让 AI 边听边干活，少让你等
语音助手最烦人的时刻：你话还没说完，它已经停了，然后你等它反应。这篇论文把等待时间变成干活时间——AI 在听你说话的同时，就不断用最新听到的半句话去预判、去查资料、去准备工具调用，而且不打断已经开始的准备工作。结果是在一个测试集上，工具调用覆盖率提升 16 个百分点，最终回答的平均延迟降低 36.4%。它不是你明天就能用上的功能，但这是语音助手从「你说完它才开始想」走向「它边听边想」的关键一步。
📄 原文摘要(英文)
Tool-using voice agents often leave users waiting after speech ends, even when actionable information arrives earlier in the request. Speculative approaches begin work during speech, but chunk-bounded or interrupt-on-update generation can truncate a tool call before its arguments are complete. This couples computation time to speech updates, limiting useful work completed in advance. We introduce Heartbeat Thinking, which handles updated input through new Thinker invocations while allowing ongoing invocations and dispatched tools to finish. Periodic invocations read the latest partial transcript and a shared record of completed reasoning and tool results. These results help resolve missing arguments and initiate dependent calls, allowing preparation to progress across speech updates. Across SpeechFCEval, FDBench v3, and AudioCRAG, Heartbeat improves aggregate speculative tool coverage or retrieval reuse over a SHANKS-style windowed baseline, with benchmark-dependent effects on final task quality. On FDBench v3, it increases tool coverage by 16 percentage points and reduces mean final-response latency by 36.4\%. The results show that heartbeat thinking paradigm preserving ongoing computation during speech can turn listening time into reusable preparation for faster and more capable voice-agent responses.