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ReGround is introduced, a large-scale dataset for reviewer comment grounding that links 10,267 reviewer comments to 16,274 evidence in the original anonymous submission of 3,656 papers and exposes grounding reviewer comments as a difficult and practically important problem for scientific document understanding.
Forget costly supervised fine-tuning鈥攖his new contrastive reward mechanism enables efficient zoom-in tool learning in MLLMs, outperforming traditional methods.
