Heartbeat 思考：实时语音代理中的推测推理与工具执行
Heartbeat Thinking: Speculative Reasoning and Tool Execution While Listening in Realtime Voice Agents
Heartbeat 思考通过在语音更新期间继续准备工具调用，提升了语音代理的覆盖率和响应速度。该技术使语音代理在用户说话时就开始准备工具调用，解决了传统方法因语音更新而中断调用的限制。在 FDBench v3 基准测试中，Heartbeat 思考将工具覆盖率提高了 16 个百分点，并将平均最终响应延迟降低了 36.4%。
Published on Oct 19
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Abstract
A heartbeat-style speculative reasoning approach lets voice agents continue preparing tool calls during speech updates, improving coverage and reducing response latency.
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.
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来源: Hugging Face Daily Papers · huggingface.co