AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration
Authors: name: Jiaqi Liu, name: Shi Qiu, name: Mairui Li, name: Bingzhou Li, name: Haonian Ji, name: Siwei Han, name: Xinyu Ye, name: Peng Xia, name: Zihan Dong, name: Congyu Zhang, name: Letian Zhang, name: Guiming Chen, name: Haoqin Tu, name: Xinyu Yang, name: Lu Feng, name: Xujiang Zhao, name: Haifeng Chen, name: Jiawei Zhou, name: Xiao Wang, name: Weitong Zhang, name: Hongtu Zhu, name: Yun Li, name: Jieru Mei, name: Hongliang Fei, name: Jiaheng Zhang, name: Linjie Li, name: Linjun Zhang, name: Yuyin Zhou, name: Sheng Wang, name: Caiming Xiong, name: James Zou, name: Zeyu Zheng, name: Cihang Xie, name: Mingyu Ding, name: Huaxiu Yao

Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail and inform the next attempt, and lessons accumulate across cycles. Existing autonomous research systems often model this process as a linear pipeline: they rely on single-agent reasoning, stop when execution fails, and do not carry experience across runs. We present AutoResearchClaw, a multi-agent autonomous research pipeline built on five mechanisms: structured multi-agent debate for hypothesis generation and result analysis, a self-healing executor with a Pivot/Refine decision loop that transforms failures into information, verifiable result reporting that prevents fabricated numbers and hallucinated citations, human-in-the-loop collaboration with seven intervention modes spanning full autonomy to step-by-step oversight, and cross-run evolution that converts past mistakes into future safeguards. On ARC-Bench, a 25-topic experiment-stage benchmark, AutoResearchClaw outperforms AI Scientist v2 by 54.7%. A human-in-the-loop ablation across seven intervention modes reveals that precise, targeted collaboration at high-leverage decision points consistently outperforms both full autonomy and exhaustive step-by-step oversight. We position AutoResearchClaw as a research amplifier that augments rather than replaces human scientific judgment. Code is available at https://github.com/aiming-lab/AutoResearchClaw.