Conversational Behavior Modeling Foundation Model With Multi-Level Perception

• URL: http://arxiv.org/abs/2602.11065v1
• Date: Wed, 11 Feb 2026 17:32:52 GMT
• Title: Conversational Behavior Modeling Foundation Model With Multi-Level Perception
• Authors: Dingkun Zhou, Shuchang Pan, Jiachen Lian, Siddharth Banerjee, Sarika Pasumarthy, Dhruv Hebbar, Siddhant Patel, Zeyi Austin Li, Kan Jen Cheng, Sanay Bordia, Krish Patel, Akshaj Gupta, Tingle Li, Gopala Anumanchipalli,
• Abstract summary: We introduce framework models that predict intent and then reasons over conversational behaviors via a Graph-of-Thoughts (GoT)<n>GoT structures streaming predictions as an evolving graph, enabling a transformer to forecast the next speech act generate concise justifications for its decisions.<n>Experiments show that the framework delivers robust behavior detection, produces interpretable reasoning chains, and establishes a foundation for benchmarking conversational reasoning in full duplex spoken dialogue systems.
• Score: 13.659870465634228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human conversation is organized by an implicit chain of thoughts that manifests as timed speech acts. Capturing this perceptual pathway is key to building natural full-duplex interactive systems. We introduce a framework that models this process as multi-level perception, and then reasons over conversational behaviors via a Graph-of-Thoughts (GoT). Our approach formalizes the intent-to-action pathway with a hierarchical labeling scheme, predicting high-level communicative intents and low-level speech acts to learn their causal and temporal dependencies. To train this system, we develop a high quality corpus that pairs controllable, event-rich dialogue data with human-annotated labels. The GoT framework structures streaming predictions as an evolving graph, enabling a transformer to forecast the next speech act, generate concise justifications for its decisions, and dynamically refine its reasoning. Experiments on both synthetic and real duplex dialogues show that the framework delivers robust behavior detection, produces interpretable reasoning chains, and establishes a foundation for benchmarking conversational reasoning in full duplex spoken dialogue systems.

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