mlsanityData to 14 Oct 2026

Discriminating Fixture Coverage in Agent-Infrastructure Verification Suites

arXiv 2610.02928 ↗First posted 2 Oct 2026cs.SE cs.AI cs.LGcited by 0

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Abstract (arXiv)

Invariant suites and runtime monitors increasingly gate agent deployment decisions, and the evidence offered for any particular suite is almost always a single observation: it passes an implementation believed correct and fails one believed broken. We measure what that observation is worth. Applying mutation analysis to an invariant suite for a multi-session agent state-projection layer, we first find that this standard validation certifies a suite in which a first-order mutant removing event-identity deduplication survives every check. We then freeze the repaired twelve-check suite, record its hash, and run it once against ten mutants specified by an adversarial reader who designed none of its fixtures: it kills five. Instrumenting the five survivors against the reference shows they fail in two distinct ways, not one. Three are never activated, because no fixture supplies an input on which the mutated code behaves differently at all. The other two corrupt internal state that no oracle in the suite can observe. The two modes need different repairs, and neither is visible from a pass/fail report. Treating the missing inputs as a coverage question, we enumerate seven discriminating dimensions of the input space, register in advance which are uncovered and which survivors they should explain, and add one fixture per uncovered dimension while reusing the existing oracles verbatim. All five survivors then die, each to the check written for its predicted dimension. We report this as a repair result on the same challenge set rather than a second held-out estimate, and give the artifact, including the frozen hash, the registered predictions, all mutants and the run logs, so the distinction is checkable.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 16 references that are in the map. First posted on arXiv 2 Oct 2026.

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References · 5

  1. 2024Open Problems in Technical AI Governancecited by 49
  2. 2022Unifying Evaluation of Machine Learning Safety Monitorscited by 10
  3. 2026A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governancecited by 8
  4. 2026When Errors Become Narratives: A Longitudinal Taxonomy of Silent Failures in a Production LLM Agent Runtimecited by 2
  5. 2026LogicHunter: Testing LLM Agent Frameworks with an Agentic Oraclecited by 2

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