When LLMs process structured data, the serialization format directly affects cost and context utilization. Standard JSON wastes tokens repeating key names in every row of a tabular array--overhead that scales linearly with row count. This paper presents JTON (JSON Tabular Object Notation), a strict JSON superset whose main idea, Zen Grid, factors column headers into a single row and encodes values with semicolons, preserving JSON's type system while cutting redundancy. Across seven real-world domains, Zen Grid reduces token counts by 15-60% versus JSON compact (28.5% average; 32% with bare_strings). Comprehension tests on 10 LLMs show a net +0.3 pp accuracy gain over JSON: four models improve, three hold steady, and three dip slightly. Generation tests on 12 LLMs yield 100% syntactic validity in both few-shot and zero-shot settings. A Rust/PyO3 reference implementation adds SIMD-accelerated parsing at 1.4x the speed of Python's json module. Code, a 683-vector test suite, and all experimental data are publicly available.
JTON：一种面向大语言模型的、具有Zen Grid表格编码的令牌高效JSON超集 / JTON: A Token-Efficient JSON Superset with Zen Grid Tabular Encoding for Large Language Models
这篇论文提出了一种名为JTON的新数据格式，它通过一种叫Zen Grid的表格编码方法，在完全兼容JSON的同时，大幅减少了LLM处理表格数据时所需的令牌数量，从而降低了使用成本并提升了部分模型的性能。
源自 arXiv: 2604.05865
