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This work proposes MATCH, a closed-loop framework for model-aware tool learning with curriculum scheduling and hierarchically gated rewards, and shows consistent improvements across four backbones from two model families.
Population-Guided Intent-Aware Rewriting (PGIR) is proposed, which captures dominant population intent via a Semantic Clustering Unit (SCU) and generates intent-aware rewrites through a Rewriting Unit (U), aligning queries with mainstream search goals without requiring user history.
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