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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/2601.09465 |
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| _version_ | 1866914305368129536 |
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| author | Zhang, Shuo Yuan, Chaofa Guo, Ryan Yu, Xiaomin Xu, Rui Chen, Zhangquan Li, Zinuo Yang, Zhi Guan, Shuhao Tang, Zhenheng Hu, Sen Zhang, Liwen Chen, Ronghao Wang, Huacan |
| author_facet | Zhang, Shuo Yuan, Chaofa Guo, Ryan Yu, Xiaomin Xu, Rui Chen, Zhangquan Li, Zinuo Yang, Zhi Guan, Shuhao Tang, Zhenheng Hu, Sen Zhang, Liwen Chen, Ronghao Wang, Huacan |
| contents | While LLM-based agents have shown promise for deep research, most existing approaches rely on fixed workflows that struggle to adapt to real-world, open-ended queries. Recent work therefore explores self-evolution by allowing agents to rewrite their own code or prompts to improve problem-solving ability, but unconstrained optimization often triggers instability, hallucinations, and instruction drift. We propose EvoFSM, a structured self-evolving framework that achieves both adaptability and control by evolving an explicit Finite State Machine (FSM) instead of relying on free-form rewriting. EvoFSM decouples the optimization space into macroscopic Flow (state-transition logic) and microscopic Skill (state-specific behaviors), enabling targeted improvements under clear behavioral boundaries. Guided by a critic mechanism, EvoFSM refines the FSM through a small set of constrained operations, and further incorporates a self-evolving memory that distills successful trajectories as reusable priors and failure patterns as constraints for future queries. Extensive evaluations on five multi-hop QA benchmarks demonstrate the effectiveness of EvoFSM. In particular, EvoFSM reaches 58.0% accuracy on the DeepSearch benchmark. Additional results on interactive decision-making tasks further validate its generalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09465 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines Zhang, Shuo Yuan, Chaofa Guo, Ryan Yu, Xiaomin Xu, Rui Chen, Zhangquan Li, Zinuo Yang, Zhi Guan, Shuhao Tang, Zhenheng Hu, Sen Zhang, Liwen Chen, Ronghao Wang, Huacan Artificial Intelligence While LLM-based agents have shown promise for deep research, most existing approaches rely on fixed workflows that struggle to adapt to real-world, open-ended queries. Recent work therefore explores self-evolution by allowing agents to rewrite their own code or prompts to improve problem-solving ability, but unconstrained optimization often triggers instability, hallucinations, and instruction drift. We propose EvoFSM, a structured self-evolving framework that achieves both adaptability and control by evolving an explicit Finite State Machine (FSM) instead of relying on free-form rewriting. EvoFSM decouples the optimization space into macroscopic Flow (state-transition logic) and microscopic Skill (state-specific behaviors), enabling targeted improvements under clear behavioral boundaries. Guided by a critic mechanism, EvoFSM refines the FSM through a small set of constrained operations, and further incorporates a self-evolving memory that distills successful trajectories as reusable priors and failure patterns as constraints for future queries. Extensive evaluations on five multi-hop QA benchmarks demonstrate the effectiveness of EvoFSM. In particular, EvoFSM reaches 58.0% accuracy on the DeepSearch benchmark. Additional results on interactive decision-making tasks further validate its generalization. |
| title | EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.09465 |