Compute-Bounded Security Assurance - Coverage, Verification, and Response under Resource Constraints

The inquiry distinguishes repeated success, distinct coverage, accepted evidence, resource use, service capacity, and operational protection when evaluating added inference effort.

Source: E023

These assurance quantities should not be treated as interchangeable measures of defensive progress.

Source: E023

The work integrates coverage, verification, evidence acceptance, resource constraints, service behavior, response timing, defensive architecture, and evaluation design into an assurance framework.

Source: E027

Its contribution is a theoretical integration with counterexamples against unwarranted extrapolation, rather than a new empirical performance result or scaling law.

Source: E028

For repeated fixed-procedure assessment, coverage is characterized through a latent per-attempt success distribution.

Source: E025

Within that model, coverage approaches a limit and its successive increments are nonnegative and diminishing.

Source: E018, E040

This characterization applies to fixed-procedure repetition and does not model arbitrary adaptive reassessment.

Source: E011

Pairwise outcome correlation and effective sample size for mean estimation do not determine coverage behavior.

Source: E012

Conditionally independent Bernoulli models can match in mean success and pairwise correlation while having different limiting coverage, including a limit below complete coverage.

Source: E031

Evaluation should prespecify the task population, versions, outcome equivalence, weights, admissible evidence, budgets, deadlines, and primary estimand while separating tuning from testing.

Source: E042

Reporting should separately retain distinct correct outcomes, acceptance errors, missed conclusions, abstention, grounding, utility, latency, completed and abandoned work, and complete resource use.

Source: E026

The numerical illustrations evaluate stated formulas using synthetic parameter choices.

Source: E022

The work reports no reproduced vulnerability, live-system assessment, operational performance dataset, or deployment trial.

Source: E022

The work integrates coverage, verification, evidence acceptance, resource constraints, service behavior, response timing, defensive architecture, and evaluation design into an assurance framework.

Source: E027

Its contribution is a theoretical integration with counterexamples against unwarranted extrapolation, rather than a new empirical performance result or scaling law.

Source: E028

The inquiry distinguishes repeated success, distinct coverage, accepted evidence, resource use, service capacity, and operational protection when evaluating added inference effort.

Source: E023

These assurance quantities should not be treated as interchangeable measures of defensive progress.

Source: E023

The work integrates coverage, verification, evidence acceptance, resource constraints, service behavior, response timing, defensive architecture, and evaluation design into an assurance framework.

Source: E027

Its contribution is a theoretical integration with counterexamples against unwarranted extrapolation, rather than a new empirical performance result or scaling law.

Source: E028

The assurance object fixes the system version, configuration, property specification, environmental assumptions, evaluation horizon, and a finite obligation collection.

Source: E030

Obligation weights can describe coverage or workload, but interpreting them as risk requires further assumptions about event likelihood, consequences, and overlap.

Source: E035

The defensive scope covers authorized evidence review, specified configuration checking, and assessment of documented remediation.

Source: E014

No autonomous exploitation procedure is specified; an external harmful-event process serves only to characterize defensive response timing.

Source: E033

For repeated fixed-procedure assessment, coverage is characterized through a latent per-attempt success distribution.

Source: E025

Within that model, coverage approaches a limit and its successive increments are nonnegative and diminishing.

Source: E018, E040

This characterization applies to fixed-procedure repetition and does not model arbitrary adaptive reassessment.

Source: E011

The proposed architecture routes versioned evidence through bounded assessment and separate adjudication before recommendations enter an authorized change process.

Source: E029

The design does not give model output authority to act, and protective controls remain relevant when inference or evidence services fail.

Source: E010

Pairwise outcome correlation and effective sample size for mean estimation do not determine coverage behavior.

Source: E012

Conditionally independent Bernoulli models can match in mean success and pairwise correlation while having different limiting coverage, including a limit below complete coverage.

Source: E031

Under independent exponential impact and detection clocks with deterministic mitigation delay, prevention probability follows the stated analytic relation.

Source: E009

The illustrative calculation is conditional on assumed hazards and mitigation timing, not a measurement or a bound on adversarial compute advantage.

Source: E004

Moving latent mass away from guaranteed failure can leave finite-budget outcome laws arbitrarily similar while changing asymptotic coverage.

Source: E016

Finite-budget observations therefore cannot generally identify an asymptotic failure-support mass without reported structural or parametric assumptions.

Source: E021

The numerical illustrations evaluate stated formulas using synthetic parameter choices.

Source: E022

The work reports no reproduced vulnerability, live-system assessment, operational performance dataset, or deployment trial.

Source: E022

The coverage results are exact only within models that assume conditional independence, a latent success distribution, and a fixed evaluation universe.

Source: E001

Adaptive procedures may not have diminishing increments, open-ended production lacks a known coverage denominator, and finite-budget indistinguishability constrains support inference even when the mixture model fits.

Source: E008

Evaluation should prespecify the task population, versions, outcome equivalence, weights, admissible evidence, budgets, deadlines, and primary estimand while separating tuning from testing.

Source: E042

Reporting should separately retain distinct correct outcomes, acceptance errors, missed conclusions, abstention, grounding, utility, latency, completed and abandoned work, and complete resource use.

Source: E026