AsyncVoice Agent: Real-Time Explanation for LLM Planning and Reasoning
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.
68f6f42324c44893631118c7
Yueqian Lin
claimed_verified
2025-10-21T14:03:33.005Z
64b5198c25882acb62fb77ef
https://cdn-avatars.huggingface.co/v1/production/uploads/64b5198c25882acb62fb77ef/HX9pfMEPQlfjvSAgSLplY.png
Yueqian Lin
user
linyueqian
68f6f42324c44893631118c8
Zhengmian Hu
68f6f42324c44893631118c9
Jayakumar Subramanian
68f6f42324c44893631118ca
Qinsi Wang
68f6f42324c44893631118cb
Nikos Vlassis
68f6f42324c44893631118cc
Hai "Helen" Li
68f6f42324c44893631118cd
Yiran Chen
AsyncVoice Agent: Real-Time Explanation for LLM Planning and Reasoning
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.
AsyncVoice Agent, with its asynchronous architecture, enhances human-AI collaboration by enabling real-time interaction and interruption of the model's reasoning process, significantly reducing latency while maintaining accuracy.
Chain-of-Thought CoT streaming LLM backend conversational voice frontend narration inference interaction latency high-fidelity task accuracy two-way dialogue