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Commit or Abstain? System One Decision Models, LLMs, and Missing Legal Authority

M. Mikail Demir · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29961 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

検証可能な法的AIは、法律の実用的なニーズに対応するスケーラブルでコスト効率的なモデルを必要とします。システムワン決断モデルは、大きな言語モデルに構築されていますが、テキストを生成する代わりに、各オプションのためのカリブレートされた確率を返します。これは、問題が閉じられている法的権威の検証に適しています:特定の保有、またはそれらのいずれも。当社は、正しい権威が欠けているときにそのようなモデルが拒否するかどうかをテストします。我々は、正しい保有が5,314項目のうちの1,670に置き換わる拒否条件でCaseHOLDを拡張し、6つの生成型LLMと4つの決定モデルを比較

Machine translation (Meta M2M100); the English original follows.

Abstract

Given the enormous number of claims to be checked, verifiable legal AI requires scalable, cost-effective models that answer the practical needs of law. System One decision models are built on large language models but, instead of generating text, return a calibrated probability for each option. This suits the verification of legal authority, where the question is closed: a given holding, or none of them. We test whether such models abstain when the correct authority is missing. We extend CaseHOLD with an abstention condition in which the correct holding is replaced in 1,670 of 5,314 items, and compare four decision models with six generative LLMs. When no option is correct, generative LLMs still pick one in 38-86% of items; the most cautious decision model does so in 30%. On standard CaseHOLD, a decision model matches the best generative LLM (88.1% vs. 88.2%) at 1/55 of the cost. Accepting that LLM's answer only when a decision model agrees halves its wrong citations, and distilling a decision model's probabilities into Qwen3-8B, without labels, yields a well-calibrated local model. Legal AI should be judged by its wrong citations, not by accuracy alone.

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Topics: Commit, Computer science, Generative grammar, Decision model, Verifiable secret sharing, Decision analysis

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