Augur: A Synthetic Decision Lab for Rehearsing Reactions to Product and Policy Changes
Before a product or policy change ships, the question that matters is how people will react to it. Augur rehearses that reaction offline: it builds a typed knowledge graph from the change documents, populates a grounded persona market, simulates the interaction, and returns an auditable decision memo recommending one of five actions. We assemble Gold-50, fifty real product and policy episodes whose real-world outcome is known, adjudicated against the public record, and score the five-way release verdict against it. Our central finding is methodological and negative: most of the measured gap between frontier cloud models and open-weight models we fine-tune and serve offline is attributable...
| Authors | Rahul Khedar, Mayank Malhotra, Avinash Karn |
|---|---|
| Primary category | cs.AI |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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