Mitigating Perceptual Judgment Bias in Multimodal LLM-as-a-Judge via Perceptual Perturbation and Reward Modeling
- Published
- Source
- arXiv
- Paper number
- 286
- Field
- Computer Vision
- arXiv ID
- 2606.02578
Key points
- To address this issue, the paper introduces the Perceptually Perturbed Judgment Dataset, which constructs minimally edited counterfactual responses that separate perceptual errors and enable verifiable supervision.
- Experiments across multiple MLLM-as-a-Judge benchmarks show that this approach substantially improves perceptual fidelity, ranking consistency, and alignment with human judgments.
- The result establishes a scalable and generalizable path toward training multimodal judges that are perceptually grounded, interpretable, and robust to vision reasoning conflicts.
Paper links
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