Summary

AutoResearchClaw automates the entire research paper generation lifecycle, from idea to publication-ready LaTeX. It targets researchers and power users by handling literature review, experimentation, analysis, and writing autonomously, offering significant time savings.

How It Works

A 23-stage LLM-driven pipeline decomposes topics, discovers literature (arXiv, Semantic Scholar), synthesizes knowledge, and generates hypotheses via multi-agent debate. It designs, codes (hardware-aware for GPU/MPS/CPU), and executes experiments in a sandbox, featuring self-healing. Results trigger autonomous PIVOT/REFINE decisions. The system drafts papers, conducts multi-agent peer review, and generates conference-ready LaTeX with verified citations. Novelty includes autonomous decision loops, self-learning, and robust citation integrity.

Quick Start & Requirements

Install via pip install -e . and run with researchclaw run --topic "Your research idea here" --auto-approve. Requires Python 3, an LLM API key (OpenAI, OpenRouter), and hardware for experiments (auto-detects GPU/MPS/CPU). Integration and testing guides are available.

Highlighted Details

• Autonomous PIVOT/REFINE Loop: Dynamically redirects research based on intermediate results.
• Multi-Agent Debate: Enhances hypothesis generation, analysis, and peer review.
• Self-Learning: Improves future research cycles using lessons from past runs.
• 4-Layer Citation Verification: Eliminates hallucinated references by checking multiple sources.
• Hardware-Aware Experimentation: Adapts code to available hardware (GPU/MPS/CPU).
• Conference-Grade Output: Generates LaTeX for NeurIPS, ICML, ICLR.

Maintenance & Community

Developed by the "AutoResearchClaw team." The README provides no specific details on active maintenance, community channels, roadmaps, or sponsorships.

Licensing & Compatibility

Released under the permissive MIT License, suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

Appears to be in active development ("looking for testers"). Includes human-approval "gate stages" unless bypassed with --auto-approve. Relies on LLM APIs, incurring costs and potential rate limits. Experiment execution can be resource-intensive; sandbox environments have memory constraints.