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Essentially yes, but with a few additions CSV lacks: 1. Multiple tables in one document (table.users, table.orders) 2. References between tables (:user:42 link
by maheshvaikri99 9mo ago
Essentially yes, but with a few additions CSV lacks:
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.
- throw03172019 9mo agoGot it. Thanks. Any data on how LLMs like this format? Are they able to make the associations etc?
- maheshvaikri99 9mo agoYes - 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.