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