Search: “scientific agents”

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

ScienceBuddy released: interactive scientific agent workspace coupling harness evolution with model reinforcement learning for continual self-improvement across four scientific task families.

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

Hugging Face daily papers·updated Sep 15, 17:55 UTC · 1d ago·first Sep 14, 20:00 UTC · 2d agoAI research 2 sources

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

HypoEvolve uses a generational genetic algorithm coordinating specialized LLM agents to generate scientific hypotheses, outperforming six baselines on cancer drug repurposing.

HypoEvolve is a framework that coordinates specialized LLM agents through a generational genetic algorithm to produce, revise, and retain scientific hypotheses with explicit collaboration roles. It evaluates drug repurposing hypotheses against external evidence from DepMap and Open Targets across 34 cancer types. It achieves the highest scores against six baselines, reaching DepMap selectivity of 0.171 versus 0.115 for the strongest baseline, and gains over single-pass generation generalize to held-out cancer types.

Hugging Face daily papers·updated Sep 14, 17:44 UTC · 2d ago·first Sep 13, 20:00 UTC · 3d agoAI research 2 sources

ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments

ScienceIDE converts scientific code repositories into verifiable agent training environments, producing the PhAI-IDE 4B-72B model family.

ScienceIDE turns scientific code repositories into executable environments supporting task generation, execution, and scientific verification, guided by expert-defined scientific cases and acceptance criteria. Using verified interaction trajectories, the authors train PhAI-IDE-72B, PhAI-IDE-9B, and PhAI-IDE-4B. The model family shows gains in held-out scientific-code repair and selected general-purpose code, reasoning, and knowledge benchmarks, evidencing positive transfer from scientific experience.

Hugging Face daily papers·updated Sep 16, 17:55 UTC · 19h ago·first Sep 15, 20:00 UTC · 1d agoAI research 2 sources

Evaluating Verified Autonomy in Quantum Engineering

Quantum-Harbor lab and QIQCBench (49 tasks) expose wide performance gaps across 17 frontier agentic systems in verified quantum engineering.

Researchers built Quantum-Harbor, a virtual laboratory providing a controlled execution environment where scientific AI agents interacting with quantum systems can have both actions and conclusions directly verified. QIQCBench contributes 49 expert-authored tasks spanning calibration and control, error correction and compilation, and sensing and networking. Across 17 frontier agentic systems, verified performance varied widely, exposing a substantial gap between demonstrated capability and reliable autonomous operation.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 16:46 UTC · 1d agoAI research1

[AINews] OpenAI reports Navier-Stokes singularity find in 88 hours using Astra-next, roughly 10,000 agents and 130B tokens (>$40M), a contender for second ever Millennium Prize awarded

OpenAI-linked accounts claim roughly 10,000 AI agents produced a Navier-Stokes singularity result in 88 hours, pending mathematical verification.

OpenAI-affiliated accounts claim a system of roughly 10,000 agents, trained over about a year with multi-agent reinforcement learning, produced a finite-time singularity result related to the Navier-Stokes Millennium Problem. The claimed 88-hour runtime and 130B-token cost circulate only via social posts, and no preprint, theorem statement, or proof artifact is available. Acceptance by the mathematics community is unresolved, so the claim's epistemic status remains unknown. The roundup also notes Cognition's $48B and Mistral's $24B fundraises, GPT Image 2.5, and Meta's Muse agent relaunch.

Latent Space·Sep 9, 05:04 UTC · 8d agoAI research1

AlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genome

Google DeepMind released AlphaGenome Atlas, a free 1-petabyte platform predicting the molecular effects of all ~9 billion possible single-letter DNA variants.

Google DeepMind introduced AlphaGenome Atlas, containing precomputed predictions for the effects of roughly 9 billion single-nucleotide variants across the human genome, spanning hundreds of human and mouse cell types. The 1-petabyte dataset is more than 30 times larger than the AlphaFold Database and includes an AlphaGenome Variant Impact (AVI) score combining AlphaGenome and AlphaMissense predictions for both coding and non-coding regions. External collaborators have already used it to identify and experimentally verify variants in unsolved rare disease research. It is available via a free web portal, the AlphaGenome API, and as a skill in Google Antigravity.

Google DeepMind·Sep 8, 14:00 UTC · 8d agoAI research 2 sources

T1: Terminal Agent Reinforcement Learning for Long-Horizon Tasks

T1, a 122B MoE terminal agent trained with reinforcement learning, reaches 64.0% on Terminal-Bench 2.1, surpassing GPT-5.4 and GLM-5.1 on long-horizon tasks.

T1 is a 122B mixture-of-experts model trained with reinforcement learning to operate a real shell in a cloud sandbox for up to 300+ tool-call turns per task, rewarded by executing each task's own verifier. The recipe combines aggressive warm starts, dense process rewards, TITO construction, and rollout routing replay, cutting the training-to-inference log-probability difference from 0.021 to 0.013 with zero token drift. Training used an out-of-distribution corpus disjoint from Terminal-Bench 2.1. Post-training raised the base model from 43.8% to 64.0% resolved on Terminal-Bench 2.1 and 27.9% on Long-Horizon Terminal Bench.

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d 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·Sep 8, 17:59 UTC · 8d agoAI research

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark with 660 instances measures whether LLMs can spot and fix underspecified research-method details for faithful implementation.

