Problem
Research questions and friction points this paper is trying to address.
capability-bound process supervision
query-to-agent matching
executable capability
Debate-to-Skill: Capability-Bound Process Supervision for Industrial Query-to-Agent Annotation
🤖 AI Summary
本文针对工业查询与代理匹配中的能力误判问题，提出了一种基于辩论的方法（Debate-to-Skill），通过过程监督来提高匹配准确性。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
capability-bound process supervision
Debate-to-Skill
disagreement-driven refinement
🔎 Similar Papers
No similar papers found.
