A new open-source project is pushing the boundaries of what AI agents can do for academic research. AutoResearchClaw, released March 15 by the AIMING Lab, automates the entire research pipeline - from a single sentence describing your idea to a compile-ready LaTeX paper targeting NeurIPS, ICML, or ICLR. The repo has already hit 5,700 stars and 565 forks in just three days.

23 Stages, Zero Babysitting

The pipeline breaks research into 23 discrete stages across eight phases: scoping, literature discovery, knowledge synthesis, experiment design, experiment execution, analysis, paper writing, and finalization. Literature is sourced from real APIs - OpenAlex, Semantic Scholar, and arXiv - with a 4-layer citation verification system that auto-removes hallucinated references via arXiv ID checks, CrossRef/DataCite DOI lookup, title matching, and LLM relevance scoring.

Experiments run in a Docker sandbox with hardware auto-detection (NVIDIA CUDA, Apple MPS, or CPU). When experiments fail, the system self-heals by diagnosing errors and regenerating code. When hypotheses don't hold, Stage 15 autonomously decides to refine parameters or pivot to a new direction entirely.

Multi-Agent Quality Control

Three multi-agent subsystems - CodeAgent, BenchmarkAgent, and FigureAgent - handle specialized tasks. A 4-round paper quality audit includes AI-slop detection and a 7-dimension review scoring system. Three human-in-the-loop gates can pause for approval, or be skipped with --auto-approve for fully autonomous runs.

Self-Learning Across Runs

The latest v0.3.0 release integrates MetaClaw, a cross-run learning system. Pipeline failures are converted into structured lessons and reusable skills, injected into all 23 stages on subsequent runs. In controlled experiments, this improved overall robustness by 18.3% and cut retry rates by nearly 25%.

The project is compatible with OpenClaw, Claude Code, Codex CLI, and other ACP-compatible agents - meaning you can literally type "Research X" in a chat and get a paper back.