Enhancing Audio Reasoning via Semantic Summary Prediction
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arXiv:2606. 14591v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have shown strong performance on a wide range of audio understanding tasks, yet they still struggle with complex audio reasoning.
arXiv:2603.02266v2 Announce Type: replace-cross Abstract: Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-La...
arXiv:2509. 22363v4 Announce Type: replace Abstract: Large Audio Language Models (LALMs) integrate audio encoders with pretrained Large Language Models to perform complex multimodal reasoning tasks.
arXiv:2609.23589v1 Announce Type: cross Abstract: Large audio-language models (LALMs) are increasingly used for a broader range of audio reasoning tasks. These models typically incorporate audio repr...
LaSR (Latent Speech Reasoning) is a new training paradigm for context‑aware speech recognition that uses a latent reasoning trajectory instead of explicit intermediate tokens. It aligns chain‑of‑thought supervision around the acoustic region of target words and introduces latent reasoning periods for grounding context and guiding transcription transitions. Experiments on Fun‑Audio‑Chat show that LaSR improves terminology recognition without added latency and outperforms standard fine‑tuning baselines, demonstrating the promise of latent reasoning for efficient, context‑aware speech assistants.
arXiv:2606. 17417v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) achieve strong performance on a variety of audio understanding tasks but continue to struggle with temporal reasoning, a fundamental capability central to human auditory perception.