SpectCount fine-tunes LALMs using on-the-fly synthetic signals to fix identified spectrotemporal weaknesses and boost performance on unseen auditory benchmarks.
Audio-Maestro: Enhanc- ing large audio-language models with tool-augmented reasoning,
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Audio-Mind introduces a conditional, auditable agentic framework for audio understanding that preserves frontend judgment and acquires bounded external evidence only when needed, reporting 80.4% on MMAR and 82.8% on MSU-Bench.
citing papers explorer
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SpectCount: Spectrotemporal Counting via Synthetic Signals Improves Large Audio Language Models
SpectCount fine-tunes LALMs using on-the-fly synthetic signals to fix identified spectrotemporal weaknesses and boost performance on unseen auditory benchmarks.
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Audio-Mind: An Auditable Agentic Framework for Audio Understanding
Audio-Mind introduces a conditional, auditable agentic framework for audio understanding that preserves frontend judgment and acquires bounded external evidence only when needed, reporting 80.4% on MMAR and 82.8% on MSU-Bench.