Contribution: Prior-Art Review — Negative-Control Method for Verifier-Side Silent-Failure Detection

fg-tida-themes-contribution-prior-art-review-negative-control-method-for-verifier-side-silent-failure-detection-34

TypeFG-TIDA document
GroupTrust and Identity for Humans and Agentic AI
StateSubmitted
AuthorsNot entered
Last updated2026-10-23 15:10 UTC

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Themes

Abstract

Prior-Art Review — Negative-Control Method for Verifier-Side Silent-Failure Detection

Version: 1.0 (draft) · Date anchor: composed as-of September 2026 (no fixed-day claim)
Canonical pin: e977a04a77c71b2f67925c06c343eca7874c98fb (set digest a5b9c7a7)
Archived DOI: 10.5281/zenodo.23051638 (concept 10.5281/zenodo.23051637)

© Rights & attribution block (required on every external copy)

© 2026 赵兴华 / Steven Zhao·China (ORCID 0009-0001-0512-1237).
Brands SynomosAI / MedXpert are unregistered names only — no legal entity or trademark filed.
Code: MIT License. Review / method content: All Rights Reserved (ARR).
This is a constructed, illustrative review of a constructed, illustrative method — not a record of specific real-world incidents.
Reproduce only from the pinned commit above; cite the archived DOI 10.5281/zenodo.23051638.

1. Purpose and scope

This review positions the Negative-Control Method for Verifier-Side Silent-Failure Detection
(the method referenced in FG-TIDA/themes #6 and #21, and specified in method_specification_v1.md)
against six strands of prior and adjacent work. It is a literature-mapping document, not a novel
contribution claim.

Every relation statement below follows a three-part pattern: [claim] → [pointer] → [boundary].
The method itself is constructed and illustrative; its Layer-2 beacon-seeded generator is
design-stage / not yet implemented, and the catalogue's 14 cases are hand-constructed. This
review does not assert the method has been third-party verified or externally validated
— those are explicitly out of scope and pending.

Delimitation (important): this method is an evaluation technique for verifier-side failure
semantics. It is not an AI-governance or policy framework.

2. How the search was done

Six one-source-verified search threads were run and re-run for first-hand citations:

  1. Negative controls in ML / causal-discovery evaluation (Petersen 2025, UAI / PMLR).
  2. Silent / measurement failure in AI evaluation (Bajaj 2026, arXiv).
  3. Verifier independence / third-party evaluation (OpenAI external-testing page; AEF-1; METR).
  4. NIST AI 600-1 Generative AI Profile (red-teaming actions, esp. MS-2.7-007).
  5. ISO/IEC 42001:2023 AI Management System (Annex A controls).
  6. EU AI Act Article 43 conformity assessment (Annex VI / VII; Digital Omnibus 2026/1744).

No claim in this review is stated without a resolvable first-hand pointer (proceedings, arXiv,
agency publication, standard, regulation, or primary web page).

