Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking

• URL: http://arxiv.org/abs/2607.03093v1
• Date: Fri, 03 Jul 2026 08:24:23 GMT
• Title: Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking
• Abstract summary: Thinking has emerged as a critical capability for Large Language Models tackling complex tasks.<n>We propose Proactive Thinking, a framework that empowers models to pre-compute potential response elements during conversational downtime.<n>We demonstrate that proactive thinking effectively improves interaction efficiency without compromising performance.
• Score: 42.788336763990316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Thinking has emerged as a critical capability for Large Language Models (LLMs) tackling complex tasks. However, its reactive nature, where reasoning is passively triggered only upon receiving a user response, inevitably introduces latency that compromises conversational fluidity. This stands in sharp contrast to human dialogue, where speakers proactively anticipate and plan future content during natural pauses to ensure seamless interaction. To bridge this gap, we propose Proactive Thinking, a framework that empowers models to pre-compute potential response elements during conversational downtime instead of waiting idly for the next input. We then introduce a training-free baseline that can think ahead by anticipating future states, balancing efficiency and quality through speculative continual thinking. To evaluate this approach in practice, we adapt three benchmarks of varying complexity into time-aware environments that simulate real-time conversational flow. We demonstrate that proactive thinking effectively improves interaction efficiency without compromising performance. Ultimately, this work advocates for a fundamental shift toward more intelligent, anticipatory, and real-time conversational AI.

Related papers

• The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning [76.96796481909581]
  Internal cognitive processing is instrumental in formulating high-quality responses.<n>We propose a novel method named FLAIR that conducts latent thinking simultaneously with speech perception.<n>Our approach aligns seamlessly with spoken dialogue systems.
  arXiv  Detail & Related papers  (2026-03-18T15:30:29Z)
• ProAct: A Dual-System Framework for Proactive Embodied Social Agents [6.33976966063203]
  Embodied social agents have recently advanced in generating synchronized speech and gestures.<n> Proactive social behavior requires deliberation over accumulated context and intent inference.<n>We present emphProAct, a dual-system framework for streaming multimodal interaction.
  arXiv  Detail & Related papers  (2026-02-15T08:27:34Z)
• Chronological Thinking in Full-Duplex Spoken Dialogue Language Models [66.84843878538207]
  Chronological Thinking aims to improve response quality in full SDLMs.<n>No additional latency: once the user stops speaking, the agent halts thinking and begins speaking without further delay.<n>Results: Experiments demonstrate the effectiveness of chronological thinking through both objective metrics and human evaluations.
  arXiv  Detail & Related papers  (2025-10-02T10:28:11Z)
• Proactive Conversational Agents with Inner Thoughts [42.34140072445951]
  One of the long-standing aspirations in conversational AI is to allow them to autonomously take initiatives in conversations, i.e., being proactive.<n>Prior NLP research focused mainly on predicting the next speaker from contexts like preceding conversations.<n>We propose that just like humans, rather than merely reacting to turn-taking cues, a proactive AI formulates its own inner thoughts during a conversation, and seeks the right moment to contribute.
  arXiv  Detail & Related papers  (2024-12-31T10:41:56Z)
• Predictive Speech Recognition and End-of-Utterance Detection Towards Spoken Dialog Systems [55.99999020778169]
  We study a function that can predict the forthcoming words and estimate the time remaining until the end of an utterance. We develop a cross-attention-based algorithm that incorporates both acoustic and linguistic information. Results demonstrate the proposed model's ability to predict upcoming words and estimate future EOU events up to 300ms prior to the actual EOU.
  arXiv  Detail & Related papers  (2024-09-30T06:29:58Z)
• Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy [83.61404191470126]
  We propose a new solution named I-Pro that can learn Proactive policy in the Interactive setting. Specifically, we learn the trade-off via a learned goal weight, which consists of four factors. The experimental results demonstrate I-Pro significantly outperforms baselines in terms of effectiveness and interpretability.
  arXiv  Detail & Related papers  (2022-04-07T14:11:31Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.