newsReddit r/MachineLearningTrust 52 · CommunityPublished 13d agoLive · 12d ago
Is KV Cache in a high dimensional vector space? [D]
I've been doing some research on this question: At inference time a large part of a model's working memory lives in the KV cache, plus whatever external memory the harness bolts on. I've been poking at the storage-and-retrieval side of this, treating that cache as an index, and what stands out is that it isn't a flat list. It's a structured set of vectors with a navigable geometry, since the keys carry the model's learned sense of what relates to what.
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 60%A JoLT for the KV Cache: Near-Lossless KV Cache Compression via Joint Tucker and JL-Residual Allocation for LLMs →
- PossiblePossibly related (embedding) · 56%FreqDepthKV: Frequency-Guided Depth Sharing for Robust KV Cache Compression in Long-Context LLM Inference →
- PossiblePossibly related (embedding) · 53%xcena-dev/maru →
- PossiblePossibly related (embedding) · 52%GSRQ: Gain-Shape Residual Quantization for Sub-1-bit KV Cache →
- PossiblePossibly related (embedding) · 50%pythongiant/KVBoost →
Covers
paperA JoLT for the KV Cache: Near-Lossless KV Cache Compression via Joint Tucker and JL-Residual Allocation for LLMspaperFreqDepthKV: Frequency-Guided Depth Sharing for Robust KV Cache Compression in Long-Context LLM Inferencerepoxcena-dev/marupaperGSRQ: Gain-Shape Residual Quantization for Sub-1-bit KV Cacherepopythongiant/KVBoost
Related across the graph
paperA JoLT for the KV Cache: Near-Lossless KV Cache Compression via Joint Tucker and JL-Residual Allocation for LLMspaperFreqDepthKV: Frequency-Guided Depth Sharing for Robust KV Cache Compression in Long-Context LLM InferencepaperGSRQ: Gain-Shape Residual Quantization for Sub-1-bit KV Cacherepoxcena-dev/marurepopythongiant/KVBoost
