Trace2Tower：LLM智能体技能层次框架
Trace2Tower: Transition-Aware EigenTrace Induction of Multi-Level Skills for LLM Agents
研究人员推出Trace2Tower框架，能将LLM智能体行为转化为技能层次，在两个基准测试中大幅超越现有方法。
Trace2Tower是一种新的EigenTrace框架，将原始轨迹转化为稳健的技能层次结构。该框架在ALFWorld基准上达到87.31%成功率，仅需10.35步和0.26次无效操作。在WebShop基准上，它实现了50.67%的精确成功率。对比现有基线，Trace2Tower在任务掌握和上下文高效经验重用方面表现显著更优。
Trace2Tower: Transition-Aware EigenTrace Induction of Multi-Level Skills for LLM Agents
Large language model agents increasingly rely on execution traces to master complex interactive tasks. However, current paradigms are bottlenecked by shallow trajectory retrieval and flat skill summarization, fundamentally ignoring the temporal dependencies and outcome-conditioned topology of agent behavior. We introduce Trace2Tower, a transition-aware EigenTrace framework that distills raw trajectories into a robust skill hierarchy. Trace2Tower abstracts step-level interactions into canonical events, constructing a unified graph governed by semantic compatibility, transition dynamics, and outcome evidence. Through a novel contrastive spectral decomposition, it isolates stable, success-aligned behavioral modes while rigorously suppressing failure-prone shortcuts. These modes organically populate a dynamic skill tower of action templates, procedural routines, and overarching task strategies, continuously refined via verifier-guided feedback. On ALFWorld, Trace2Tower achieves 87.31% success requiring only 10.35 steps and 0.26 invalid actions; on WebShop, it reaches 50.67% exact success. Across both benchmarks, Trace2Tower significantly outperforms existing baselines in task mastery and context-efficient experience reuse.
