The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning
arXiv:2603.17837v5 Announce Type: replace-cross Abstract: During conversational interactions, humans subconsciously engage in concurrent thinking while listening to a speaker. Although this internal cognitive processing may not always manifest as explicit linguistic structures, it is instrumental in formulating high-quality responses. Inspired by this cognitive phenomenon, we propose a novel Full-duplex LAtent and Internal Reasoning method named FLAIR that conducts latent thinking simultaneously with speech perception. Unlike conventional "thinking" mechanisms in NLP, which require post-hoc ge
Rapid advancements in AI, particularly in natural language processing and multimodal AI, are enabling more sophisticated models of human-like cognition, moving beyond explicit linguistic structures.
This research directly addresses a core limitation in current AI models by integrating 'internal thought' processes, potentially leading to more robust, context-aware, and human-like conversational AI.
AI models could begin to exhibit more sophisticated reasoning capabilities during real-time interaction, allowing for more natural and effective communication in complex dialogue scenarios.
- · AI researchers
- · Conversational AI platforms
- · Customer service industries
- · Cognitive computing developers
- · Simple chatbot architectures
- · Rule-based AI systems
AI systems will become better at understanding nuanced human communication and intent, even when not explicitly stated.
This improved understanding could lead to AI agents being more effective assistants, collaborators, and even educators.
The development of these 'internal thought' models might offer new insights into human cognition itself, creating a feedback loop between AI and neuroscience.
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Read at arXiv cs.CL