Cache-to-Cache: Direct Semantic Communication Between Large Language Models
In existing designs, LLMs communicate through text, forcing internal representations to be transformed into output token sequences. This process both loses rich semantic information and incurs token-by-token generation latency. Motivated by these limitations, we ask: Can LLMs communicate beyond text?
- ▪In existing designs, LLMs communicate through text, forcing internal representations to be transformed into output token sequences.
- ▪This process both loses rich semantic information and incurs token-by-token generation latency.
- ▪Motivated by these limitations, we ask: Can LLMs communicate beyond text?
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Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2510.03215 |
| Publication time | Fri, 18 Sep 2026 18:55:35 +0000 |
| Retrieval time | 2026-09-18T19:33:45.484Z |
| Last seen | 2026-09-18T19:33:45.484Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 0F56umJIyY-k · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Computation and Language arXiv:2510.03215 (cs) [Submitted on 3 Oct 2025 (v1), last revised 2 Mar 2026 (this version, v2)] Title:Cache-to-Cache: Direct Semantic Communication Between Large Language Models Authors:Tianyu Fu, Zihan Min, Hanling Zhang, Jichao Yan, Guohao Dai, Wanli Ouyang, Yu Wang View a PDF of the paper titled Cache-to-Cache: Direct Semantic Communication Between Large Language Models, by Tianyu Fu and 6 other authors View PDF HTML (experimental) Abstract:Multi-LLM systems harness the complementary strengths of diverse Large Language Models, achieving performance and efficiency gains that are not attainable by a single model. In existing designs, LLMs communicate through text, forcing internal representations to be transformed into output token sequences.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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