GraphSkillAA: Attribution-Guided Skill-Graph Updating with Targeted Validation and Rollback
External skills provide domain knowledge and procedures without updating model parameters, but flat collections obscure skill applicability, dependencies, and composition. Graphs organize skills into addressable nodes and explicit relations, supporting selection and composition. Yet existing skill-graph methods use this structure mainly for retrieval, rather than to localize updates, scope retesting, or precisely roll back rejected changes. We introduce GRAPHSKILLAA (GraphSkill Abductive Attribution), which uses one addressable graph for skill selection, execution, failure attribution, targeted updating, validation, and rollback. Nodes separate applicability, execution, and exclusion cond...
| Authors | Ziqiao Shang, Ling-Yue Ge, Lan-Zhe Guo |
|---|---|
| Primary category | cs.AI |
| Published | 17 September 2026 |
| Last updated at source | 23 September 2026 |
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