BabelTele: When LLMs Speak in Code Humans Can't Read

Paper: Large Language Models Do Not Always Need Readable Language (2606.19857)
Published: 18 Jun 2026.

Learn more on Emergent Mind: https://www.emergentmind.com/papers/2606.19857
arXiv: https://arxiv.org/abs/2606.19857
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This presentation explores a radical departure from human-readable prompts and agent communication. The paper demonstrates that large language models can compress information into dense, opaque representations—mixing symbols, abbreviations, and multilingual tokens—that other models decode perfectly while humans struggle. We examine BabelTele's compression efficiency, cross-model portability, and practical gains in multi-agent systems and memory-constrained contexts, revealing a future where model-to-model communication abandons readability for raw semantic density.

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