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The paper introduces acoustic-to-text KV compression for full‑duplex speech models, converting acoustic key‑value states into compact textual memory during listening‑time slack. When the KV cache exceeds a target budget, older acoustic states are evicted while transcripts and recent acoustic context are retained. Experiments on ten‑minute LongSpeech sessions show a 64.6% reduction in peak streaming KV‑cache size and improved transcription, temporal question answering, and summarization, with comparable pause‑handling, turn‑taking, and interruption performance in Full‑Duplex‑Bench.
Full-duplex speech models require training data that preserves turn-taking, overlap, interruption, and backchannel behavior, yet these signals are entangled across speakers in noisy real-world recordi...
PERSIST is a persistent memory system designed for multi-session, multi-speaker spoken dialogue, explicitly modeling Who, What, and When to enable voice assistants to recall past conversations and adapt to individual users. It structures cross-session histories into readable event records and retrieves them using a 3W joint scoring mechanism that combines semantic content, acoustic speaker identity, and temporal state. By reusing intermediate representations from the dialogue backbone, PERSIST reduces retrieval latency from 578.42 ms to 7.03 ms and achieves 85.08% end‑to‑end task accuracy on the SpokenTrace benchmark, improving EM@3 from 49.01% to 82.10%.
arXiv:2606.11167v2 Announce Type: replace Abstract: Full-duplex spoken dialogue models can listen and speak simultaneously, making them a promising architecture for natural conversation. However, cur...
arXiv:2606. 07547v1 Announce Type: cross Abstract: Speech-based large language models are typically constrained to spoken replies, which limits their user-facing outputs to what can be verbalized and suppresses text-native capabilities such as code generation, structured analysis, and multi-step reasoning in realtime interaction, for tasks that require persistent, structured, and inspectable intermediate outputs.
arXiv:2609.39446v1 Announce Type: new Abstract: Existing full-duplex speech benchmarks cover only subsets of real-time interaction behaviors, often under limited contextual conditions. We introduce D...