2025/10/17 by Yueqian Lin, Lin, Yueqian, Zhengmian Hu +11
Computer Science · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #FOS: Electrical engineering #Multimedia (cs.MM) #Multimodal Machine Learning Applications #Topic Modeling #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2510.16156
openalex publication_date 2025/10/17 · openalex created_date 2025/10/22 · openalex updated_date 2026/07/28
Effective human-AI collaboration on complex reasoning tasks requires that users understand and interact with the model's process, not just receive an output. However, the monolithic text from methods like Chain-of-Thought (CoT) prevents this, as current interfaces lack real-time verbalization and robust user barge-in. We present AsyncVoice Agent, a system whose asynchronous architecture decouples a streaming LLM backend from a conversational voice frontend. This design allows narration and inference to run in parallel, empowering users to interrupt, query, and steer the model's reasoning process at any time. Objective benchmarks show this approach reduces interaction latency by more than 600x compared to monolithic baselines while ensuring high fidelity and competitive task accuracy. By enabling a two-way dialogue with a model's thought process, AsyncVoice Agent offers a new paradigm for building more effective, steerable, and trustworthy human-AI systems for high-stakes tasks.