arXiv:2606.07547v1 Announce Type: cross Abstract: 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 chan
Ongoing advancements in large language models (LLMs) and speech processing are enabling exploration into more integrated and versatile AI interaction modalities.
This development addresses a critical limitation of current speech-based AI, unlocking text-native capabilities for real-time, inspectable human-AI interaction in complex tasks.
AI systems can now potentially leverage their full reasoning and generation capabilities in spoken conversations, moving beyond simple verbal responses to include structured outputs and multi-step processes.
- · AI developers
- · Enterprise software
- · Customer service platforms
- · Generative AI startups
- · Text-only productivity tools
- · Simple voice assistants
- · Narrow AI solutions
Full-duplex speech models will offer a richer, more comprehensive user experience.
This improved interaction could lead to greater adoption of AI agents in complex professional workflows, collapsing traditional SaaS layers.
The enhanced human-AI collaboration facilitated by these models may accelerate the development of more sophisticated and autonomous AI systems across various industries.
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Read at arXiv cs.AI