HACK++ is a head-aware KV cache compression framework for VAR models that decouples current-scale attention from historical cache under adaptive per-head budgets to achieve near-lossless generation at 30% attention and 10% cache budgets.
Zipcache: Accurate and efficient kv cache quantization with salient token identification
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 6representative citing papers
Spherical KV combines angle-domain attention using spherical key codes with rate-distortion retention to cut KV cache residency and HBM traffic while keeping a paged, fusion-friendly decode path.
GSRQ applies a gain-shape variant of K-means inside residual quantization to improve directional fidelity, raising LongBench accuracy from 11.34 to 33.54 at 1-bit on LLaMA-3-8B.
IndexMem proposes a learned KV importance predictor paired with a latent memory module to enable bounded KV cache size for long-context inference, reporting gains on RULER, Needle-in-a-Haystack, and LongBench across multiple LLMs.
Introduces NPAS and AV Filter using LLM attention weights to defend RAG against poisoning, reporting up to 20% accuracy gains while adaptive attacks reach 35% success.
Meta-Soft dynamically synthesizes targeted soft tokens from a learnable meta-library using Gumbel-Softmax and applies attention-flow integration to compress KV cache while attempting to preserve evicted context information.
citing papers explorer
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HACK++: Towards More Effective Head-Aware Key-Value Compression for Efficient Visual Autoregressive Modeling
HACK++ is a head-aware KV cache compression framework for VAR models that decouples current-scale attention from historical cache under adaptive per-head budgets to achieve near-lossless generation at 30% attention and 10% cache budgets.
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SPHERICAL KV: Angle-Domain Attention and Rate-Distortion Retention for Efficient Long-Context Inference
Spherical KV combines angle-domain attention using spherical key codes with rate-distortion retention to cut KV cache residency and HBM traffic while keeping a paged, fusion-friendly decode path.
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GSRQ: Gain-Shape Residual Quantization for Sub-1-bit KV Cache
GSRQ applies a gain-shape variant of K-means inside residual quantization to improve directional fidelity, raising LongBench accuracy from 11.34 to 33.54 at 1-bit on LLaMA-3-8B.
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IndexMem: Learned KV-Cache Eviction with Latent Memory for Long-Context LLM Inference
IndexMem proposes a learned KV importance predictor paired with a latent memory module to enable bounded KV cache size for long-context inference, reporting gains on RULER, Needle-in-a-Haystack, and LongBench across multiple LLMs.
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Through the Stealth Lens: Attention-Aware Defenses Against Poisoning in RAG
Introduces NPAS and AV Filter using LLM attention weights to defend RAG against poisoning, reporting up to 20% accuracy gains while adaptive attacks reach 35% success.
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Meta-Soft: Leveraging Composable Meta-Tokens for Context-Preserving KV Cache Compression
Meta-Soft dynamically synthesizes targeted soft tokens from a learnable meta-library using Gumbel-Softmax and applies attention-flow integration to compress KV cache while attempting to preserve evicted context information.