| 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. |