Qwen3.5 Omni
qwen3-5-omni-a943bd43·2 events·first seen 24d agoAliases: Qwen3.5 Omni, Qwen3-Omni
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DOA: Training-Free Decoder-Only Attention Policy for Long-Form Simultaneous Speech Translation with SpeechLLMs
The paper proposes Decoder-Only Attention (DOA), a training-free streaming policy for simultaneous speech-to-text translation (SimulST) that works with off-the-shelf decoder-only Speech LLMs. DOA derives proxy alignment signals from self-attention rather than cross-attention, enabling long-form simultaneous translation without retraining. Experiments on Phi4-Multimodal and Qwen3-Omni demonstrate low-latency performance approaching offline decoding quality, validating that decoder self-attention contains sufficient alignment information for streaming decisions.
Thinking Machines Lab Reveals TML-Interaction-Small: Real-Time Multimodal Interaction Model
Thinking Machines Lab (founded by Mira Murati) has announced TML-Interaction-Small, a 276B-parameter mixture-of-experts multimodal model that processes audio, video, and text concurrently using 200ms 'micro-turns' rather than waiting for conversational turns to complete. The architecture uses encoder-free early fusion, pairing a fast foreground interaction model with an asynchronous background reasoning model that shares context. On interactivity benchmarks (FD-bench V1/V1.5), it outperforms GPT-Realtime-2 and Gemini-3.1-flash-live-preview, though it trails GPT-Realtime-2 on intelligence benchmarks. A closed research preview is expected in coming months with wider release later in 2026.