Beyond Task-Only Matching: Personalized Skill Routing with Counterfactual Evaluation
The rapid expansion of reusable skill repositories makes skill routing a critical capability for large language model (LLM) agents. Existing methods treat routing as task-only semantic matching. However, when users with incompatible constraints issue an identical request, this assumption conflates task relevance with skill suitability: a task-only router can select a semantically plausible skill that is unsuitable for the requesting user. To expose this failure mode, we formulate \textit{personalized skill routing} as profile-conditioned retrieval, in which relevance depends jointly on the task and the user profile. We first introduce a profile-counterfactual benchmark, in which the task ...
| Authors | Tianle Wang, Yanghe Zou, Xiang Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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