用可执行有限状态机控制 LLM Agent，长流程指令遵循显著提升
Keeping LLM Agents on Procedure: Executable Finite-State Control for Long-Horizon Instruction Fidelity
做 Agent 工作流的人该看看这篇：把 SOP 状态放到运行时的 FSM 里管，Haiku 4.5 就能比不带这套控制的 Sonnet 4.6 更稳，还省六成成本。实验有预注册和置信区间，方法可直接借鉴到自己的编排框架里。
一篇 arXiv 论文提出把工作流状态放在模型上下文之外的有限状态机（FSM）中，在工具调用、状态转换、回合结束三个点强制约束 Agent 行为，解决长流程运行中的步骤漂移问题。在预注册的 200 账户 SOP-Bench 批次实验中，Claude Haiku 4.5 加 FSM 平均正确且按规程决策 139 个账户，最强基线仅 48 个（差异 95% CI 为 [46, 130]），且能保持到流程结束。重述指令或恢复中断的 Agent 只会把漂移转移到别的捷径上，说明问题在上下文管理而非提示词。
由于长程可靠性由运行时而非模型提供，小模型也能承担程序化工作：带 FSM 的 Haiku 4.5 平均决策数超过不带 FSM 的 Claude Sonnet 4.6，成本只有其 38%。论文在 SWE-bench Verified、τ²-bench 及分阶段、跨上下文工作流上做了进一步分析，三个模型上的增益与其无 FSM 时的漂移程度正相关。
结论指向一条与堆模型能力不同的路线：长程自动化可以靠更强的执行控制实现，这对以 SOP 驱动的企业流程自动化有直接参考价值。
Keeping LLM Agents on Procedure: Executable Finite-State Control for Long-Horizon Instruction Fidelity
Much of the automation handed to language-model agents is procedural: each step needs little intelligence, but a long unattended run must follow a skill or SOP stated once at the start. We show that agents drift from such procedures over long runs, and that restating the instructions or resuming a stopped agent only moves the drift to another shortcut. We study an executable finite-state machine (FSM) that keeps the workflow state outside the model's context and enforces it at three points: tool calls, state transitions and the end of the agent's turn. In a pre-registered experiment on 200-account SOP-Bench batches, Claude Haiku 4.5 with the FSM decided on average 139 accounts correctly and by the procedure, against 48 for the strongest baseline (95% CI of the difference [46, 130]), and kept this rate to the end; across three models, the gain tracked how much the plain agent drifted. Because the runtime, not the model, supplies this long-horizon reliability, the FSM can let small models carry procedural work: Haiku 4.5 with it averaged more such decisions than plain Claude Sonnet 4.6 at 38% of the cost. Analyses on SWE-bench Verified, $τ^2$-bench, and staged and cross-context workflows further suggest that reliable long-horizon automation can come from stronger execution control, not only stronger models.
本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Claude、Haiku、SOP-Bench、SWE-bench，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.AI」（@Yutao Sun, Haokun Li, Mingshuai Chen, Guanyan Li, Zhongyi Wang, Shengchao Qin, Jianwei Yin），经 AITOP 多维评分管线结构化提炼。
