When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning
Chain-of-Thought (CoT) improves large language models (LLMs) on difficult reasoning tasks, but it often incurs long natural-language rationales that are poorly aligned with efficient machine reasoning. We propose Communicative Language Symbolism Routing (CLSR), a test-time framework in which multiple LLM agents autonomously invent, evolve, and share compact Language Symbolism Frameworks (LSFs), while a latent-free router adaptively selects and composes these languages per query to optimize the accuracy-token trade-off. Unlike prompt optimization that refines surface instructions, CLSR treats each LSF as a reusable symbolic protocol with compact symbols, usage rules, and a message-passing ...
| Authors | Zhengqi Pei, Qingming Huang, Shuhui Wang |
|---|---|
| Primary category | cs.AI |
| Published | 28 June 2026 |
| Last updated at source | 10 October 2026 |
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