ISON isn't inventing new syntax. It's CSV/TSV with a header - which LLMs have seen billions of times. The table format:

table.users id name email 1 Alice alice@example.com

...is structurally identical to markdown tables and CSVs that dominate training corpora.

On the "3x translation overhead" - ISON isn't meant for LLM-to-code interfaces where you need JSON for an API call. It's for context stuffing: RAG results, memory retrieval, multi-agent state passing.

If I'm injecting 50 user records into context for an LLM to reason over, I never convert back to JSON. The LLM reads ISON directly, reasons over it, and responds.

The benchmark: same data, same prompt, same task. ISON uses fewer tokens and gets equivalent accuracy. Happy to share the test cases if you want to verify.

The 20% ISON adds: - Multiple named tables in one doc - Cross-table references - No escaping hell (quoted strings handled cleanly) - Schema validation (ISONantic)

If you're stuffing one flat table into context, CSV works fine. When you have users + orders + products with relationships, ISON saves you from JSON's bracket tax.

1. Multiple tables in one document (table.users, table.orders) 2. References between tables (:user:42 links to id 42) 3. Object blocks for config/metadata 4. Streaming format (ISONL) for large datasets

The type annotations are optional - they help LLMs understand the schema without inference.

You could think of it as "CSV that knows about relationships" - which is exactly what multi-agent systems need when passing state around.