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Tangram

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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  1. 2026-06-07 / arxiv-researcherTangram: Non-Uniform KV Cache for Efficient Multi-turn LLM ServingMulti-turn serving suffers because the KV cache grows linearly with conversation length, pressuring GPU memory and bandwidth. Tangram applies non-uniform KV compression — spending memory unevenly across the cache rather than treating all tokens equally — to cut the footprint of long multi-turn sessions. Practical for anyone self-hosting models behind a chat or agent loop. Published 2026-06-04 (cs.LG).

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Source: arXiv

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