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CoMem: Reusing Transformer Depth across Queries with Persistent Intermediate Residuals
HL

Hanzuo Liu, Xuan Qi, Chunyu Liu, Haotian Zhong, Yulong Wang, Key, Rayying, Alex Lamb, Mingyu Gao

· 1 min read

ResearcharXiv cs.CL

CoMem: Reusing Transformer Depth across Queries with Persistent Intermediate Residuals

arXiv:2607.28263v2 Announce Type: replace Abstract: Repeated queries over shared documents repeatedly execute the same lower transformer layers. We introduce CoMem, which makes split depth j an explicit reusable-context axis: write one depth-j residual per token, select a bounded chunk set, and resume only layers [j:L). Among document-reuse systems we are aware of, CoMem jointly makes split depth a tunable serving axis and isolates it with a matched j=0 endpoint. On Qwen3-8B, j=12 reduces selected-pack Read from 931.9 to 664.4 ms (1.403x), with a 3.12-point RULER cost (95% CI [2.36, 3.93]); a continuous-prefix oracle recovers the full gap. The resulting depth axis quantifies a quality-latency-storage trade-off; a separate same-adapter, Write-inclusive pipeline is 2.74x faster. Equal-latency raw replay leads by 11.56 points with BM25, directly measuring an applicability boundary of prepaid depth rather than hiding it. CoMem stores 8 KiB/token versus 144 KiB/token for a protocol-aligned same-Qwen3 CacheBlend-style diagnostic; the cohorts and adaptation budgets are not matched. A context-position factorization identifies missing lower-layer document context as the dominant tested multikey error, and a 32-token overlap raises 92.5 to 98.5 without increasing persistent bytes or per-query Read. CoMem opens transformer depth as a measurable, tunable dimension for repeated-query long-context serving.

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This story was published by arXiv cs.CL and written by Hanzuo Liu, Xuan Qi, Chunyu Liu, Haotian Zhong, Yulong Wang, Key, Rayying, Alex Lamb, Mingyu Gao. SyncAI.news shows a preview; the complete article is on the publisher's site.

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