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

Pei, Zhengqi et al.
arXiv (All) · Papers
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neural-and-evolutionary-computing
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 multipl
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