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RADAR is proposed, an automated framework for knowledge boundary discovery and tool overuse mitigation that globally propagates labels and rewrites conflicts, yielding deployment-aligned decisions with fewer unnecessary tool calls.
RD-MCTS, a Monte Carlo Tree Search framework that incorporates diagram-derived structural priors, constraint-consistent state transitions, and inference-time step-level process rewards is proposed, aimed at improving constraint-consistent reasoning on high-difficulty questions.
This work pro-poses the D iscriminative-to-G enerative ( D2G ) framework, which first leverages discriminative models to produce a top-k set of candidate relations, and then integrates this knowledge into generative models via in-context or prompt learning.
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