ai-scientist

Sakana AI has released AI Scientist-v2, an end-to-end automated research system that can autonomously go from generating research hypotheses to writing papers, and has been accepted by the ICLR2025 Workshop after peer review.

This paper introduces Xcientist, a research harness that externalizes AI-driven scientific research synthesis and validation into inspectable, contract-governed processes to ensure accountability and traceability.

Researchers at MIT present a paper on self-evolving AI scientists that can discover and adapt their own scientific vocabulary, using a categorical framework to mathematically quantify genuine novelty and separate discovery from mere search or retrieval.

Harvard University's AutoScientists proposes a decentralized multi-agent team approach, allowing multiple agents to share experimental status, automatically form teams, and review research plans, significantly outperforming existing methods on multiple benchmarks.

A new paper from Meta, Stanford, and Google introduces AutoResearchClaw, which improves automated research by integrating failure recovery, debate, and selective human input. It outperforms AI Scientist v2 by 54.7% on ARC-Bench and reveals that autonomy is enhanced when constrained by process rather than given unlimited freedom.

A study evaluates frontier models' ability to forecast scientific progress across 4,760 events, finding they can identify plausible directions but cannot reliably predict outcomes or timelines, with systematic overconfidence.

A comprehensive open-source collection of 138 scientific agent skills that transform AI coding assistants like Claude Code and Codex into AI scientists, covering biology, chemistry, medicine, and more, with integration of over 100 scientific databases and specialized Python packages.

This paper presents AI CFD Scientist, an open-source AI agent for computational fluid dynamics that autonomously discovers physics corrections using vision-language verification and code modification, outperforming general AI scientists on CFD tasks.

EvoScientist is an adaptive multi-agent framework for end-to-end scientific discovery that continuously improves through persistent memory modules, comprising three specialized agents for idea generation, experiment execution, and knowledge distillation. It outperforms 7 state-of-the-art systems in scientific idea generation and improves code execution success rates through multi-agent evolution.