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This work augments the LALM with a dedicated frame-level grounding model while leveraging its semantic modeling capability to represent the event query, and shows that the grounding model can provide temporal evidence to support downstream reasoning.
LALMs can gain a far more precise sense of time by simply interleaving learned time embeddings into their audio feature sequences and then being fine-tuned with RL.