XKV讓模型傳緩存

Computer Science > Artificial Intelligence arXiv:2608.

20617 (cs) [Submitted on 20 Aug 2026] Title:Dual-Cache Latent Space Communication between Heterogeneous Language Models Authors:Jiyao Liu, Qi Zhang, Yaoyi Jia, Ziwen Kan, Song Wang View a PDF of the paper titled Dual-Cache Latent Space Communication between Heterogeneous Language Models, by Jiyao Liu and 4 other authors View PDF HTML (experimental) Abstract:Multi-agent LLM systems split work across models, so answering often requires knowledge that sits in another agent's context: a Sharer has encoded information that a Receiver needs to complete its task.

They usually communicate by exchanging text, which puts autoregressive decoding on the critical path and reduces the exchange to a discrete message written without sight of the receiver's state.

Recent latent protocols instead translate the sharer's key-value (KV) cache into the receiver's: C2C supports heterogeneous models but requires both to read the same input, while LCF-X removes this shared-context requirement through position-free sharer-cache pooling.

Three restrictions remain: LCF-X compresses the sharer alone, supplies the same layer-local summary to every receiver position with no joint cross-layer memory to retrieve from, and assumes matched layer count and KV geometry.

We introduce XKV, which lifts all three: learned-query attention pools both caches; self-attention over receiver-aligned layer tokens, with a learned layer map reconciling different depths, mixes the pooled summaries into a compact joint memory; and a shared position decoder lets every raw receiver cache position retrieve its own per-head-gated residual in the receiver's native KV geometry.

Both models stay frozen and may differ in family, depth, KV-head count, head dimension, and tokenizer; only the translator is trained.

Across 45 dataset-model-pair settings (six heterogeneous and three same-model ordered pairings, five datasets), XKV attains the highest macro score and best average rank, improving on LCF-X on every dataset (by 4.6 exact-match and 4.

2 F1 points on ROPES) and surpassing text communication on four of the five, while training 76% fewer parameters and translating a cache pair 10.3x faster (5.8 vs.59.9 ms); end to end, XKV is 26% faster than LCF-X and 6.8x faster than text communication.Subjects: Artificial Intelligence (cs.

AI); Machine Learning (cs.LG) Cite as: arXiv:2608.20617 [cs.AI] (or arXiv:2608.20617v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.

20617 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Qi Zhang [view email] [v1] Thu, 20 Aug 2026 23:35:36 UTC (244 KB) Full-text links: Access Paper: View a PDF of the paper titled Dual-Cache Latent Space Communication between Heterogeneous Language Models, by Jiyao Liu and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.

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