SkillCAT: New Framework Enables Language Model Agents to Develop Reusable Skills Without Training
Researchers have proposed SkillCAT, a training-free framework that enables large language model agents to evolve reusable skills from execution trajectories more reliably than existing methods. The system addresses three key weaknesses in current skill self-evolution pipelines: single-trajectory learning, premature skill merging, and inefficient full-corpus loading at inference. Evaluated across multiple benchmarks, SkillCAT achieved average score improvements of up to 40.40% over baselines, suggesting meaningful gains in agent capability without requiring additional model training.
SkillCAT is a newly proposed training-free framework designed to improve how LLM-based agents learn and reuse skills from task execution histories. Current skill self-evolution pipelines typically suffer from three limitations: they learn from only one trajectory per task, merge candidate skill patches before validating them, and load an entire skill corpus at inference time regardless of task relevance. SkillCAT addresses these issues through three distinct stages: Contrastive Causal Extraction (CCE), which samples multiple trajectories and compares success/failure pairs to identify outcome-determining factors; Assessment-Augmented Evolution (AAE), which validates each candidate skill patch by replaying it on source-task clones before hierarchical merging; and Topology-Aware Task Execution (TTE), which organizes evolved skills into a routable sub-skill topology so only relevant capability nodes are loaded at inference. The framework was evaluated on SpreadsheetBench, WikiTableQuestions, and DocVQA, and also tested for cross-model and out-of-distribution generalization. Results show improvements of up to 40.40% over baseline methods on average scores across these settings. The work is presented as a preprint on arXiv and has not yet undergone formal peer review.
What's missing
As a preprint, SkillCAT has not undergone peer review. Key open questions include: computational overhead of sampling multiple trajectories per task compared to single-trajectory baselines, and whether the 40.40% improvement figure represents average or peak gains across all benchmarks tested.
What different sources said
- arXiv cs.CLCenter
SkillCAT: Contrastive Assessment and Topology-Aware Skill Self-Evolution for LLM Agents
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