Debate-to-Skill: Capability-Bound Process Supervision for Industrial Query-to-Agent Annotation

Abstract:Industrial query-to-agent matching fails when topical relevance is mistaken for executable capability, especially on long-tail and boundary-sensitive requests. We formulate annotation as \emph{capability-bound process supervision} and instantiate it with Debate-to-Skill, which uses reusable decision principles, structured deliberation, verifier-based verdict extraction, and disagreement-driven refinement. On an industrial Query2Agent benchmark, we compare Debate-to-Skill with direct-label supervision, reasoning-SFT, and structural ablations. The results test whether gains come from supervising the capability-critical decision process itself, especially on grey-zone cases where semantic relatedness and executable capability diverge.

Artificial Intelligence (cs.AI)

arXiv:2609.11176 [cs.AI]

(or arXiv:2609.11176v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.11176 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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