给AI画张“做事流程图”，它就不会干着干着忘了要干嘛
大模型当“代理”干活时，最大的毛病不是笨，是干着干着就忘了自己在干嘛——工具调用顺序乱、重复做无用功。这篇把“怎么做”像知识图谱那样拆成(步骤，关系，步骤)的三元组，给AI画一张会自己长出来的流程图：每走一步，系统只把当前节点附近的一小段图翻译成“此刻该往哪走”的提示，不替它做决定。更关键的是，这张图会自我进化：AI拿失败轨迹和成功轨迹对比，自己改图，改完先在验证集上试，有效才留，无效就回滚。从一张最小骨架开始，它能长出比人手设计的还好的图，还能修复专家给的错误初稿。它不是你明天就能用上的东西，但“让AI自己维护一张行动地图”这个思路，可能是解决长任务失控的一条真路。
📄 原文摘要(英文)
Large language models are increasingly deployed as agents that plan over long horizons and act through external tools. Most agents select actions through unconstrained generation over an accumulating history, leaving implicit the procedural knowledge of what to do, in what order, and under which conditions. As trajectories lengthen, agents can lose track of their objectives, invoke tools out of order, and repeat unproductive actions. We introduce the Procedural Graph: just as a knowledge graph organizes factual knowledge into (entity, relation, entity) triplets for what-is questions, a Procedural Graph organizes procedural knowledge into (procedure, relation, procedure) triplets for what-to-do questions. At each decision step, the framework localizes the agent's active node, and a guidance model translates the surrounding subgraph into step-level situational guidance that biases the solver's next action without dictating it. The graph is self-evolving: an LLM refiner contrasts failed trajectories with successful ones and edits the graph's topology and attributes, committing edits that preserve or improve held-out validation performance while retaining rejected ones to discourage repetition. Starting from a minimal skeleton, the loop builds graphs that match or surpass hand-designed ones. It can also repair a flawed expert prior. Across multiple datasets, task types, and LLMs, the Procedural Graph delivers consistent gains over memory-based baselines, and self-evolution further improves performance without manual engineering.