When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning

2026-06-28

AI Summary

Traditional chain-of-thought (CoT) prompting generates verbose reasoning traces in complex tasks, struggling to balance efficiency and accuracy. This work proposes the Communicative Language Symbolism Routing (CLSR) framework, which treats linguistic symbol systems as reusable symbolic protocols. During inference, multiple large language model agents autonomously invent, evolve, and share compact symbolic languages, dynamically composed via a latent-variable-free router to trade off accuracy against computational cost. Integrating multi-agent collaboration, evolutionary optimization, and information-theoretic analysis, CLSR reduces reasoning token consumption by 3–6× across multiple benchmarks while matching standard CoT accuracy. The approach further establishes a theoretical connection between emergent symbolic protocols and program execution pipelines.

Abstract

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 contract, and improves it through an evolutionary loop driven by correctness and token cost. At inference time, the router may invoke a single low-cost LSF call, ensemble multiple LSFs, or execute a multi-round LSF composition protocol on harder queries. Across challenging benchmarks, CLSR reduces latency-oriented generated token completion by $3\sim 6\times$ compared to standard CoT while maintaining accuracy. We further derive an information-theoretic lower bound on token cost under arbitrary symbolism and show that, under an interpreter-realizability premise, multi-round LSF protocols conditionally subsume program-execution pipelines. Code is publicly available (https://github.com/pzqpzq/LSF_MDia).

Problem

• Large Language Models
• Chain-of-Thought
• Efficient Reasoning
• Symbolic Communication
• Token Efficiency

Innovation

• Language Symbolism Framework
• Multi-Agent Communication
• Token-Efficient Reasoning
• Evolutionary Symbol Protocol
• Latent-Free Routing