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Author

Wenjun Ke

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Conference Sep 2026

Mitigating Tool Overuse for LLMs via Active Knowledge Boundary Probing

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.

Zhao-Yu Yang, Wenjun Ke, Yuan-Yao Li et al. · 0 citations
Conference Open access Sep 2026

Benchmarking and Enhancing Relational Diagrams Reasoning for Multimodal Large Language Models

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.

Tianyu Hong, Peng Wang, Wenjun Ke et al. · 0 citations

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