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Applied AI Institute
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MLLMs struggle with reasoning on complex documents, with even the best models showing significant performance gaps on BEAR-Bench.
Allocating memory for larger batches rather than more negative samples can significantly enhance convergence speed and recommendation quality in neural recommender systems.
