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AV-Flamingo outperforms existing models on complex audio-visual tasks, revealing that size isn't everything when it comes to reasoning capabilities.
LALMs can boost their temporal reasoning accuracy by 3.2% simply by better redistributing attention across audio tokens rather than relying on textual cues.
Audio-language models can now reason about 30-minute-long audio clips with timestamp-grounded intermediate steps, unlocking a new level of fine-grained understanding.
AVLLMs may "hear" at intermediate layers, but they largely ignore audio cues in favor of vision when generating text, revealing a fundamental modality bias.
