For me the really interesting part was:
> Haven’t we seen LLMs do this already?
> Yes, BabelTele (arXiv, June 2026) demonstrated that LLMs can encode text in compact, non-standard forms — omnilingual word fragments, symbols, emoji — that other models recover with high fidelity (99.5% semantic fidelity at 27.9% of original length, by their metrics), including cross-model transfer, agent memory, and multi-agent communication. It proves the general phenomenon: human readability is not a requirement for model-to-model text.
I remember people testing early GPT-4 (2023?) in similar ways, to compress text, it would emit a string of strange text, Unicode, emojis, but was able to decode the compressed version very reliably.
This seems to cut usage by another ~50%, at the cost of being incomprehensible to humans.
> Haven’t we seen LLMs do this already?
> Yes, BabelTele (arXiv, June 2026) demonstrated that LLMs can encode text in compact, non-standard forms — omnilingual word fragments, symbols, emoji — that other models recover with high fidelity (99.5% semantic fidelity at 27.9% of original length, by their metrics), including cross-model transfer, agent memory, and multi-agent communication. It proves the general phenomenon: human readability is not a requirement for model-to-model text.
I remember people testing early GPT-4 (2023?) in similar ways, to compress text, it would emit a string of strange text, Unicode, emojis, but was able to decode the compressed version very reliably.
This seems to cut usage by another ~50%, at the cost of being incomprehensible to humans.