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

2026-01-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

KV Packet is a novel, recomputation-free framework designed to optimize Key-Value (KV) caching in Large Language Models (LLMs) for Retrieval-Augmented Generation (RAG) systems. Standard KV caches are context-dependent, requiring expensive recomputation when documents are reused in new contexts, leading to increased Time-to-First-Token (TTFT) latency and computational overhead. KV Packet addresses this by treating cached documents as immutable "packets" wrapped in lightweight, trainable soft-token adapters (Headers and Trailers). These adapters are trained via self-supervised distillation to bridge context discontinuities without modifying the base LLM parameters or requiring inference-time recomputation. Experiments on Llama-3.1 and Qwen2.5 models demonstrate that KV Packet achieves near-zero FLOPs and lower TTFT compared to recomputation-based baselines like CacheBlend and EPIC, while maintaining F1 scores comparable to full recomputation. It also seamlessly integrates with existing KV compression techniques.

Key takeaway

For MLOps Engineers deploying LLMs in RAG systems, KV Packet offers a significant reduction in inference-time computational overhead and Time-to-First-Token (TTFT) latency. By adopting this recomputation-free framework, your teams can achieve high generation quality comparable to full recomputation baselines, while also gaining seamless compatibility with KV compression techniques, which is critical for efficient resource utilization. Consider implementing KV Packet to optimize your LLM serving infrastructure.

Key insights

KV Packet enables recomputation-free, context-independent KV caching for LLMs using trainable soft-token adapters.

Principles

• Boundary artifacts disrupt attention in naive KV cache concatenation.
• Self-supervised distillation can align adapter behavior to full-context models.
• Universal adapters generalize across diverse document domains.

Method

KV Packet wraps frozen document KV caches with trainable Header and Trailer soft-token adapters. These adapters are optimized via self-supervised distillation, minimizing KL divergence between full-context and packet-based model output distributions.

In practice

• Use KV Packet for RAG to reduce LLM inference latency.
• Train universal adapters on diverse datasets for broad applicability.
• Integrate KV Packet with off-the-shelf KV compression methods.

Topics

• KV Packet
• LLM KV Caching
• Retrieval-Augmented Generation
• Soft-token Adapters
• Self-supervised Distillation

Best for: MLOps Engineer, AI Engineer, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Architect

Related on AIssential

• KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs — KV Packet enables recomputation-free KV cache reuse for LLMs using soft-token adapters and self-supervised distillation.
• RedKnot: Efficient Long-Context LLM Serving with Head-Aware KV Reuse and SegPagedAttention — Head-aware KV cache management and segmented paging physically align with LLM sparsity, significantly boosting long-context serving efficiency and capacity.
• SpectrumKV: Per-Token Mixed-Precision KV Cache Transfer for Prefill-Decode Disaggregated LLM Serving — Prefill-decode KV cache transfer benefits from per-token mixed-precision allocation rather than binary pruning.
• KV Cache Explained Like You’re an LLM Engineer — KV cache makes LLM inference viable by storing past Key/Value tensors, avoiding redundant attention recomputation.
• Enabling KV Caching of Shared Prefix for Diffusion Language Models — DLM shared prefix KV caching is feasible by adapting reuse based on layer depth and prefix ratio.
• Meta-Soft: Leveraging Composable Meta-Tokens for Context-Preserving KV Cache Compression — Dynamic, composable meta-tokens can compress LLM KV caches while preserving context, outperforming static eviction.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.