According to @godofprompt, researchers have developed a novel Cache-to-Cache (C2C) method allowing large language models (LLMs) to communicate directly via their internal key-value (KV) caches, eliminating the need for text-based exchanges. This approach delivers an 8.5-10.5% accuracy improvement and doubles processing speed, with zero token waste (source: @godofprompt, https://x.com/godofprompt/status/2012462714657132595). The practical implications for AI industry applications are significant, enabling more efficient multi-agent systems, reducing computational costs, and opening new business opportunities in real-time AI communication platforms, collaborative AI agents, and autonomous decision-making systems. This breakthrough sets a new benchmark in AI model interoperability and workflow efficiency.

According to @godofprompt on Twitter, researchers have introduced Cache-to-Cache (C2C) technology, enabling large language models (LLMs) to communicate directly through their key-value caches (KV-Caches) without generating intermediate text. This method results in an 8.5-10.5% accuracy increase, operates twice as fast, and eliminates token waste, marking a significant leap in AI efficiency and scalability. The C2C approach has major business implications, such as reducing computational costs and accelerating multi-agent AI workflows, paving the way for more practical and cost-effective enterprise AI solutions (source: @godofprompt, Jan 17, 2026).