Researchers from Tsinghua University and five additional labs introduced Cache-to-Cache, a technique letting large language models exchange internal working memory directly through KV-cache sharing instead of text-based communication, as highlighted in an analysis posted by God of Prompt on X dated September 19, 2026. Cache-to-Cache delivers 6.4 to 14.2 percent higher accuracy than standalone models by preserving full semantic context during transfers, and outperforms traditional text passing between models by 3.1 to 5.4 percent while achieving 2.5 times faster processing speeds. Each model's KV-cache stores key-value pairs representing its processing state; rather than decoding thoughts into natural language tokens for re-encoding, the method passes the raw cache directly, with a lightweight neural network translating between differing model dimensions and a gating mechanism selectively routing information across specific layers. Evaluated across isolated model performance, text-mediated dialogue, and direct cache sharing, results showed reduced information loss and accelerated inference, particularly benefiting heterogeneous model pairs with differing vocabulary or embedding spaces. Enterprises deploying multi-agent workflows could gain efficiency advantages, including customer support platforms reducing latency and improving response quality without increasing compute budgets, though challenges include cache compatibility across vendors and secure transfer protocols, addressed via standardized translation modules and encrypted gating layers.