VeriCache turns lossy KV cache compression into lossless LLM inference by drafting with compressed cache and verifying drafts with full cache, achieving up to 4x throughput with identical outputs.
KVTuner: Sensitivity-aware layer-wise mixed-precision KV cache quantization for efficient and nearly lossless LLM inference
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RDKV derives per-token and per-channel weights from attention distortion, then uses reverse water-filling to assign bit-widths from full precision to zero after prefilling, recovering 97.81% accuracy with 2.48% cache retention on LongBench.
HeadQ applies score-space logit corrections for keys and attention-weighted surrogates for values to KV-cache quantization, removing 84-94% of excess perplexity in 2-bit key experiments across six models.
VECTOR augments eviction-based KV cache compression with three-way token routing that combines importance scoring and offline regression-based reconstructability estimation to improve quality at high compression ratios.
citing papers explorer
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VeriCache: Turning Lossy KV Cache into Lossless LLM Inference
VeriCache turns lossy KV cache compression into lossless LLM inference by drafting with compressed cache and verifying drafts with full cache, achieving up to 4x throughput with identical outputs.
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RDKV: Rate-Distortion Bit Allocation for Joint Eviction and Quantization of the KV Cache
RDKV derives per-token and per-channel weights from attention distortion, then uses reverse water-filling to assign bit-widths from full precision to zero after prefilling, recovering 97.81% accuracy with 2.48% cache retention on LongBench.
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HeadQ: Model-Visible Distortion and Score-Space Correction for KV-Cache Quantization
HeadQ applies score-space logit corrections for keys and attention-weighted surrogates for values to KV-cache quantization, removing 84-94% of excess perplexity in 2-bit key experiments across six models.
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A Simple Plug-in for Improving Eviction-Based KV Cache Compression
VECTOR augments eviction-based KV cache compression with three-way token routing that combines importance scoring and offline regression-based reconstructability estimation to improve quality at high compression ratios.