YUKTI: 自然言語の状況から堅牢で検証可能な意思決定へ――不確実性型命題IR・仮定ロバストパレートフロンティア・後悔証明書YUKTI: From Natural-Language Situations to Robust, Verifiable Decisions An Uncertainty-Typed Proposition IR, Assumption-Robust Pareto Frontiers, and a Regret Certificate

Decision-making under uncertainty is one of the hardest tasks to delegate to automated systems, and it becomes harder still when the situation is described only in ambiguous natural language. A research framework called YUKTI, presented in a recent arXiv preprint, proposes a pipeline that turns free-form textual descriptions of a decision scenario into a formal, machine-checkable representation, then produces recommendations accompanied by explicit guarantees about how sensitive those recommendations are to the underlying assumptions. The work matters because it targets a persistent weakness in current AI decision support: systems that give confident answers while silently making assumptions the user never sees.

At the center of YUKTI is what the authors call an uncertainty-typed proposition intermediate representation, or IR. The term borrows from compiler design, where an intermediate representation is a structured, normalized form that sits between human-facing input and the machinery that performs computation. Here, the natural-language scenario is parsed into propositions that carry explicit type information about their uncertainty, distinguishing, for example, facts that are stated, assumptions that are inferred, and quantities that are unknown or contested. By making uncertainty a first-class property of each proposition, the framework aims to prevent a common failure mode in which an ambiguous premise is quietly resolved one way and then treated as settled.

From this representation, YUKTI derives two main artifacts. The first is an assumption-robust Pareto frontier. Pareto optimization is a standard tool in multi-objective decision-making: when goals conflict, the Pareto frontier is the set of options for which no objective can be improved without sacrificing another. YUKTI's contribution appears to be computing this frontier so that it remains stable across the range of plausible assumptions, so the recommended trade-offs do not collapse the moment an uncertain premise shifts. The second artifact is a regret certificate. Regret, in decision theory, measures how much worse an outcome is compared with the best choice one could have made in hindsight. A certificate, in the formal-methods sense, is evidence that can be independently verified. Together, the phrase suggests a checkable bound on how much the chosen decision could underperform if the assumptions turn out to be wrong.

This combination places YUKTI within the broader movement toward neuro-symbolic and verifiable AI. Over the past few years, researchers have increasingly paired large language models, which excel at interpreting messy natural language, with symbolic or formal components that provide guarantees language models alone cannot offer. Related efforts include using LLMs to translate word problems into solver-ready constraints, autoformalization work that maps informal mathematics into proof assistants such as Lean or Isabelle, and tools that generate SMT or linear-programming instances from text. YUKTI fits this pattern by using language understanding for the front end while reserving the actual decision guarantees for an auditable, formal back end.

The emphasis on auditability is timely. As organizations consider deploying AI in higher-stakes settings such as finance, logistics, healthcare triage, and public policy, regulators and internal risk teams have pushed for systems whose reasoning can be inspected and justified rather than accepted on trust. A framework that emits an explicit list of assumptions, a frontier of defensible trade-offs, and a bound on potential regret is, in principle, easier to review than an opaque model output. It also aligns with established ideas from robust and distributionally robust optimization, where the goal is to perform well across a set of possible conditions rather than to optimize for a single guessed scenario.

Several caveats are worth noting. The reliability of the whole pipeline is likely to depend heavily on the quality of the natural-language-to-IR translation step, which is itself error-prone; a misread premise would propagate into the formal analysis. The strength of any regret certificate also depends on whether the enumerated set of assumptions genuinely captures the real uncertainty, since guarantees hold only within the modeled space. As a preprint, the results have not yet undergone peer review, and independent evaluation on diverse, realistic scenarios would help clarify how well the approach scales beyond the paper's examples.

Even with those reservations, YUKTI reflects a meaningful direction for the field. Rather than smoothing away ambiguous assumptions as noise, it treats them as objects to expose, type, and reason about explicitly. If the translation and modeling steps prove robust in practice, that shift could make AI-assisted decisions more transparent and easier to defend when the stakes are high.