[2603.03306] Token-Oriented Object Notation vs JSON: A Benchmark of Plain and Constrained Decoding Generation

Computer Science > Computation and Language arXiv:2603.03306 (cs) [Submitted on 8 Feb 2026] Title:Token-Oriented Object Notation vs JSON: A Benchmark of Plain and Constrained Decoding Generation Authors:Ivan Matveev View a PDF of the paper titled Token-Oriented Object Notation vs JSON: A Benchmark of Plain and Constrained Decoding Generation, by Ivan Matveev View PDF HTML (experimental) Abstract:Recently presented Token-Oriented Object Notation (TOON) aims to replace JSON as a serialization format for passing structured data to LLMs with significantly reduced token usage. While showing solid accuracy in LLM comprehension, there is a lack of tests against JSON generation. Though never present in training data, TOON syntax is simple enough to suggest one-shot in-context learning could support accurate generation. The inevitable prompt overhead can be an acceptable trade-off for shorter completions. To test this, we conducted a benchmark creating several test cases with regard to structural complexity, a validation pipeline, and comparing plain JSON generation vs structured output (via constrained decoding) JSON generation vs TOON one-shot in-context learning generation. JSON structured output was included to establish a minimum token budget baseline and to set a starting point for future experiments testing TOON constrained decoding inference enforcement. Key findings: TOON shows promising accuracy/token consumption ratio for in-domain generation tasks, though this advantage...