Papers by Yujin Zhou

2 papers
Omni-RewardBench: Toward a Comprehensive Evaluation of Generative Reward Models Across Modalities (2026.acl-long)

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Challenge: Existing evaluation benchmarks for ORMs are largely text-centric or limited to bimodal tasks . a new study examines the effectiveness of Omni-RewardBench for ORms across modalities .
Approach: They propose a hybrid automatic-annotation and human-verification pipeline to construct high-quality evaluation data.
Outcome: The proposed model is the first benchmark for comprehensive evaluation of ORMs across modalities.
Glance-or-Gaze: Incentivizing LMMs to Adaptively Focus Search via Reinforcement Learning (2026.findings-acl)

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Challenge: Existing search-augmented approaches rely on indiscriminate whole-image retrieval and lack deep iterative reflection, limiting their effectiveness on complex visual queries.
Approach: They propose a fully autonomous framework that shifts from passive perception to active visual planning and introduces a Selective Gaze mechanism that dynamically chooses whether to glance at global context or gaze into high-value regions.
Outcome: Experiments across six benchmarks demonstrate state-of-the-art performance.

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