CaSKG: Counterfactual-Causal Skill Graphs for Scalable Agent Skill Retrieval
Reusable skill libraries allow large language model (LLM) agents to reuse procedural knowledge across tasks, but they also turn memory access into a challenging retrieval problem. Full-library prompting preserves coverage at high context cost, vector retrieval returns compact neighborhoods but treats skills as independent text, and graph-based retrieval can recover workflow context only when the edges that carry relevance are reliable. We propose CaSKG, a counterfactual-causal skill graph framework that calibrates procedural relations before retrieval. CaSKG first builds a high-recall directed candidate graph from semantic, lexical, input/output, and structural evidence, with repair evide...
| Authors | Zhiyuan Li, Linyuan Gao, Xuechun Ding et al. |
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| Primary category | cs.AI |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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