Recent advances in spoken dialogue language models (SDLMs) reflect growing interest in shifting from turn-based to full-duplex systems, where the models continuously perceive user speech streams while generating responses. This simultaneous listening and speaking design enables real-time interaction and the agent can handle dynamic conversational behaviors like user barge-in. However, during the listening phase, existing systems keep the agent idle by repeatedly predicting the silence token, which departs from human behavior: we usually engage in lightweight thinking during conversation rather than remaining absent-minded. Inspired by this, we propose Chronological Thinking, an on-the-fly conversational thinking mechanism that aims to improve response quality in full-duplex SDLMs. Specifically, chronological thinking presents a paradigm shift from conventional LLM thinking approaches, such as Chain-of-Thought, purpose-built for streaming acoustic input. (1) Strictly causal: the agent reasons incrementally while listening, updating internal hypotheses only from past audio with no lookahead. (2) No additional latency: reasoning is amortized during the listening window; once the user stops speaking, the agent halts thinking and begins speaking without further delay. Experiments demonstrate the effectiveness of chronological thinking through both objective metrics and human evaluations show consistent improvements in response quality. Furthermore, chronological thinking robustly handles conversational dynamics and attains competitive performance on full-duplex interaction metrics.

翻译：近期口语对话语言模型（SDLMs）的进展反映了对话系统从轮次式向全双工模式转变的日益增长需求。在此类系统中，模型在生成回复的同时持续感知用户语音流。这种同步听说的交互设计不仅实现了实时对话，还使智能体能够处理用户打断等动态会话行为。然而在聆听阶段，现有系统通过重复预测静默标记使智能体处于空闲状态，这与人类对话行为相悖：人类在交流过程中通常会进行轻量级思考，而非完全放空。受此启发，我们提出时序思考机制——一种旨在提升全双工SDLMs回复质量的实时对话推理机制。具体而言，时序思考实现了从传统大语言模型推理范式（如思维链）到专为流式声学输入设计的根本性转变：（1）严格因果性：智能体在聆听时进行增量推理，仅根据历史音频更新内部假设，绝不进行前瞻预测；（2）零额外延迟：推理过程被平均分配至整个聆听窗口，当用户停止说话时智能体立即终止思考并开始回复，无需额外等待。实验通过客观指标与人工评估表明，时序思考机制能持续提升回复质量。此外，该机制能稳健处理会话动态变化，在全双工交互指标上取得具有竞争力的性能表现。