Acoustic-to-Text KV压缩：让全双工语音模型长对话内存降64.6%
Acoustic-to-Text KV Compression for Full-Duplex Speech Models
全双工语音模型聊久了内存会爆，这篇用转录旁路压缩KV，十分钟会话峰值缓存直接砍掉64.6%，性能还不掉。
论文提出 acoustic-to-text KV compression，在全双工语音语言模型的 listening-time slack 区间内，通过转录旁路把语音转成紧凑的文本记忆。
推理时 KV 缓存超过预算即淘汰旧声学状态，保留转录与近期声学上下文。旁路用 LoRA 训练，并以知识蒸馏保持原始模型的听说行为。
在 MiniCPM-o 4.5 上的实现使十分钟 LongSpeech 会话的峰值流式 KV 缓存降低 64.
6%，转录、时序问答和摘要指标同时优于基线，Full-Duplex-Bench 上的停顿、轮次切换和打断表现持平。
Acoustic-to-Text KV Compression for Full-Duplex Speech Models
Full-duplex speech language models continuously accumulate acoustic key-value (KV) states, making long-running interactions memory-intensive. During listening, the model can finish processing an audio unit before the next arrives; we term the remaining interval listening-time slack. We propose acoustic-to-text KV compression, which introduces a transcription side channel to convert incoming speech into compact textual memory within this interval. When the cache exceeds a target budget during inference, older acoustic states are evicted while transcripts and recent acoustic context remain. We train the side channel with LoRA using cross-entropy on transcription segments. To preserve listening and speaking behavior, we apply knowledge distillation to the original model's token-level output distributions at native prediction positions. On ten-minute LongSpeech sessions, our MiniCPM-o 4.5 implementation reduces peak streaming KV-cache size by 64.6% compared with the same model without eviction. The proposed method also improves transcription, temporal question answering, and summarization over the baseline. Full-Duplex-Bench evaluations further show comparable pause-handling, turn-taking, and interruption performance.
本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：MiniCPM-o 4.5、全双工语音、KV压缩、LoRA，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.AI」（@Yejin Lee, Seungbeom Kim, Yongha Lee, Kyuhong Shim），经 AITOP 多维评分管线结构化提炼。截至目前，全网已有 3 家独立信源对该事件进行交叉印证与跟进报道（含 TestingCatalog、LangChain 等），属于近期高关注度行业信号。
