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论文提出 TSFLab，一个自我改进、与具体智能体解耦的时间序列 forecasting 研究工作台，为编码智能体提供共享资源与工具，以低成本完成复现、基准测试与新方法开发，并显著提升任务完成率、降低 token 与开发时间成本。
An inside look at how frontier labs like OpenAI and Anthropic are reorganizing around AI agents that execute most research work while humans direct and verify, with evidence that recursive self-improvement is already well underway inside these AGI-oriented organizations.
Researcher Jiahao Shao publicly criticizes Anthropic for flagging and restricting his account despite claiming he never distilled Claude, alleging that using Claude for research automation like RFT/SFT and job submission triggers punitive downgrades and session kills.
VerifiedLab introduces a multi-agent harness framework where AI scientists with distinct roles (PI, librarian, specialists, gate, critic) reason over field-verified records to turn scientific knowledge from passive artifacts into active systems, demonstrated on perovskite solar cells by recovering known trends and anticipating devices reported a year later.
An essay examines how unreleased frontier AI systems from OpenAI and Anthropic have formalized Fermat's Last Theorem, addressed the Navier-Stokes problem, and targeted Clay Millennium Prize problems, provoking anxiety and soul-searching among mathematicians about the future of human research.
Agent4Sci 通过跨 11 个领域的 20 个科学 ML 基准评估前沿编码智能体，发现它们主要靠工程技巧而非领域洞察提升性能，而轻量插件技能可显著改善表现（Claude Opus 从 10.4% 提升至 20.1%）。
哈佛物理教授 Matthew Schwartz 借助开源工具 BootLoops 与 Claude，在三个月内跨 18 个学科产出 36 篇研究手稿，其中多篇计算此前从未被完成。该工具由 Anthropic 资助、以 MIT 协议开源，可与 Claude、Gemini 或 ChatGPT 等多种模型配合使用。
A commentary essay drawing an analogy between AI's rapid, disruptive scientific discoveries—such as OpenAI tackling Millennium Prize problems in mathematics—and the 'roadside picnic' metaphor from the Strugatskys' novel, where incomprehensible artifacts are dumped on fields faster than they can be understood or integrated.
Google Cloud AI Research introduces AIM (Agentic Idea Manager), an autonomous framework for idea-driven automated research that uses an Agentic Surrogate and Agentic Acquisition (inspired by Bayesian optimization) to organize and select research ideas, plus a Solution Auditor and Resource Planner to maintain idea-solution alignment and allocate experimental budgets. AIM outperforms the strongest AutoLab baseline by up to 4.9 points and reaches baseline performance up to 3.1x faster.
Hao Zhang leaves Meta's Superintelligence Lab to join Discovery Loop, a new AI company founded by Jeff Dean aimed at automating machine learning research with a high valuation.
This paper introduces AIDE^2, a system that enables AI research agents to autonomously improve their own code through recursive self-improvement, leading to performance gains across various AI research tasks.
Paper2Agent, a multi-agent framework published in Nature, automatically transforms research papers into virtual authors, enabling AI agents to interact with and build upon scientific knowledge for enhanced discovery.
Andy Matuschak shares an experiment with programmable highlighters that automate citation finding, fact-checking, and interaction with AI agents.
The article reflects on how AlphaFold transformed protein structure prediction and speculates that similar AI advancements could revolutionize mathematics, shifting human roles towards interpretation and verification while potentially automating key tasks.
OpenAI claims its AI agents now contribute 3.1 workdays for every human researcher workday, achieving an "automated research intern" level, and predicts it will reach an "automated AI researcher" by March 2028. The company says agentic systems have accelerated progress toward recursive self-improvement, effectively scaling its research workforce from roughly 1,000 to an equivalent of over 4,000.
This paper develops an LLM pipeline for automating systematic literature reviews in disease spread modeling, comparing the performance of GPT-4.1 and GPT-5.0 against human-conducted reviews.
The article questions whether a system with persistent memory but fixed model weights, like AQuA, qualifies as recursive self-improvement, referencing a paper that uses a narrower definition.
The paper introduces a framework for personalized auto-research systems that condition every stage of the research process on individual scientist representations, arguing that personalization is essential for AI to serve as true co-scientists rather than generic instruments.
Akshay Pachaar introduces Primus, an autonomous AI researcher from Transformer Lab that automates the full research loop from literature review to paper writing, demonstrating its self-correcting capabilities in experiments.
This paper proposes a benchmarking protocol using automated multi-model LLM review to evaluate AI Scientist systems, comparing frameworks like Sakana AI, CycleResearcher, and Data-to-Paper, and finds that FARS benchmark papers significantly outperform other systems.