Yes - I ran a 300 Questions benchmark comparing ISON vs JSON vs JSON-COMPACT etc on the same tasks.

ISON: 88.3% accuracy JSON: lower (can share exact numbers if interested)

Tested across Claude, GPT-4, DeepSeek, and Llama 3.

The key finding: LLMs handle tabular formats natively because they've seen billions of markdown tables and CSVs in training. No special prompting needed.

For associations, I tested with multi-table ISON docs like:

table.users id name 1 Alice 2 Bob

table.orders id user_id product 101 :1 Widget 102 :2 Gadget

Prompt: "What did Alice order?"

All models correctly resolved :1 → Alice → Widget without explicit instructions about the reference syntax.

The 30-70% token savings come from removing JSON's structural overhead (braces, quotes, colons, commas) while keeping the same semantic density.

Haven't published formal benchmarks on this yet - that's good feedback. I should.