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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.
SciForge is an open-source, AI-native multimodal workbench for scientific discovery that integrates search, reasoning, workflow execution, and evidence governance, demonstrated through eight end-to-end use cases including gene discovery and protein design.
A Twitter thread highlights emerging autoresearch labs that are building AI systems to automate the full research loop, from hypothesis to experimentation.
This paper proposes a method for autonomous research agents using hypothesis-tree refinement to generate and test hypotheses, aiming toward generalist scientific discovery.
AutoSci is a memory-centric agentic system designed to automate the full scientific research lifecycle, from literature understanding to rebuttal, using LLM-based agents with persistent memory and self-evolution capabilities.
The open-source AI agent Feynman, through the collaboration of four intelligent agents, compresses PhD-level research processes (including arXiv research, literature review, code verification) into fully automated execution, requiring only a single instruction from the user.
A system built on Claude Code allows it to control Google's NotebookLM from the terminal, automating research by searching YouTube, uploading sources, and exporting cited answers directly into Obsidian. This workflow eliminates the need for multiple browser tabs and manual copying, with verified citation accuracy.
Hugging Face open-sourced ml-intern, an autonomous agent that performs the entire ML post-training loop—reading papers, finding datasets, writing scripts, generating data, monitoring training, and uploading weights—achieving significant GPQA improvement with a 1.7B model in 10 hours without human intervention.
A survey paper examining the transition of AI from task-specific assistants to workflow-level research automators, defining AutoResearch as the spectrum of AI-powered scientific workflow automation and analyzing challenges in autonomy, reproducibility, and accountability.
ARIS is an open-source tool that has gone viral on GitHub (8.8k stars). It uses a lightweight Markdown skill pack to enable Claude Code or other LLM agents to autonomously complete the entire machine learning research lifecycle, including literature review, experiment execution, and paper writing.
NanoResearch is a multi-agent framework designed to personalize research automation by co-evolving skills, memory, and policy to adapt to individual user preferences and research styles.