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The introduced Programmatic Search Agent (PSA), which makes a local executable computation over candidates the unit of a search action, unifies a persistent candidate workspace, flexible primitive composition, and selective evidence presentation.
This paper proposes UniRank, a Homogeneous Scaling framework that consolidates multiple ranking objectives into a single, unified generative model and introduces two paradigmatic innovations: Parallel Prediction via Task-Specific Anchors and Reinforcement Learning Optimization with Posterior CoT.
These findings suggest that, within the pointwise scoring paradigm, routing continuous relevance semantics through discrete text constrains ranking signal resolution reveals a bottleneck that is stable and difficult to overcome under current standard methods, rather than an easily resolvable training bias.
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