YUKTI: Robust Decision-Making from Natural Language with Uncertainty-Typed Propositions

Jul 14, 2026

YUKTI introduces a framework that converts natural-language situations into robust, verifiable decisions by representing assumptions as typed-proposition graphs with uncertainty. It uses Assumption-Robust Pareto Frontiers to resample assumptions and score action survival, reducing mean and tail regret by over 90% versus naive point plans. In a real public dataset of 41,188 decisions, YUKTI outperformed the logged status quo by 34% and a naive point rule by 4%, while reducing the optimizer's curse.

Why it matters: YUKTI addresses the fragility of LLM-based decision pipelines by explicitly modeling assumption uncertainty, enabling robust decisions in high-stakes domains such as budget allocation and clinical attention.

Full story at: arXiv AI/ML ↗