mlsanityData to 7 Oct 2026

Compiling Sufficient Governance Context from Declared Losses and Reachable States: Exact Observation-Contract Synthesis with Cardinality and Cost Objectives

arXiv 2609.26016 ↗First posted 22 Sep 2026cs.SE cs.AI cs.LOcited by 1

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Abstract (arXiv)

We call the object this paper derives and certifies a minimal sufficient governance context: given a finite reachable-state model, a deterministic declared verdict, and candidate observable attributes, we compute sufficient observation sets, distinguish attributes that are individually indispensable from contracts that are jointly sufficient, and select among sufficient contracts under a cardinality or declared-cost objective. An observation contract is a set of candidate attributes whose values determine the declared verdict on every reachable state; an authority contract is one selected under an objective and bound to a gate schema. We synthesize every inclusion-minimal sufficient contract where exhaustive enumeration is affordable, and a minimum-cardinality or minimum-cost contract by SAT/MaxSAT encoding otherwise, checking sufficiency directly. On a constructed code/cloud domain, the individually-indispensable core is not sufficient as an observation contract and two distinct reducts exist; a preregistered cost model separates them exactly. On a second, larger, constructed domain, the same pattern recurs, but that domain's cost model does not separate the alternatives: a fully explained cost tie, reported as found. We measure discernibility-family scaling where exhaustive enumeration is confirmed infeasible within a registered timeout, while SAT/MaxSAT synthesis solves in well under a second; MaxSAT showed no measured cardinality advantage over plain SAT. AuthorityBench compares four baselines across three domains; the declared-only baseline is not exactly sufficient on any. Every selected contract is checked for sufficiency, with a counterexample on failure and a check summary, not a portable certificate, on success -- the compiler-focused scope of a two-scope table; an independently specified end-to-end case study is registered follow-up work, not claimed here.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 33 references that are in the map. First posted on arXiv 22 Sep 2026.

Builds on

8 of 33 references.

References · 8

  1. 2017Counterfactual Fairnesscited by 802
  2. 2015Shield Synthesis: Runtime Enforcement for Reactive Systemscited by 41
  3. 2014Mining Attribute-Based Access Control Policies from Logscited by 10
  4. 2023STPA for Learning-Enabled Systems: A Survey and A New Practicecited by 7
  5. 2024From Silos to Systems: Process-Oriented Hazard Analysis for AI Systemscited by 6
  6. 2025Systematic Hazard Analysis for Frontier AI using STPAcited by 6
  7. 2022Machine Learning in Access Control: A Taxonomy and Surveycited by 3
  8. 2026One Gate Is Not Enough: Composing Stateful Pre-Action Controls for Agentic AIcited by 2

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