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Post-Audit Mirage: Identifiability Limits of Offline Agent Verification

Aman Behera, Payal Payal

arXiv:2610.31130v1cs.AIcs.LG

TL;DR

Offline tests may not reveal whether an agent update remains safe after deployment when different outcomes are compatible with the same audit evidence. The paper formalizes this boundary, benchmarks matched lifecycles, and monitors fresh group-labelled outcomes; experiments show exact observation matching and that only some inspected settings yield a qualifying decision.

  • Problem

    An offline verifier may face the same approval record in safe and harmful deployments that require opposite actions, leaving deployment safety unidentified from that record.

    Source: §1 · p. 1

  • Method

    The paper operationalizes the boundary with matched safe-harmful lifecycles and applies a fail-closed monitor to fresh group-labelled outcomes after the update is fixed.

    Source: §1 · p. 1, §3 · p. 2

  • Results

    All 13,600 public inputs matched byte for byte while protected truth reversed; only four inspected difficulty settings qualified for the primary monitor.

    Source: §4 · pp. 3–4

  • Takeaways & Limitations

    Verifier-agent evaluation should test both calculation validity and whether the available evidence supports the deployment decision.

    Source: §4 · p. 4

    The monitor assumes independent fixed-rate binary outcomes, outcome-independent missingness, and a stable target, and cannot detect undeclared selection bias, group omission, or target shift.

    Source: §4 · p. 4

Abstract

from arXiv · show

Offline tests can fail to reveal whether an agent update will remain safe after deployment. We operationalize this post-audit mirage using matched safe and harmful lifecycles that expose identical verifier-visible evidence while an isolated scorer records opposite deployment truth. Our theorem applies standard observational-equivalence and indistinguishability ideas to this executable audit boundary and states the resulting error-abstention tradeoff. The Post-Audit Mirage benchmark makes that boundary executable across authorization, scheduling, and shared-capacity environments. IdentifiedRangeMonitor uses fresh group-labelled harm outcomes to return deploy, hold, cannot determine, or unsupported. Experiments show that monitoring can recover the missing distinction in favorable settings but may remain undecided for rare groups, small effects, or missing observations. Verifier-agent evaluation must therefore test both calculation validity and whether the available evidence supports the deployment decision.

1 Introduction

An offline verifier can receive the same approval record in deployments that require opposite decisions. The paper names this information gap the post-audit mirage and develops an executable benchmark and monitoring approach.

1 Introduction

  • A deployment change affecting who reaches a sensitive route can harm a rare group despite an agent update passing stored requests and offline checks.The approval record can be identical across the safe and harmful deployments, leaving the offline verifier unable to know which action is correct.Source: §1 · p. 1
  • The post-audit mirage means a fixed audit record permits more calculation without supplying missing deployment information.The paper distinguishes this identification gap from adaptive sample reuse, repeated inference, and distribution-shift detection.Source: §1 · p. 1
  • The paper contributes an executable indistinguishability boundary, a matched-world benchmark, and a fail-closed monitor using fresh group-labelled outcomes.The benchmark spans three environments; the monitor can remain undecided when evidence is insufficient and report unsupported when collection assumptions fail.Source: §1 · p. 1
  • The paper asks whether a verifier’s permitted observations identify the deployment property, an orthogonal question to other agent-development approaches.The cited approaches include sequential candidate testing, composed controllers, exogenous audits, and benchmarks of evolutionary trajectories [10] [14] [33] [34] [38].Source: §1 · p. 2

2 Why the audit cannot decide

When safe and harmful worlds induce the same public audit record but require opposite decisions, an offline rule cannot distinguish them. Its error and abstention probabilities obey a lower bound.

2 Why the audit cannot decide

  • Equal public-record distributions and identical private-randomness distributions force every offline rule to have the same decision distribution in both worlds.The result applies standard identification and two-point indistinguishability arguments at the audit interface, rather than claiming a new theoretical principle.Source: §2 · p. 2
  • With abstention probability r, at least one matched world has error at least (1 − r)/2.For example, 20% abstention entails at least 40% error in one world.Source: §2 · p. 2
  • Figure 1 depicts matched worlds with identical complete offline evidence but opposite post-update outcomes requiring opposite actions.The safe and harmful labels are scorer-only truth and are unavailable to the verifier.Source: Figure 1 · p. 2

3 Benchmark and monitoring

The benchmark pairs safe and harmful lifecycles with identical public records, then tests whether fresh group-labelled outcomes can support a deployment decision. Monitoring is conditional on its collection assumptions and can remain undecided.

