Liberating LLM Capabilities in Full-Duplex Speech Models
Speech-based large language models are typically constrained to spoken replies, which limits their user-facing outputs to what can be verbalized and suppresses text-native capabilities such as code generation, structured analysis, and multi-step reasoning in realtime interaction, for tasks that require persistent, structured, and inspectable intermediate outputs. Existing work improves spoken reasoning or full-duplex turn-taking, but still treats text as a hidden intermediate state or a subordinate modality rather than a first-class output channel. We propose Listen-Write-Speak (LWS), a text-first tri-channel paradigm in which a single autoregressive LLM continuously listens to user audio...
| Authors | Luoyuan Zhang, Bokai Xu, Junbo Cui et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 May 2026 |
| Last updated at source | 15 September 2026 |
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