EvoFSM：基于有限状态机的可控自进化深度研究框架

摘要

尽管基于大语言模型的智能体在深度研究任务中展现出潜力，但现有方法大多依赖固定工作流，难以适应现实世界中开放式的复杂查询。近期研究尝试通过让智能体重写自身代码或提示来实现自我进化以提升问题解决能力，但无约束的优化往往引发系统不稳定、幻觉及指令偏移等问题。我们提出EvoFSM框架，通过演化显式有限状态机而非依赖自由形式的重写，实现适应性与可控性的统一。该框架将优化空间解耦为宏观流程（状态转移逻辑）与微观技能（状态特定行为），在明确行为边界下实现精准改进。依托评审机制引导，EvoFSM通过一组受限操作精化有限状态机，并引入自我进化记忆模块——将成功轨迹提炼为可复用的先验知识，失败模式则转化为未来查询的约束条件。在五个多跳问答基准测试上的广泛实验证明了EvoFSM的有效性，其中在DeepSearch基准上达到58.0%的准确率。交互式决策任务的附加结果进一步验证了其泛化能力。

English

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：基于有限状态机的可控自进化深度研究框架

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

摘要

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