Governed Deduction: Policy-Grounded Premise Authorization Beyond Relevance
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Abstract (arXiv)
Reasoning systems usually treat premise use as a question of relevance: if a fact is available and useful, it may be selected for inference. Authorization imposes a different constraint: a premise may be represented and logically usable but not permitted for a particular local transition. We formalize this distinction as Governed Deduction (GD), with a transition-local admission predicate admit(p, tau, S). From an independently produced RBAC-augmented Spider benchmark, we construct 4,461 matched authorization pairs in which the same query premise and policy state support permitted and denied consuming transitions. An initial joint controller reaches 99.19% held-out accuracy, but a transition-only control reaches 100%, exposing a role-name shortcut. After a frozen, label-independent context-local role permutation removes that shortcut, premise/state-only, transition-only, and joint linear controllers all score exactly 50% on 1,856 held-out edges, while a symbolic policy oracle remains at 100%. The result is a controlled negative finding: the benchmark instantiates policy-grounded authorization beyond relevance, but the frozen linear representation does not recover the relation. Matched one-sided controls and leakage audits are therefore essential for evaluating learned policy-sensitive reasoning.
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References · 11
- 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Modelscited by 427
- 2022Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoningcited by 334
- 2020Transformers as Soft Reasoners over Languagecited by 270
- 2021Proof Artifact Co-training for Theorem Proving with Language Modelscited by 132
- 2016DeepMath - Deep Sequence Models for Premise Selectioncited by 81
- 2023Magnushammer: A Transformer-Based Approach to Premise Selectioncited by 50
- 2019Generating Correctness Proofs with Neural Networkscited by 37
- 2021TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learningcited by 37
- 2013MizAR 40 for Mizar 40cited by 23
- 2025Don't Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Modelscited by 8
- 2026Pause or Fabricate? Training Language Models for Grounded Reasoningcited by 2
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