One Tree, Three Roles: Goal-Structured Extraction, Admission, and Reuse of Procedural Memory

Large language model agents can improve by reusing procedures from past executions. However, long-horizon trajectories interleave multiple goals, failed attempts, and later corrections, making it difficult to identify coherent procedures and determine which execution evidence supports them. Reusable procedures also vary in scope, from local tool-use steps to multi-step workflows. We introduce GPM (Goal-structured Procedural Memory), a training-free framework for procedural memory that reconstructs the goal hierarchy reflected in a completed task trajectory. Higher-level goals are accomplished through lower-level subgoals, which may themselves be further decomposed. We reconstruct these relations from the finished ReAct trajectory to form a Goal Tree, while linking each goal to the actions and observations for it. The same tree is used across the memory pipeline: a goal or subtree defines the scope of a procedure, its linked execution records provide the evidence used to decide whether that procedure should be stored, and procedures can later be retrieved at different levels of granularity. Because the tree is reconstructed only after execution, the agent needs no hierarchical planner while acting. Experiments on AppWorld show that GPM improves Task Goal Completion by 3.8 and 7.4 percentage points across two executor backbones. A controlled admission study further shows that goal-scoped verification reduces invalid admission by 7.6 percentage points while largely preserving valid memories.