-naming tokens.

• Revisiting the "Push-T" Robot Manipulation Task with Agentic Robotics (Semantic Scholar)

  paper · Story page

  Prompted with no demonstration data, Claude Code with Fable 5 wrote a policy for the Push-T manipulation benchmark. The paper reports 100% success with 46% fewer steps than the best diffusion policy trained on 200 human demonstrations, plus extensions to Push-A through Push-Z and 3D arm simulations.

• A Multi-Agent LLM Pipeline for Legal Argument Mining (Semantic Scholar)

  paper · Story page

  how upstream errors cascade through a multi-agent legal pipeline

• Debate Training Reduces Reward Hacking in RLAIF (Alignment Forum)

  community thread · Story page

  GDM's Amplified Oversight team reports that RL against an LLM judge gets the judge hacked, since convincing it is often easier than solving the task. Adding a debate opponent during training reduces that reward hacking.

• On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification (arXiv (via papers.cool))

  paper · Story page

  A re-evaluation of two memory-based self-improving agents finds the loop can amplify evaluation noise, and gains depend heavily on default task orderings acting as a hidden curriculum. Shuffle the tasks and the improvement story changes.

• The P-Completeness of Inverted Index Traversal: On the Complexity of Evaluating Boolean Query DAGs (Apple ML Research)

  Story page

  Why nested Boolean queries can blow up inverted indexes

• One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL (Semantic Scholar)

  paper · Story page

  Train a policy against one LLM user simulator and it overfits that simulator's dominant mode, then transfers poorly to other simulators and real users. Verbalized Sampling lifts held-out success by up to 9%; co-training against a population of simulators reaches 14%.

• Learning more about Claude's mathematical capabilities (Anthropic Research)

  blog post · Story page

  An unreleased research version of Claude raised a longstanding Riemann zeta lower bound from 41.6% to 67.2%, with a formally verifiable proof.

• Conformity Mitigations in Large Language Models Lie on a Single Resistance-Receptivity Frontier (arXiv)

  paper · Story page

  Conformity mitigations trade resistance for receptivity

• Scaling up Continual Learning (Ronak Malde (Trajectory))

  talk · Story page

  The but wait problem in on-policy self-distillation

• An AI Scientist that Doesn't Drift: Taste, Structure, and Falsifiable Findings in a Quadruped Navigation Research Loop (arXiv)

  paper · Story page

  A research loop built against drift: immutable experiment cards so a falsified hypothesis can't be retconned, subagents locked to mechanical roles, and a preference oracle that alone makes subjective calls. The oracle changed research direction, not the best score.

• Ryan Greenblatt: Human level AIs might build runaway superintelligences by 2032 (Dwarkesh Patel)

  blog post · Story page

  Dwarkesh Patel and Ryan Greenblatt debate recursive self-improvement

• Why do our AI models stop learning the second we deploy them? (Gradient Flow)

  newsletter item · Story page

  Ben Lorica on why deployed models largely stop learning

• ADIAS: Automated Design of Interactive Agentic Systems (arXiv)

  paper · Story page

  Issue-centric optimization for automated agent design

• Advanced AI sycophancy (Sean Goedecke)

  blog post · Story page

  Goedecke questions whether frontier models have gotten less sycophantic or merely better at flattering smart, neurotic information workers: the effective move is disagreeing with you without making you feel stupid. Worth holding in mind anywhere you let a model grade your work.

• Meta research: agents learn harness policies instead of hand-authored harnesses (@omarsar0)

  X post · Story page

  New Meta research has agents learn harness policies offline, then deploy them to construct and update external harness state online, instead of relying on hand-authored harnesses that are hard to tune for long-horizon tasks.

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