Trace2Tower Distills Agent Traces into a Skill Hierarchy, Hitting 87.31% on ALFWorld in 10.35 Steps

arXiv 2609.05261·medium signalXBlueskyLinkedInCopy link

arXiv 2609.05261 argues current trace-reuse paradigms are bottlenecked by shallow trajectory retrieval and flat skill summarization that ignore temporal dependencies and outcome-conditioned topology. Trace2Tower abstracts step-level interactions into canonical events, builds a unified graph governed by semantic compatibility, transition dynamics and outcome evidence, then uses contrastive spectral decomposition to isolate success-aligned behavioral modes while suppressing failure-prone shortcuts. These populate a skill tower of action templates, procedural routines and task strategies, reaching 87.31% success on ALFWorld with 10.35 steps and 0.26 invalid actions, and 50.67% exact success on WebShop.