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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/2605.10813 |
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| _version_ | 1866911687439810560 |
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| author | Xu, Jinhang Zhu, Qiyuan Wu, Yujun Wang, Zirui Zhang, Dongxu Tian, Marcia Duan, Yiling Li, Siyuan Wei, Jingxuan Han, Sirui Guo, Yike Zhang, Odin He, Conghui Tan, Cheng |
| author_facet | Xu, Jinhang Zhu, Qiyuan Wu, Yujun Wang, Zirui Zhang, Dongxu Tian, Marcia Duan, Yiling Li, Siyuan Wei, Jingxuan Han, Sirui Guo, Yike Zhang, Odin He, Conghui Tan, Cheng |
| contents | LLM-powered multi-agent systems can now automate the full research pipeline from ideation to paper writing, but a fundamental question remains: automation for whom? Researchers operate under different resource configurations, hold different methodological preferences, and target different output formats. A system that produces uniform outputs regardless of these differences will systematically under-serve every individual user, making personalization a precondition for research automation to be genuinely usable. However, achieving it requires three capabilities that current systems lack: accumulating reusable procedural knowledge across projects, retaining user-specific experience across sessions, and internalizing implicit preferences that resist explicit formalization. We propose NanoResearch, a multi-agent framework that addresses these gaps through tri-level co-evolution. A skill bank distills recurring operations into compact procedural rules reusable across projects. A memory module maintains user- and project-specific experience that grounds planning decisions in each user's research history. A label-free policy learning converts free-form feedback into persistent parameter updates of the planner, reshaping subsequent coordination. These three layers co-evolve: reliable skills produce richer memory, richer memory informs better planning, and preference internalization continuously realigns the loop to each user. Extensive experiments demonstrate that NanoResearch delivers substantial gains over state-of-the-art AI research systems, and progressively refines itself to produce better research at lower cost over successive cycles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10813 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NanoResearch: Co-Evolving Skills, Memory, and Policy for Personalized Research Automation Xu, Jinhang Zhu, Qiyuan Wu, Yujun Wang, Zirui Zhang, Dongxu Tian, Marcia Duan, Yiling Li, Siyuan Wei, Jingxuan Han, Sirui Guo, Yike Zhang, Odin He, Conghui Tan, Cheng Artificial Intelligence LLM-powered multi-agent systems can now automate the full research pipeline from ideation to paper writing, but a fundamental question remains: automation for whom? Researchers operate under different resource configurations, hold different methodological preferences, and target different output formats. A system that produces uniform outputs regardless of these differences will systematically under-serve every individual user, making personalization a precondition for research automation to be genuinely usable. However, achieving it requires three capabilities that current systems lack: accumulating reusable procedural knowledge across projects, retaining user-specific experience across sessions, and internalizing implicit preferences that resist explicit formalization. We propose NanoResearch, a multi-agent framework that addresses these gaps through tri-level co-evolution. A skill bank distills recurring operations into compact procedural rules reusable across projects. A memory module maintains user- and project-specific experience that grounds planning decisions in each user's research history. A label-free policy learning converts free-form feedback into persistent parameter updates of the planner, reshaping subsequent coordination. These three layers co-evolve: reliable skills produce richer memory, richer memory informs better planning, and preference internalization continuously realigns the loop to each user. Extensive experiments demonstrate that NanoResearch delivers substantial gains over state-of-the-art AI research systems, and progressively refines itself to produce better research at lower cost over successive cycles. |
| title | NanoResearch: Co-Evolving Skills, Memory, and Policy for Personalized Research Automation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.10813 |