Researchers introduce IdeaAMBIG, a benchmark of 660 evidence-grounded instances (163 real-world gaps from reproducibility reports and GitHub issues, 497 controlled synthetic gaps) built from papers, codebases, and reproduction artifacts. It evaluates codification-readiness assessment, defect localization, and clarification action generation. Across 13 LLMs, the best model achieved only 9.6% Macro Defect Recovery Rate on real-world instances but 80.6% clarification success when given the annotated defect. An oracle study showed gold resolutions raise the codification-ready rate from 14% to 98%, identifying defect localization as the main bottleneck.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:59 UTC · 7d agoAI research2

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark of 660 specification-gap instances shows LLMs localize implementation-critical research gaps poorly, with best model at 9.6% defect recovery.

IdeaAMBIG is a benchmark of 660 evidence-grounded instances evaluating whether research-method specifications provide enough information for faithful implementation: 163 real-world gaps from reproducibility reports and GitHub issues plus 497 controlled synthetic gaps. It tests codification-readiness assessment, defect localization, and clarification action generation across 13 LLMs. The best model achieves only a 9.6% Macro Defect Recovery Rate on real-world instances, though 80.6% clarification success when given the annotated defect, and an oracle study shows gold resolutions raise codification-ready rates from 14% to 98%. Defect localization emerges as the main bottleneck across all evaluated models.

Hugging Face daily papers·Sep 8, 20:00 UTC · 8d agoAI research

Import AI 469: Science AI; RSI simulator; and Zuck's technological pessimism

New DiG-bench benchmark of 70 hidden-rule games shows only Opus 5 and Fable 5 solving the hardest tiers, probing AI discovery and creativity.

Import AI 469 highlights DiG-bench (Discovery in Games), a benchmark of 70 handcrafted games with hidden rules and objectives where only 21 games are public and most are kept private to avoid training contamination. Only Opus 5 and Fable 5 with Claude Code solved any Tier 7 tasks (about 0.2 success), with GPT-5.5 next; the games are text-based and have beaten every human tester at least once. The newsletter also covers an RSI simulator game by Paradigm Research and Inherent's Faraday, a post-trained open-weight model that supervises frontier models to improve scientific research output.

Import AI·Aug 17, 13:05 UTC · Aug 17, 2026AI research

OpenAI Frontier organizes AI agents under one system

Help Net Security·Feb 5, 00:00 UTC · Feb 5, 2026AI research

I wrote an AI textbook — how long until AI can do it better?

AI researcher Nathan Lambert argues LLMs remain weak at long-form technical writing, questioning whether models can autonomously organize scientific knowledge for breakthroughs.

Nathan Lambert describes writing a post-training textbook, Reinforcement Learning from Human Feedback, and finds today's LLMs weak at organizing long-form technical content despite becoming superhuman at coding and math. He notes GPT 5.5 Pro found deep typos across a 200-300 page manuscript while Claude models proved more useful as editors. He argues that compressing knowledge through writing is a prerequisite for autonomous scientific insight and tempers expectations for near-term AI-driven open science.

Interconnects·Aug 12, 13:01 UTC · Aug 12, 2026AI research

Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye

METR analysis finds AI accelerating cyber vulnerability discovery, while SPADE self-play environment generation improves Qwen3 reasoning benchmark scores at 30B scale.

Import AI 470 discusses a METR research note reporting differential acceleration from AI: major acceleration in reported cyber vulnerabilities (cURL, OpenSSL, Firefox, Microsoft, NVD, OSV), minor acceleration in mathematics, and no measurable acceleration in AI-research optimization benchmarks. It also covers SPADE, a self-play framework from a multi-university team (University of Washington, Stanford, MIT, CMU, and others) that co-evolves executable training environments and agent capability using Environment Designer and Reasoning Agent roles with hint-based regret rewards. Trained on Qwen3-4B-Instruct-2507, Qwen3-8B, and Qwen3-30B-A3B-Instruct-2507 via GRPO (400 rollouts of 25 environments), SPADE lifted the 30B-A3B game-environment suite average to 58.3, +8.1 over base, and improved tool-use results across backbones. The issue also references Hawkeye for building better GPU kernels.

Import AI·Aug 24, 13:12 UTC · 24d agoAI research1

The AI ‘Ghosts’ Contaminating Academic Publishing

Samsung and University of Warsaw researchers find LLMs repeatedly generate the same fake author names, contaminating academic records with 1,655 ghost-authored DOIs.

A preprint from Samsung and the University of Warsaw, "The Ghost Couple: Correlated LLM Name Priors and Their Haunting of the Web and Academic Publishing," shows that LLMs such as Claude, ChatGPT, and Gemini repeatedly generate the same fictional names like Elena Vasquez, Marcus Chen, and Aris Thorne as experts and co-authors. Researchers identified 1,655 ghost-authored records on CERN-operated Zenodo carrying real DataCite DOIs, fabricated journals, and backdated publication dates. Ghost names also form synthetic research groups on ResearchGate and are indexed without verification by Google Scholar and Semantic Scholar. The researchers suggest correlated name priors could serve as provenance signals for detecting AI-generated content.

404 Media·Aug 27, 18:14 UTC · 20d agoAI research