Jev made "System One" decision models the hot idea of the season: state in, typed probabilistic decisions out, many questions in one pass. Voice agents need it most, yet a text decision model only sees a transcript, and the transcript is where tone disappears.
DuplexJev takes raw audio as its state. A frozen ASR encoder feeds a frozen LLM through a small connector; every question is a choice question read as one token, nothing decoded.
⚡ Fast: 10 decisions in 92 ms on one H200 (transcribe + LLM: 1,978 ms), ~170 decisions/s per GPU, under $0.50 per million decisions (4B). A new decision is one line of text.
🏆 Hears what the transcript drops: #1 in paralinguistic understanding. LISTEN (EACL 2026) + StressTest + MSPB, 1,035 questions, none from our training corpora, same for every model: DuplexJev-32B-Para v1.3: 50.2 Gemini 3.8 Flash: 47.8 Qwen3-Omni-30B: 45.0 Gemini 3.1 Pro: 41.0 GPT-audio-1.5: 40.9 Gemini 3.8 Live: 40.0 GPT-realtime-2.1: 35.6 Prosody (MSPB): 77.3 vs 71.9 next best. (Split suggested by @dipankarsarkar, thank you!)
How it learned to listen: speech LLMs trained by transcript distillation imitate a teacher that never heard a voice (gender ~55%, emotion ~28%). Training the answer token on the correct answer instead takes both to ~90%.