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University of Maryland
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TEMPO not only achieves superior timestamping accuracy in audio-language models but also integrates innovative techniques like atomic timestamp tokens and reinforcement learning for enhanced performance.
LALMs can boost their temporal reasoning accuracy by 3.2% simply by better redistributing attention across audio tokens rather than relying on textual cues.
