Large Language Models Do Not Always Need Readable Language

Large language models (LLMs) are commonly prompted and interfaced with human-readable natural language, even when the intended reader is another model. This paper investigates whether semantic information can be encoded in compact, non-standard textual forms that sacrifice human readability while remaining recoverable by LLMs. We refer to this class of model-centric textual representations as BabelTele, approached here not as a fixed protocol but as an empirical probe into LLMs’ capacity to generate and interpret such representations. Through readability diagnostics, model likelihood measures, human questionnaires, and downstream task evaluations, we find that BabelTele can substantially depart from ordinary natural language while preserving core semantics for instruction-tuned LLMs. As a task-agnostic representational paradigm, BabelTele demonstrates high information density, maintaining 99.5% semantic fidelity even when the text volume is condensed to 27.9% of its original length. We further evaluate its semantic robustness in cross-model transfer, agent memory, and multi-agent communication.

Introduction. Large language models (LLMs) have become a dominant interface in contemporary intelligent systems. Since GPT-3 demonstrated strong fewshot generalization through text-based prompting (Brown et al., 2020), the field has largely followed a unified paradigm: knowledge is represented in natural language, instructions are issued in natural language, and model outputs are returned in natural language. Subsequent alignment methods and dialogue-oriented models further reinforced this design, optimizing model behavior toward controllability, and readability (Ouyang et al., 2022; Touvron et al., 2023). However, natural language optimized for human communication is not necessarily an efficient representation for model processing. Human language contains substantial redundancy: complete syntax, discourse markers, and narrative coherence all help people follow, remember, and disambiguate information. These properties are valuable for human readers, but they reduce semantic density.

Discussion / Conclusion. This paper investigates BabelTele, a high-density textual representation optimized for model decodability rather than human readability. Our experiments demonstrate that BabelTele achieves strong compression ratios with negligible performance degradation, while remaining semantically recoverable by LLMs. Notably, this capability generalizes across a diverse set of proprietary and open-weight models in a zero-shot manner, suggesting that the ability to interpret such representations is a general capability of LLMs rather than an artifact of any particular model. In practical scenarios including multi-agent communication and agent memory, BabelTele shows promising potential as a modelnative intermediate representation. We therefore view BabelTele not as a finished protocol, but as evidence that high-density textual representations optimized for LLM-to-LLM communication need not prioritize human readability, and as a direction worth further exploration. Our current evaluation focuses on a selected set of benchmarks and model families, the behavior of BabelTele across a broader range of tasks and architectures remains to be explored.