3 Benchmark and monitoring

  • Each benchmark unit pairs safe and harmful lifecycles for 50 update rounds, sharing one serialized public lifecycle object while keeping realized environment truth separate.Construction requires identical public rounds, opposite protected answers, and matching paired decision sequences for public-only methods; there are 100 AuthGate, 100 ConstraintPlan, and 72 BatchTriage pairs.Source: §3 · p. 2
  • Nine public-only diagnostic rules test whether decisions change when method-facing information is fixed, but are not faithful reproductions or rankings of complete external systems.The rules range from always hold and fixed thresholds to adaptive spending, online e-testing, reused holdout, and transferred evidence.Source: §3 · p. 2
  • After the update is fixed, the monitor uses fresh group-labelled harm outcomes; it deploys only if every group’s upper bound is safe and holds if any lower bound is harmful.When plausible harm-rate ranges cross the threshold it returns cannot_determine; Figure 2 also marks biased or shifted monitoring unsupported.Source: §3 · p. 2, Figure 2 · p. 3
  • Table 1 frames the benchmark families by their evidence boundary: public audit fields are method-facing, while deployment effects remain scorer-only.The table caption identifies the families and the distinction between verifier-visible evidence and protected deployment effects.Source: Table 1 · p. 3
  • The monitor assumes independent binary outcomes with stable within-group rates, a prespecified group list, harm-independent missingness, and an unchanged target distribution.It certifies only declared group harm, not feasibility, utility, undeclared groups, or complete deployment safety; Wilson comparisons are fixed-sample only and invalid for optional stopping.Source: §3 · p. 3

4 Experiments and results

Across the benchmark, paired audits remained indistinguishable to offline procedures, while fresh monitoring separated safe and harmful lifecycles at the final inspected scale but sometimes remained undecided.

Matched audit

  • Across 272 pairs and 50 rounds per pair, all 13,600 public inputs matched byte for byte, protected truth reversed, and all nine offline procedures returned identical paired sequences.This verifies observation equivalence without scorer leakage; it does not show that the procedures are generally equivalent or poor.Source: §4 · p. 3

Fixed-panel restoration

  • 0/272 harmful lifecycles were incorrectly deployed as safe at 20,000 attempted observations, while all 272 safe and 272 harmful lifecycles met the prespecified correctness rule.This is a descriptive result on a fixed synthetic panel, not a population error-rate estimate.Source: §4 · p. 4

Controlled difficulty sweep

  • Only four settings qualified within the inspected range; smaller margins, rarer groups, and missing observations kept monitoring undecided longer.Each Figure 3 cell reports the first attempted-observation count meeting the stated correctness and harmful-approval requirements.Source: Figure 3 · p. 4, §4 · p. 4

Scope

  • The studies do not rank the nine offline rules as general-purpose methods, and Wilson onsets are descriptive rather than formal sample-complexity estimates.The reported first qualifying inspected count is not a formal sample-complexity estimate.Source: §4 · p. 4

A Complete proof

The proof turns shared public records and randomness into equal decision laws; the benchmark operationalizes matched audit worlds and a group-wise monitor with conditional coverage.

A Complete proof

  • Equal laws for the public record and private randomness give equal decision probabilities, forcing at least one matched world’s error probability to be at least (1 − r)/2.For deterministic non-abstaining rules, one decision is wrong with probability one.Source: A Complete proof · p. 8

B Environment and integrity details

  • Paired worlds preserve all 50 public rounds while protected answers are opposite; delayed token renewal and routing changes illustrate operationally plausible hidden harm.The environment constructor checks coupled public inputs and randomness and excludes protected scorer fields from method constructors.Source: B Environment and integrity details · p. 8

C Monitor guarantee

  • Under independent fixed-rate Bernoulli outcomes within each declared group, Hoeffding bounds and a union bound provide simultaneous coverage over groups and observation counts.The guarantee supports conclusions about declared group-harm means, not feasibility, utility, undeclared groups, or shifted targets.Source: C Monitor guarantee · p. 8

D Experimental details

The experiments use fixed and controlled synthetic panels with prespecified decision criteria; the release provides source, data, and reproduction commands.

D Experimental details

  • The matched panel contains 272 semantic units, while the controlled panel uses 500 independent streams per world and cell across 18 cells.The twelve inspected sample sizes span 5 to 20,000 attempted observations; four controlled settings qualify and 14 do not by 20,000.Source: D Experimental details · p. 9
  • A controlled cell qualifies when pointwise one-sided 95% exact bounds meet the safe- and harmful-correctness thresholds and the harmful false-safe bound is at most 0.05.The first qualifying inspected look is descriptive, not a sample-complexity estimate or a simultaneous confidence statement across looks and metrics.Source: D Experimental details · p. 9
  • The executable release includes source, independent JavaScript scorers, frozen synthetic records, integrity tests, generated results, and reproduction commands.One reference regeneration took 267.30 seconds wall time on a specified Apple M5 system; the authors state these measurements are not hardware requirements.Source: D Experimental details · p. 9

E Complete status and decision-time reporting

The section reports status and decision-time summaries for fixed and controlled panels, while emphasizing the benchmark’s scope and the claims’ stated limits.

