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Akashic: Efficient LLM Inference with MemAttention for Long Contexts

Akashic is a low-overhead LLM inference service designed to handle long multi-turn interactions by avoiding full history replay. It uses MemAttention to improve serving efficiency and output quality by focusing on task-relevant context.

Topic: LLM Inference Source: arXiv · arxiv.org Published 2026-07-07 00:06 UTC Fetched 2026-07-08 05:20 UTC

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Long context accumulation in LLM agents increases computational cost and can degrade output quality by mixing relevant and irrelevant information. Akashic addresses these challenges, enabling more efficient and effective LLM inference in complex workflows.

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Score 75 Source Type arxiv Reposts 0 Topic Quality 58

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