MomentKV maintains count, key mean, value mean, and value-key covariance over evicted tokens to guide selective eviction and provide a first-order approximation of their attention contribution, outperforming baselines on LongBench and RULER.
Caote: Kv cache selection for llms via attention output error-based token eviction.arXiv preprint arXiv:2504.14051
5 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
A fixed-contract probe shows value-aware KV eviction recovers needed evidence in 72.6% of accuracy-improving cases on LongBench but only 32.4% otherwise, suggesting an order of recover evidence, rank value, then preserve couplings.
ConFu boosts speculative decoding acceptance rates 8-20% over EAGLE-3 by letting draft models use contemplate tokens and MoE to anticipate future generation direction.
citing papers explorer
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MomentKV: Closing the Directional Gap in KV Cache Eviction for Long-Context Inference
MomentKV maintains count, key mean, value mean, and value-key covariance over evicted tokens to guide selective eviction and provide a first-order approximation of their attention contribution, outperforming baselines on LongBench and RULER.
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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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When Does Value-Aware KV Eviction Help? A Fixed-Contract Diagnostic for Non-Monotone Cache Compression
A fixed-contract probe shows value-aware KV eviction recovers needed evidence in 72.6% of accuracy-improving cases on LongBench but only 32.4% otherwise, suggesting an order of recover evidence, rank value, then preserve couplings.
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ConFu: Contemplate the Future for Better Speculative Sampling
ConFu boosts speculative decoding acceptance rates 8-20% over EAGLE-3 by letting draft models use contemplate tokens and MoE to anticipate future generation direction.