MusTBENCH evaluates temporal grounding in large audio-language models via five expert-validated tasks, and MusT improves performance through encoder adaptation, LLM adaptation, supervised fine-tuning, and RL optimization.
Bryan, Zeyu Jin, and Justin Salamon
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MOSS-Audio is an audio-language model using a 12.5 Hz encoder, DeepStack cross-layer injection, time markers, and an event-preserving annotation pipeline for unified audio understanding.
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MusTBENCH: Benchmarking and Advancing Temporal Grounding in Music LLMs
MusTBENCH evaluates temporal grounding in large audio-language models via five expert-validated tasks, and MusT improves performance through encoder adaptation, LLM adaptation, supervised fine-tuning, and RL optimization.