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Detailed captions transfer visual information into vision-language model supervision, but quality is easily conflated with length: long single-pass outputs still miss small objects, text, and spatial relations or promote uncertain observations into confident claims. We formulate fine-grained captioning as recoverable v...
MEMO is proposed, a multimodal evidence memory organization method for LLM agents that presents memory more efficiently with fewer memory tokens, improves downstream task performance, and builds more effective working memory under constrained budgets.
COPD is proposed, a contrastive OPD framework that substantially reduces reasoning length without compromising model performance and consistently improves efficiency across different tasks and model scales.
This work introduces Edit2TikZ, a comprehensive benchmark for scientific figure editing tasks, featuring 1,548 diverse and high-quality samples, and constructs a human-aligned evaluation framework to measure whether a requested edit is completed while irrelevant content is preserved.
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