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The Ohio State University
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"Lost at the End" reveals a striking primacy bias in multimodal KB-VQA, where early context dramatically outperforms late context, challenging conventional evaluation metrics.
Open-source QUEST agents, trained solely on 8K synthetic tasks, rival or surpass proprietary research agents, proving that scaling data synthesis can unlock frontier performance.
