DepthWeave-KV: Token-Adaptive Cross-Layer Residual Factorization for Long-Context KV Cache Compression
Score
78提出跨层残差分解与token自适应路由的新颖组合，对现有统一压缩范式有显著改进，信息增量较高。
方法描述清晰，实验覆盖主流基准，结果量化充分，图表详实，论证严谨。
提供了融合CUDA实现，内存节省和吞吐提升显著，可直接部署于现有LLM推理框架。
长上下文推理是当前热点，KV缓存压缩需求迫切，论文发布于arXiv 2025年7月，时效性极高。
来源于arXiv预印本社区，非顶级会议收录，但作者单位和实验细节可查，方法可复现。
Base 87 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper presents innovative methodology, thorough experiments, and practical CUDA implementation with high engineering value for long-context inference, though generalizability may depend on specific architectures.
AI commentary
「This paper tackles the most painful KV cache memory issue in long-context LLM inference. Its highlight is moving away from one-size-fits-all compression and letting the model decide which tokens need more detail—a smart and practical token-adaptive approach. For engineers working on model deployment and inference optimization, the 8.3x memory reduction and calibration-free feature mean it can be directly integrated into production; the open-source implementation is worth watching.」
Quick overview
Long-context language model inference is increasingly limited by the memory bandwidth and capacity required to store key-value caches, yet existing compression methods often apply uniform budgets across layers or tokens and degrade retrieval when lexical cues and semantic states require different preservation. We introduce DepthWeave-KV, a token-adaptive cache compression method that factorizes key and value states across neighboring transformer layers using shared low-rank channel bases while retaining lightweight token-specific residuals where attention behavior is sensitive. DepthWeave-KV combines cross-depth residual factorization with a token-conditional depth router that allocates higher reconstruction rank to instruction-bearing and retrieval-critical tokens, and uses calibration-free online error tracking from attention-output probes to adapt compression during generation without retraining the base model. A fused CUDA implementation jointly performs basis lookup, residual dequantization, and attention projection to reduce decode-time memory traffic. Across LongBench, Needle-in-a-Haystack, L-Eval, and long-form QA and summarization benchmarks, DepthWeave-KV achieves near-full-cache task quality with substantially lower memory use, improving average score and retrieval accuracy over prior compressed caches while reaching 8.3x KV memory reduction and 72.8 tokens per second at 64K context.
- Proposes DepthWeave-KV, which adaptively compresses KV cache via cross-layer shared low-rank basis decomposition and token-specific residuals, avoiding retrieval degradation from uniform budgets.
- Introduces a token-conditional depth router and online error tracking via attention-output probes for calibration-free dynamic compression without retraining.
- A fused CUDA implementation jointly performs basis lookup, residual dequantization, and attention projection to significantly reduce decode-time memory traffic.
- Achieves near-full-cache task quality on long-context benchmarks, with 8.3x KV memory reduction and 72.8 tokens/s throughput at 64K context.
Full text
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