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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.20025 |
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| _version_ | 1866917527678877696 |
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| author | Liu, Jiaqi Qiu, Shi Li, Mairui Li, Bingzhou Ji, Haonian Han, Siwei Ye, Xinyu Xia, Peng Dong, Zihan Chen, Meng Zhang, Congyu Zhang, Letian Chen, Guiming Tu, Haoqin Yang, Xinyu Feng, Lu Zhao, Xujiang Chen, Haifeng Zhou, Jiawei Wang, Xiao Zhang, Weitong Zhu, Hongtu Li, Yun Mei, Jieru Fei, Hongliang Zhang, Jiaheng Li, Linjie Zhang, Linjun Zhou, Yuyin Wang, Sheng Xiong, Caiming Zou, James Zheng, Zeyu Xie, Cihang Ding, Mingyu Yao, Huaxiu |
| author_facet | Liu, Jiaqi Qiu, Shi Li, Mairui Li, Bingzhou Ji, Haonian Han, Siwei Ye, Xinyu Xia, Peng Dong, Zihan Chen, Meng Zhang, Congyu Zhang, Letian Chen, Guiming Tu, Haoqin Yang, Xinyu Feng, Lu Zhao, Xujiang Chen, Haifeng Zhou, Jiawei Wang, Xiao Zhang, Weitong Zhu, Hongtu Li, Yun Mei, Jieru Fei, Hongliang Zhang, Jiaheng Li, Linjie Zhang, Linjun Zhou, Yuyin Wang, Sheng Xiong, Caiming Zou, James Zheng, Zeyu Xie, Cihang Ding, Mingyu Yao, Huaxiu |
| contents | 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 \textsc{Pivot}/\textsc{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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_20025 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration Liu, Jiaqi Qiu, Shi Li, Mairui Li, Bingzhou Ji, Haonian Han, Siwei Ye, Xinyu Xia, Peng Dong, Zihan Chen, Meng Zhang, Congyu Zhang, Letian Chen, Guiming Tu, Haoqin Yang, Xinyu Feng, Lu Zhao, Xujiang Chen, Haifeng Zhou, Jiawei Wang, Xiao Zhang, Weitong Zhu, Hongtu Li, Yun Mei, Jieru Fei, Hongliang Zhang, Jiaheng Li, Linjie Zhang, Linjun Zhou, Yuyin Wang, Sheng Xiong, Caiming Zou, James Zheng, Zeyu Xie, Cihang Ding, Mingyu Yao, Huaxiu Artificial Intelligence 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 \textsc{Pivot}/\textsc{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. |
| title | AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.20025 |