A unified learnable KV eviction policy with cross-layer calibration reduces memory and matches or exceeds full-cache performance on long-context tasks by retaining useful tokens and limiting attention dilution.
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5 Pith papers cite this work. Polarity classification is still indexing.
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2026 5representative citing papers
ReST-KV formulates KV eviction as layer-wise output reconstruction optimization with spatial-temporal smoothing, outperforming baselines by 2.58% on LongBench and 15.2% on RULER while cutting decoding latency by 10.61x at 128k context.
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.
LaProx reformulates KV cache eviction as an output-aware matrix approximation, enabling a unified global token selection strategy that preserves LLM performance at 5% cache size across long-context benchmarks.
HeteroCache dynamically allocates KV cache space to attention heads based on their temporal stability and uses hierarchical asynchronous retrieval to achieve state-of-the-art long-context performance with up to 3x faster decoding at 224K context length.
citing papers explorer
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Make Each Token Count: Towards Improving Long-Context Performance with KV Cache Eviction
A unified learnable KV eviction policy with cross-layer calibration reduces memory and matches or exceeds full-cache performance on long-context tasks by retaining useful tokens and limiting attention dilution.
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ReST-KV: Robust KV Cache Eviction with Layer-wise Output Reconstruction and Spatial-Temporal Smoothing
ReST-KV formulates KV eviction as layer-wise output reconstruction optimization with spatial-temporal smoothing, outperforming baselines by 2.58% on LongBench and 15.2% on RULER while cutting decoding latency by 10.61x at 128k context.
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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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Reformulating KV Cache Eviction Problem for Long-Context LLM Inference
LaProx reformulates KV cache eviction as an output-aware matrix approximation, enabling a unified global token selection strategy that preserves LLM performance at 5% cache size across long-context benchmarks.
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HeteroCache: A Dynamic Retrieval Approach to Heterogeneous KV Cache Compression for Long-Context LLM Inference
HeteroCache dynamically allocates KV cache space to attention heads based on their temporal stability and uses hierarchical asynchronous retrieval to achieve state-of-the-art long-context performance with up to 3x faster decoding at 224K context length.