3. Prior-art map

# Source What it establishes (one-source verified) Relation to our method
1 Petersen (2025), Are You Doing Better Than Random Guessing? A Call for Using Negative Controls When Evaluating Causal Discovery Algorithms, UAI 2025, PMLR 286:3465–3479; arXiv:2412.10039 (December 2024) Derives exact distributional results under random guessing for precision/recall in graph-skeleton estimation; warns metrics can look favourable under guessing and calls for negative controls as a common baseline. Complements / extends. We reuse the negative-control idea but point it at a different failure mode (verifier-side silent substitution) and add the verifier-independence axis (who fixes and holds the answer key) that Petersen does not address.
2 Bajaj (2026), Evaluation Blindness: How Silent Measurement Failures Corrupt AI Systems from Training to Deployment, arXiv:2608.02786 (submitted 3 August 2026) Formalises evaluation blindness with a detectability predicate; a six-class taxonomy over 50 incidents finds 53% of 36 verifiable public incidents silent; one class (Operational) is 100% silent by structural definition. Operationalises a countermeasure. We turn the "measurement function cannot distinguish failing from healthy" problem into a deployable verifier-layer control: pre-registered outcomes + mechanical scoring + commitment–escrow. Direct answer to the deployment-time "100% silent" class.
3 OpenAI, Strengthening our safety ecosystem through external testing (web); AI Evaluator Forum AEF-1 standard (September 2026), cosigned by OpenAI / Anthropic / xAI; METR's funding-independent model Frames verifier independence as an organisational property — access, conflicts of interest, funding independence, recusal, transparency. Adds a protocol-level instantiation. Organisational independence still trusts a lab's funding/access model. Our commitment–escrow + public-beacon design supplies independence without that trust dependency — complementary, not competing.
4 NIST (2024), AI 600-1, AI Risk Management Framework: Generative AI Profile (July 2024) Recommends AI red-teaming across GOVERN/MAP/MEASURE (GV-4.1-002, MP-5.1-005, MS-1.1-008, MS-1.3-002, MS-2.7-007, MS-2.10-001); MS-2.7-007 is effectively a test-plan outline. Maps to / supports. Our negative controls are a concrete, reproducible technique matching the spirit of MS-1.3-002 (evaluation with context-knowledgeable people) and MS-2.7-007 (resilience assessment). We provide the mechanical scoring substrate those actions ask for.
5 ISO/IEC 42001:2023, Information technology — Artificial intelligence — Management system (December 2023) First certifiable AIMS; 38 Annex A controls over 9 objectives (A.2–A.10); A.5 impact assessment (individuals + society), A.6 lifecycle incl. A.6.2.4 verification & validation. Feeds conformance evidence. Our harness is an auditable evaluation artifact that supports A.6 lifecycle controls (notably A.6.2.4 V&V) and the impact-assessment discipline of A.5. It is evidence for the standard, not a replacement.
6 Regulation (EU) 2024/1689 (AI Act), Article 43; amended by Regulation (EU) 2026/1744 (Digital Omnibus on AI, in force 27 July 2026) High-risk conformity assessment: Annex III point 1 (biometrics) may use Annex VI internal control if harmonised standards applied, else Annex VII notified body; points 2–8 use Annex VI internal control (no third party). Deadlines deferred to 2 December 2027 (Annex III) and 2 August 2028 (Annex I). Feeds the internal-control evidence pack. For the Annex VI route, our reproducible negative-control harness supplies auditable technical-documentation evidence for Art. 9 (risk management) and Art. 15 (accuracy/robustness) — without requiring a notified body.

4. What our method adds (honest positioning)

  • Negative controls applied to the verifier side, not the model. Petersen uses them for
    causal-discovery metric baselines; we use them so that whoever scores the panel cannot become
    the sole authoritative evaluator    .
  • Independence without organisational trust. Third-party-eval frameworks (AEF-1 / METR) make
    independence a function of access and funding. Our Layer-2/3 design makes it a function of
    construction (public beacon seed, or threshold escrow + reveal on trigger).
  • A deployable countermeasure to evaluation blindness. Bajaj shows the failure is structural;
    we supply the mechanical, pre-registered-outcome scoring that makes the silent class detectable.

Boundaries (must keep): this is an evaluation technique, not a governance framework; it is
not yet third-party verified; Layer-2 beacon generator is design-stage; the 14 catalogue
cases are hand-constructed (the protocol's non-public fallback).

5. Limitations of this review

  • It is a mapping, not a systematic literature review; coverage is bounded by the six threads
    above. Adjacent work (e.g., METR's AEF, specific red-teaming benchmarks) is named only where it
    directly informs the six strands.
  • All external works are referenced under their own terms; this rights block governs only our text.
  • No novelty-supremacy claim is made (we do not assert the method predates or supersedes other work).

6. How to cite

Zhao Xinghua / Steven Zhao·China. Prior-Art Review — Negative-Control Method for Verifier-Side
Silent-Failure Detection, v1.0 draft. silent-failure-catalog, pin e977a04a.
Zenodo DOI 10.5281/zenodo.23051638. Code MIT; content ARR.

References (external, under their own terms)

  1. Petersen, A. H. (2025). Are You Doing Better Than Random Guessing? A Call for Using Negative
    Controls When Evaluating Causal Discovery Algorithms.     Proc. 41st Conference on Uncertainty in
    Artificial Intelligence (UAI 2025), PMLR 286:3465–3479. arXiv:2412.10039 (December 2024).
  2. Bajaj, P. (2026). Evaluation Blindness: How Silent Measurement Failures Corrupt AI Systems from
    Training to Deployment.     arXiv:2608.02786 (submitted 3 August 2026).
  3. OpenAI. Strengthening our safety ecosystem through external testing. (web page). AI Evaluator
    Forum, AEF-1 standard (September 2026); METR independent-evaluation funding model.
  4. NIST (2024). AI 600-1, Artificial Intelligence Risk Management Framework: Generative Artificial
    Intelligence Profile.     (July 2024).
  5. ISO/IEC 42001:2023, Information technology — Artificial intelligence — Management system.
    (December 2023).
  6. Regulation (EU) 2024/1689 (Artificial Intelligence Act), Article 43; amended by Regulation (EU)
    2026/1744 (Digital Omnibus on AI, in force 27 July 2026).

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