VQV Signal
DuplexJev processes ASR-encoder hidden states through a connector into a frozen LLM, producing single-token distributions without decoding. This approach allows an 8-GPU node to answer 80 decisions about eight utterances in approximately 0.1 seconds.
DuplexJev processes ASR-encoder hidden states through a connector into a frozen LLM, producing single-token distributions without decoding. This approach allows an 8-GPU node to answer 80 decisions about eight utterances in approximately 0.1 seconds.
AI-assisted summary based on the listed source.
Full-duplex voice agents make many small, closed decisions, which current systems answer by slow autoregressive decoding. We propose DuplexJev, which feeds ASR-encoder hidden states through a small connector into a frozen LLM and reads each question as a single-token distribution over its options. Nothing is...
By avoiding slow autoregressive decoding, DuplexJev significantly speeds up decision-making in full-duplex voice agents. This method could improve responsiveness and efficiency in real-time voice applications.
Hardware and robotics watchers may want to track whether this becomes a product, benchmark, or deployment signal.
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VQV surfaced this signal because it is recent, relevant to AI Voice, connected to arXiv.
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