Leyline adds a policy-directed KV cache edit primitive with closed-form RoPE correction for agentic inference, reporting +11.2 pp cache-hit lift and +14.3 pp solve-rate gain.
Compute or load kv cache? why not both?
6 Pith papers cite this work. Polarity classification is still indexing.
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CachePrune enables fine-grained, token-level KV cache reuse across LLM requests by masking sensitive segments, eliminating direct side-channel leakage while cutting TTFT by 4.5x and raising hit rates by 44% versus prior coarse-grained methods.
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.
SparKV reduces time-to-first-token by 1.3x-5.1x and energy use by 1.5x-3.3x for on-device LLM inference by adaptively choosing between cloud KV streaming and local computation while overlapping execution and adjusting for runtime conditions.
AsymCache combines Multi-Segment Attention, position-aware eviction, and adaptive chunking to cut TTFT by up to 2.03x and TPOT by up to 1.71x versus recent baselines in LLM serving.
The paper surveys human memory categories, maps them to LLM memory, and proposes a new three-dimension (object, form, time) categorization into eight quadrants to organize existing work and highlight open problems.
citing papers explorer
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Leyline: KV Cache Directives for Agentic Inference
Leyline adds a policy-directed KV cache edit primitive with closed-form RoPE correction for agentic inference, reporting +11.2 pp cache-hit lift and +14.3 pp solve-rate gain.
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CachePrune: Privacy-Aware and Fine-Grained KV Cache Sharing for Efficient LLM Inference
CachePrune enables fine-grained, token-level KV cache reuse across LLM requests by masking sensitive segments, eliminating direct side-channel leakage while cutting TTFT by 4.5x and raising hit rates by 44% versus prior coarse-grained methods.
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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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SparKV: Overhead-Aware KV Cache Loading for Efficient On-Device LLM Inference
SparKV reduces time-to-first-token by 1.3x-5.1x and energy use by 1.5x-3.3x for on-device LLM inference by adaptively choosing between cloud KV streaming and local computation while overlapping execution and adjusting for runtime conditions.
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Multi-Segment Attention: Enabling Efficient KV-Cache Management for Faster Large Language Model Serving
AsymCache combines Multi-Segment Attention, position-aware eviction, and adaptive chunking to cut TTFT by up to 2.03x and TPOT by up to 1.71x versus recent baselines in LLM serving.
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From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs
The paper surveys human memory categories, maps them to LLM memory, and proposes a new three-dimension (object, form, time) categorization into eight quadrants to organize existing work and highlight open problems.