arXiv Machine Learning October 7, 2026 By Hyesung Jeon, Hyeongju Ha, Jae-Joon Kim

PReCache: Efficient KV Cache Sharing for Multi-LoRA Agents via Low-Rank Precomputation and Neutral Reconstruction

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arXiv Machine Learning

Jun 2

LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents

arXiv:2602. 01053v2 Announce Type: replace Abstract: Role specialization in multi-LLM agent systems is often realized via multi-LoRA, where agents share a pretrained backbone and differ only by lightweight adapters.

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arXiv Machine Learning

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KVCMAS: Efficient KV cache Correction for Shared Context in Multi-Agent Systems

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arXiv AI

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arXiv AI

Jun 6

RedKnot: Efficient Long-Context LLM Serving with Head-Aware KV Reuse and SegPagedAttention

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arXiv AI

Oct 1

Prefill-Free Cross-Family KV Cache Transfer for Heterogeneous Multi-Agent LLMs

arXiv:2609.32259v2 Announce Type: replace Abstract: Recent multi-agent LLM systems increasingly combine heterogeneous models for specialized agent roles. However, text-based communication requires ea...

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arXiv Computation and Language

Aug 21

ReCache: Efficient KV Cache Reuse and Compression for Tool-Augmented LLM Agents

arXiv:2608. 19662v1 Announce Type: new Abstract: Agentic language models repeatedly encode tool and skill schemas that recur across requests in different combinations and orders, preventing standard prefix caching from reusing their key--value (KV) states.

By Yichu Fang, Sitong Wei, Haozhe Hu, Xiaoyu Shen

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LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents

KVCMAS: Efficient KV cache Correction for Shared Context in Multi-Agent Systems

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