E Complete status and decision-time reporting

  • Unsupported was 0.0% at every reported look; fixed-panel tables aggregate 272 lifecycles per world, while controlled tables cover an 18-cell grid of 9,000 streams per world.The controlled grid describes its prespecified cells, not a production population.Source: E Complete status and decision-time… · p. 9, Table 3 · p. 10
  • Table 4 reports the fixed-panel first-decision stopping-time distribution, including right-censoring after 20,000 observations.Source: Table 4 · p. 10
  • The authors caution that conservative monitoring may delay beneficial updates, declared rosters may omit affected people, and the benchmark should inform rather than autonomously authorize deployment.Source: E Complete status and decision-time… · p. 10

1. Claims

  • The authors state that the abstract and introduction reflect the matched-world result, conditional monitoring result, fixed-panel scope, and controlled-panel power limitation.Source: §1 · p. 10

2. Limitations

The paper discusses limitations, including the synthetic benchmark’s scope and the descriptive nature of fixed-panel results; zero observed false-safe cases do not imply zero risk.

2. Limitations

  • Table 5 reports controlled-grid first-decision stopping times, including right-censoring after 20,000 observations.Source: Table 5 · p. 11
  • Table 6 compares fixed-panel final-look statuses at 20,000 attempted observations; Wilson is pointwise, while both confidence-sequence rows are stopping-valid under the declared model.Unsupported was 0.0% throughout.Source: Table 6 · p. 11
  • The benchmark is synthetic and makes no production-prevalence claim; fixed-panel findings are descriptive, and zero observed false-safe cases do not mean zero risk.Monitoring conclusions are conditioned on the declared sampling model.Source: §2 · p. 11

3. Theory assumptions and proofs

The theory section presents an application of standard indistinguishability ideas with a proof sketch and complete appendix proof, alongside disclosures supporting experimental reproducibility and access.

3. Theory assumptions and proofs

  • Table 7 compares controlled-grid decision onsets; Wilson onset counts are descriptive because Wilson is pointwise.Source: Table 7 · p. 12
  • Theorem 1 states its assumptions, gives a proof sketch, and has a complete proof in Appendix A.Source: §3 · p. 12

4. Experimental result reproducibility

  • The paper and public source release report scoring rules, statistical units, configurations, seeds, thresholds, decision criteria, commands, tests, and result records.Source: §4 · p. 12

5. Open access to data and code

  • The public code release includes executable source, data, configurations, commands, tests, figures, licenses, and a retained third-party notice.Source: §5 · p. 13

6. Experimental setting/details

  • Released configurations provide generator parameters, seeds, cohort sizes, aggregation rules, monitoring looks, thresholds, interval methods, and qualification rules; there is no training or optimization.Source: §6 · p. 13

7. Experiment statistical significance

The section characterizes fixed heterogeneous panels as descriptive and distinguishes pointwise intervals from sequential bounds, while limiting what first-look selection can establish.

7. Experiment statistical significance

  • Fixed heterogeneous panels are explicitly descriptive rather than population-level estimates.Source: §7 · p. 14
  • Wilson intervals are pointwise at prespecified looks, whereas sequential bounds are anytime-valid.Source: §7 · p. 14
  • First-look selection is not a formal sample-complexity estimate or simultaneous pointwise inference.Source: §7 · p. 14

8. Experiments compute resources

The checklist disclosures cover compute resources, ethics, broader impacts, release safeguards and licensing, asset documentation, human-subjects status, and language-model use.

8. Experiments compute resources

  • Appendix D reports compute hardware, memory, software version, worker count, execution times, memory use, and output size for the central released command.Source: §8 · p. 14

9. Code of ethics

  • The authors state that the work uses only synthetic environments, contains no human or personal data, and follows the NeurIPS Code of Ethics.Source: §9 · p. 14

10. Broader impacts

  • The broader-impact discussion identifies reduced unsupported deployment, delayed beneficial updates, omitted groups, and the role of human governance.Source: §10 · p. 15

11. Safeguards

  • The release contains small synthetic records and source code, not a trained model, scraped dataset, personal data, or high-risk dual-use asset.Source: §11 · p. 15

12. Licenses for existing assets

  • The only incorporated third-party implementation is MIT-licensed SeqE-Guard logic, whose license notice is retained with the source.Source: §12 · p. 16

13. New assets

  • The manuscript and public release document generation, scoring, assumptions, workload sizes, and statistical interpretation for the synthetic benchmark.Source: §13 · p. 16

14. Crowdsourcing and research with human subjects

  • The work contains no crowdsourcing or research with human subjects, so institutional review was not applicable.Source: §14 · pp. 16–17

16. Declaration of LLM usage

  • Language models are not an original or non-standard component of the benchmark, monitor, evaluators, or experiments.Source: §16 · p. 17

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