𝜋Skill: High-Density Knowledge Extraction from Single Trajectories via Circular Step-Level Analysis

Multimodal agents can reuse past interactions through external memory, yet trajectory-derived memories are often both coarse and unreliable: a rollout-level summary can miss locally useful decisions, while unverified experiences may remain in the prompt after they become irrelevant or repeatedly harmful. We introduce πSKILL, a training-free framework that separates experience extraction, maintenance, and use. First, Circular Step-level Knowledge Distillation (CSD) combines cross-rollout circular analysis of useful operations and observed failure points to identify operational lessons at different granularities. A deterministic admission rule limits overly specific, redundant, and non-actionable candidates. Second, Confidence-aware Memory Updating (CMU) associates each admitted experience with outcome statistics and a lifecycle state, allowing unreliable entries to be suppressed as feedback accumulates. At inference time, the agent decom- poses the task into method-oriented retrieval queries and rewrites a small set of selected experiences into task-specific guidance. Using Qwen2.5-VL-7B-Instruct, πSKILL improves the four-benchmark macro-average Average@4 from 12.67 to 15.32 and Pass@4 from 25.28 to 33.33 relative to state of the art. The gains are largest on benchmarks that require repeated procedural decisions, while the ablations show that inference-time contextualization is essential and that the two memory-construction components have task-dependent effects.