Notation Matters: A Benchmark Study of Token-Optimized Formats in Agentic AI Systems
Large language models in Agentic AI systems consume tool schemas and execution results and emit tool invocations as structured data. The default language for that exchange, JSON, was designed for application-to-application interchange rather than token efficiency, so its structural elements impose substantial token overhead. Recent work proposes token-optimized alternatives such as TOON (Token-Oriented Object Notation) and TRON (Token Reduced Object Notation) as more compact replacements, but these formats have been evaluated only on isolated comprehension or generation tasks. Whether their token reductions hold inside end-to-end agentic loops therefore remains an open question. We evalua...
| Authors | Lorenz Kutschka, Bernhard Geiger |
|---|---|
| Primary category | cs.AI |
| Published | 28 May 2026 |
| Last updated at source | 17 June 2026 |
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