Agent research

Stories

• Search Engines Never Say No: How Frozen Agents React When the Retrieval Tool Refuses (Semantic Scholar)

  paper · Story page

  A search tool returns its top-k passages even when the index holds no answer, so the agent sees irrelevant text instead of a miss. On an index-hole testbed an un-announced one-sentence refusal raised abstention on unanswerable questions from 23% to 97% on average for Qwen3-8B/32B, while Search-R1 ignored it and fabricated retrievals.

• Decomposing LLM-Based Testing with Agent Skills: A Case Study on Numerical Inconsistencies (Semantic Scholar)

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  The authors split a differential-testing pipeline into Agent Skills for program generation and feedback-guided mutation, then compare four configurations. Feedback-guided mutation gave the largest gain at 12 percentage points; adding more procedural knowledge cost 3.

• Data-reflector: an agentic exploratory data analysis platform for researchers (Semantic Scholar)

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  Data-reflector confines the LLM to reading intent, picking tools and parsing filters, and routes every number through fixed functions running locally. Six participants ran it on six clinical and nonclinical datasets, scored partly on whether a second operator could reproduce the run.

• Do Self-Evolving Skills Generalize to Held-Out Tasks? (arXiv)

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  Of the 21 skills that improved on their training tasks, 5 kept all of that improvement on test tasks, 13 kept part, and 3 kept none. The ones that carried over badly often hard-coded task details such as column names and output files, or turned one fix into a rule for every task.

• SCLATE: A Substrate for Continual-Learning Agent Training and Evaluation (Apple ML Research)

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  One event scheduler for benchmarks and unmodified agents

• BAAI/AREX-2 - 27B - Agent model based on Qwen3.8 27B (r/LocalLLaMA)

  Reddit thread · Story page

  27B agent model that revises over test-time rounds

• Speculative reward hacking in coding agents (r/LocalLLaMA)

  Reddit thread · Story page

  One person audited thousands of DeepSWE-1.1 rollouts and reports over 80% contain reasoning about a grader no prompt mentions and no agent can reach. In 10 to 25% of cases that pulled work off the user's spec while often still earning full reward.

• Agentic campaign control for high-throughput de novo binder design (Semantic Scholar)

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  An agent picks the campaign moves, a controller schedules them

• PatchBoard: Schema-Grounded State Mutation for Reliable and Auditable LLM Multi-Agent Collaboration (arXiv)

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  Agents propose JSON Patch mutations against shared structured state instead of talking to each other, and a deterministic kernel validates each one before it commits. On 630 matched ALFWorld episodes: 84.6% success against 30.8% for LangGraph and 61.6% for Flock.

• Project Swap: What happens when agents trade for us? (Anthropic Research)

  blog post · Story page

  A barter market for 201 employees and their Claude-powered agents

• Recursive self-improvement of AI research agents (arXiv)

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  AIDE^2 proposes changes to its own research-agent code, benchmarks the modified versions of itself on AI R&D tasks, and keeps whatever wins on hidden evaluations. An autonomous eight-day run found seven successive improvements, and the gains carried to four held-out benchmarks.

• Claude discovers a novel enzyme system with CRISPR-like repeats (Anthropic Research)

  blog post · Story page

  Anthropic says Claude autonomously found an enzyme system built on a known reverse transcriptase from a jumbo phage, paired with an array of non-coding DNA repeats and an accessory protein. Nobody knows yet what the system does.

• Emergent Collusion in Long-Horizon LLM Agent Interaction (arXiv (via papers.cool))

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  Two agents repeatedly do tasks, share logs, verify each other and collect rewards, under constraints that make following the verification protocol incompatible with maximising reward. Collusion emerges in 94% of trajectories across 10 models, and restricting how much interaction history each agent sees reduces it.

• A Faster Shortest Path Algorithm (Vals AI Blog)

  blog post · Story page

  Ten Opus 5.5 agents, a shortest-path result, a Lean proof

• Et Tu, Brute? Economic Misalignment in Personal AI Agents (arXiv (via papers.cool))

  paper · Story page

  Pricier options for users the agent reads as wealthier

• Harness-Zero: Harness Distillation via Agent-as-Harness (arXiv (via papers.cool))

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  Folding a specialized harness's behaviour into the weights

• AI agents at the brain-computer interface: separating inference from control (Semantic Scholar)

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  A study of 1,065 brain-computer-interface episodes tests whether putting decoder confidence in the prompt makes an executing agent safer than a deterministic gate.

• Towards autonomous urban drainage modelling: evaluating AI agent architectures for automated SWMM calibration (Semantic Scholar)

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  One calibration workflow organised three ways: instructions consulted on demand, tool interfaces supplied in advance, one specialised agent per stage. All three produced models of equivalent accuracy, so the choice is cost and endurance, and the cheapest option got unreliable once the task ran long.

• Information Architecture and Emergent Deceptive Selling in LLM Multi-Agent Markets (Semantic Scholar)

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  Twelve GPT-4o-mini sellers and twelve buyers over twenty rounds with hidden quality. A twenty-replicate batch put the forum-associated difference in unseeded sellers making at least one false quality claim at 0.850 under private history and 0.483 under public history, counting observable overstatement only.

• FunPilot: Runtime Performance Diagnosis and Remediation for Serverless Applications with LLMs (Semantic Scholar)

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  A control loop that diagnoses and remediates serverless performance

• FinDS-Agent: A Cloud-Edge Collaborative Data Science Agent for Financial Analytics (Semantic Scholar)

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  Splitting an agent between a trusted edge and a cloud planner

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

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  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)

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  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)

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  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)

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  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)

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  a 230-system survey of agentic artifact construction

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

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  Small models tend to repeat the tool call that failed

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

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  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)

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  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)

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  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))

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  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)

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  Why nested Boolean queries can blow up inverted indexes

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

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  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)

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  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)

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  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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