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VisConf: Quality-Aware Consensus for Best-of-N in Vision-Language Model Reasoning
Best-of-N methods improve text-only LLM reasoning by sampling multiple trajectories before selecting an answer. For Vision-Language Models (VLMs), answer frequency and model confidence alone can be insufficient, as sampled generations also vary in their engagement with the visual input. We find that
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