Large Language Models Do Not Always Need Readable Language

Key points

It first proposes the concept of BabelTele, a compressed representation that preserves meaning without human readability, retaining 99.5% of meaning at only 27.9% of the original length.
It is zero-shot compatible across heterogeneous models such as Gemini, GPT, Claude, Qwen, and DeepSeek, so one model can decode text compressed by another without fine-tuning.
In multi-agent communication, it saves 39% to 44% of tokens while preserving task scores between 96.6% and 99.7%.
On the LoCoMo agent memory benchmark, it achieves higher accuracy than Summary, 96.48% versus 94.20%, while using fewer tokens.
On LongBench v2 code repository QA, BabelTele compression outperforms simple truncation when the context window is exceeded, improving Qwen3.6-Max from 55.17% to 62.07%.
BabelTele interpretability is not monotonic with model size, so robustness to the compression format is the key variable.

External research summaries. These are not HDATF publications or measured product results.