CATT: Complexity-Aware Thinker–Talker Orchestration for Low-Latency Spoken Responses in Digital Humans

Digital humans must speak promptly, but thinking-enabled large language models (LLMs) answer only after reasoning. CATT (Complexity-Aware Thinker–Talker) routes easy turns to a direct reply; hard turns get an answer-free acknowledgment, progress speech from background reasoning, then the final answer. It needs no fine-tuning and is plug-and-play: its orchestration code runs unchanged when the model endpoint, router, or speech and playback layer is replaced. Evaluated on the transcript-to-speech subsystem (local Qwen3-32B, 738 isolated turns, and virtual playback), CATT starts speaking about 6.3 s earlier than a prompt-matched thinking baseline. The first substantive content comes about 10.5 s earlier on closed-form math and science questions and 3.0 s earlier on open-ended requests (about 1 s later when routed to reasoning). The final closed-form answer arrives about 3.2 s later at the same accuracy. An LLM-judged listening rating, a proxy that includes waiting, places CATT about 1 point above the thinking baseline but 0.42 below a non-thinking baseline on open-ended requests. Both latency gains held for three model families accessed through hosted APIs and in an end-to-end avatar case study. A limited ReVerT reproduction on the same reasoning started speaking 8.4 s after CATT. Real users, microphones, networks and multi-user load remain untested.