AsyncVoice Agent: Real-Time Explanation for LLM Planning and Reasoning
Authors: name: Yueqian Lin, name: Zhengmian Hu, name: Jayakumar Subramanian, name: Qinsi Wang, name: Nikos Vlassis, name: Hai "Helen" Li, name: Yiran Chen

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.