RetroThinker: Enabling Retrospective Thinking in Speech LLMs
Speech large language models (SpeechLLMs) offer reduced latency and retain paralinguistic nuances that are typically lost in cascaded automatic speech recognition (ASR) and text-based LM architectures. However, they continue to lag behind text-only LLMs on complex reasoning tasks, while real-time spoken interaction imposes strict latency constraints. Although prior works employ Chain-of-Thought (CoT) and concurrent reasoning to enhance reasoning capabilities without inducing prohibitive delays, an inherent accuracy-latency trade-off persists. In this paper, we investigate whether a streaming SpeechLLM can dynamically revise its reasoning traces on the fly. We introduce RetroThinker, a mul...
| Authors | Yi-Jen Shih, Puyuan Peng, Abdelrahman Mohamed et al. |
|---|---|
| Primary category | eess.AS |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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