Trace2Tower: Transition-Aware EigenTrace Induction of Multi-Level Skills for LLM Agents

TL;DR - Trace2Tower distills agent execution traces into a multi-level hierarchy of action templates, routines, and task strategies using transition-aware graph modeling and contrastive spectral decomposition. It improves task success and experience reuse while reducing inefficient or invalid actions.

• Converts step-level interactions into canonical events linked by semantic compatibility, transition dynamics, and outcome evidence.
• Extracts stable, success-aligned behavioral modes while suppressing failure-prone shortcuts.
• Dynamically refines learned skills through verifier-guided feedback.
• Achieves 87.31% success on ALFWorld with 10.35 steps and 0.26 invalid actions, plus 50.67% exact success on WebShop.