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Unigram entropy emerges as a key predictor of codec performance, revealing stark differences in how various architectures handle noise conditions.
Models trained on CoVA-SFT outperform existing multimodal reasoning baselines by over 2x, revealing the potential of structured visual-textual integration in AI.
