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Current MLLMs struggle with Bangla form comprehension, missing key granular details that could hinder their real-world application in low-resource languages.
Higher offline conservatism in training can paradoxically increase vulnerability to reward hacking during online adaptation, challenging long-held assumptions in the field.
No automatic metric can effectively balance validity and discriminative power in evaluating LLM-generated responses, revealing a fundamental limitation in current evaluation practices.
S-JEPA sets a new standard in speech representation learning by achieving top performance with fewer parameters and without the cumbersome offline re-clustering process.
