arXiv Machine Learning
SlimKV: Joint Token-Feature KV Cache Compression with Reconstruction-Free Beacon Attention
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arXiv AI
1d ago
AttSVD:Prompt-Adaptive Low-Rank KV Cache Compression via Attention-Guided SVD
arXiv:2610.06927v1 Announce Type: cross Abstract: The key-value (KV) cache of autoregressive transformers grows linearly with context length and dominates memory at long context. Most training-free r...
arXiv Machine Learning
Aug 26
PuzzleKV: Page-Wise Low-Rank Decomposition for KV Cache Compression
arXiv:2608.23843v1 Announce Type: new Abstract: Long-context inference in large language models (LLMs) is increasingly limited by the memory required for the key-value (KV) cache. KV cache compressio...
arXiv AI
Jul 8
FreqDepthKV: Frequency-Guided Depth Sharing for Robust KV Cache Compression in Long-Context LLM Inference
arXiv:2607. 06519v1 Announce Type: new Abstract: Long-context LLM inference is increasingly limited by the memory and bandwidth cost of KV caches, yet aggressive compression can remove the layer-specific evidence needed for retrieval and multi-step reasoning.
arXiv Machine Learning
Jul 20
VarRate: Training-Free Variable-Rate KV Cache Compression for Long-Context LLMs
arXiv:2607. 15498v1 Announce Type: cross Abstract: The key-value (KV) cache is the main memory bottleneck in long-context large language model (LLM) inference.
arXiv AI
3d ago
iS-KV: Online Low-Rank KV Cache Compression via Block-Incremental SVD
The paper introduces iS-KV, an online low‑rank KV‑cache compression technique that uses block‑incremental SVD to manage memory during long‑horizon autoregressive decoding. Unlike token‑eviction methods, iS‑KV retains all positions in a compact representation by keeping a recent window exact and incrementally folding older states into bounded‑rank bases, synchronizing coordinates as the basis evolves. Experiments on DeepSeek‑R1‑Distill‑Llama‑8B and Qwen3‑8B show that iS‑KV achieves high accuracy (82.6% and 89.2% respectively) while providing 4.06‑fold and 5.64‑fold compression, outperforming token‑eviction baselines under matched memory budgets.
arXiv Machine Learning
Sep 25
A JoLT for the KV cache: Near-Lossless KV Cache Compression via Joint Rank-bit Allocation
The paper introduces JoLT, a training‑free compressor that jointly allocates rank and precision for key‑value (KV) cache compression in long‑context language models. JoLT treats grouped prefill caches as fourth‑order tensors, applies partial Tucker decomposition along token and feature modes, and uses a rotated low‑bit quantizer for residuals, all governed by a single Lagrangian dual under a global byte constraint. Across five models from four architecture families, JoLT achieves 2–3× compression with less than 0.2% perplexity loss, and near‑lossless retrieval accuracy on LLaMA‑3.1‑8B at 64K context up to 3× compression.