EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines
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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Shuo Zhang
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2026-01-15T09:33:43.605Z
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zhangshuo
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Chaofa Yuan
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2026-04-01T08:37:06.111Z
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bbruceyuan
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Ryan Guo
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Xiaomin Yu
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2026-01-15T15:02:52.674Z
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Rui Xu
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Zhangquan Chen
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Zinuo Li
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Zhi Yang
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2026-01-15T09:33:45.582Z
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Zhi Yang
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Shuhao Guan
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Zhenheng Tang
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Sen Hu
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Liwen Zhang
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2026-01-16T15:28:47.062Z
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Liwen Zhang
SUFE-AIFLM-Lab
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SUFE-AIFLM-Lab
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Ronghao Chen
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Huacan Wang
EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines
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.
EvoFSM is a structured self-evolving framework for LLM agents that uses finite state machines to improve adaptability while maintaining control through constrained optimization and memory mechanisms.
LLM-based agents self-evolution finite state machine workflow adaptation constrained optimization critic mechanism self-evolving memory multi-hop QA DeepSearch benchmark interactive decision-making