KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs

• URL: http://arxiv.org/abs/2604.13226v2
• Date: Fri, 17 Apr 2026 02:14:01 GMT
• Title: KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs
• Abstract summary: Large Language Models rely heavily on Key-Value (KV) caching to minimize inference latency.<n>Existing solutions such as CacheBlend, EPIC, and SAM-KV mitigate this issue by selectively recomputing a subset of tokens.<n>We propose KV Packet, a recomputation-free cache reuse framework that treats cached documents as immutable packets'' wrapped in light-weight trainable soft-token adapters.
• Score: 11.980880525012056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) rely heavily on Key-Value (KV) caching to minimize inference latency. However, standard KV caches are context-dependent: reusing a cached document in a new context requires recomputing KV states to account for shifts in attention distribution. Existing solutions such as CacheBlend, EPIC, and SAM-KV mitigate this issue by selectively recomputing a subset of tokens; however, they still incur non-negligible computational overhead (FLOPs) and increased Time-to-First-Token (TTFT) latency. In this paper, we propose KV Packet, a recomputation-free cache reuse framework that treats cached documents as immutable ``packets'' wrapped in light-weight trainable soft-token adapters, which are trained via self-supervised distillation to bridge context discontinuities. Experiments on Llama-3.1 and Qwen2.5 demonstrate that the proposed KV Packet method achieves near-zero FLOPs and lower TTFT than recomputation-based baselines, while retaining F1 scores comparable to those of the full recomputation baseline.

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