EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines

• URL: http://arxiv.org/abs/2601.09465v1
• Date: Wed, 14 Jan 2026 13:19:13 GMT
• Title: EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines
• Authors: Shuo Zhang, Chaofa Yuan, Ryan Guo, Xiaomin Yu, Rui Xu, Zhangquan Chen, Zinuo Li, Zhi Yang, Shuhao Guan, Zhenheng Tang, Sen Hu, Liwen Zhang, Ronghao Chen, Huacan Wang,
• Abstract summary: EvoFSM is a structured self-evolving framework that achieves both adaptability and control by evolving an explicit Finite State Machine.<n>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.<n>In particular, EvoFSM reaches 58.0% accuracy on the DeepSearch benchmark.
• Score: 23.086761228480682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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