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Published in ICLR, 2025
In this paper, we first devise a standard association benchmark based on adjective and verb association semantic concepts. Instead of costly data annotation and organization, we propose a convenient annotation-free reconstruction method transforming the general dataset for our association tasks. Furthermore, we comprehensively investigate the MLLMs’s ability and potential for association ability. Project Code Paper
Published in ICLR, 2026
We identify a critical gap in MLLMs’ physics comprehension and propose Scene Dynamic Field, integrating physics simulators into a fine-tuning framework. Our method achieves up to 20.7% gains on fluid tasks while generalizing to unfamiliar physical domains. Paper Code
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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