When 10,000 Windows Are Not 10,000 Tests: Auditing Statistical Confidence in Sliding-Window Time-Series Classification

We study efficient, adaptive auditing of categorical labels in a fixed dataset, motivated by biodiversity-credit programs in which expert verification of species records is costly and downstream scores (e.g., payment) are more sensitive to errors on certain labels than on others. Our proposed ATLAS framework couples anytime-valid inference with a task-aware adaptive auditing policy. Representing label corrections through a confusion matrix, we prioritize which data points to inspect next based on both label uncertainty and the influence of potential label corrections on the downstream score function. For linear scores such as the mislabel rate, our priority-aware approach certifies the overall label error rate to $\pm 2$ percentage points with 24% and 11% fewer audits than random sampling on biodiversity data from Kenya and a widely used CIFAR-10N benchmark, respectively. For nonlinear scores such as the Hill-Shannon diversity, we exploit a linear-envelope representation that reduces inference to a family of linear-score audits; priority-aware sampling reaches the target precision with 36% fewer audits. When distribution-free guarantees are prohibitively costly, we develop a model-based Bayesian alternative that trades assumption-free validity for substantially fewer audits. Our biodiversity case study highlights a gap between statistical validity and practical feasibility: generic methods such as Serfling's bound and existing confidence-sequence procedures are rigorous but can require prohibitively many expert reviews in practice. Together, these results show task-aware auditing can significantly reduce the number of expert audits needed while providing finite-sample, anytime-valid guarantees.

Wearable human-activity recognition (HAR) models operate across sensors, subjects, and backbones, yet a smooth waveform may appear temporal while exploiting a persistent sensor offset primarily. We introduce SpectrumAudit, a label-sealed audit that fits a phase-randomized full-window stimulus on calibration windows from subjects held out from training and testing. After selection, it replays its exact DC projection and budget-constrained zero-mean residual on the same frozen victim without refitting. Across 27 victims from three datasets and three backbones, the selected waveforms cause 2.87-40.83-point three-phase robust accuracy losses. Under this replay budget, DC is more damaging than AC on 24/27 victims and recovers at least 90% of the full drop on 22/27; all 5 failures occur on WISDM. In a held-out UTD-MHAD check, the selected waveform causes 13.49-pp accuracy and 11.68-pp macro-F1 losses, versus -0.66 pp for matched random changes. The audit diagnoses offset versus zero-mean variation under a common peak-budget cap. The code will be released upon acceptance.

A selective classifier may be claimed to satisfy a risk-coverage contract if some rule from a predeclared menu (a threshold or a union of $K$ score intervals) attains coverage at least $κ$ and error at most $α$. Refuting this existential claim requires ruling out every permitted rule. The intersection-union test (IUT) achieves validity without a multiplicity correction, but its sample cost can increase with menu richness. We characterize this cost and compare it with the optimal audit. For a fixed interior risk target $α$ and gap $γ$, the minimax sample size for K-interval menus at coverage floor $κ$ is $K(1-κ)/(κγ^2)$, up to logarithmic factors, in the stated small-gap, large-menu regime; exact IUT attains this rate. The lower bound holds even with a known score distribution, while the upper bound extends to menus of VC dimension $d$. Procedures that refuse to reject whenever any rule appears feasible on the audit sample can pay a linear $K$ cost even when a square-root rate is possible. Reference-free testing reaches a smaller scale under constant-risk alternatives, whereas unknown heterogeneous risks can make the linear cost unavoidable. In simulations with $K=4$, the raw menu cost varies 7.7-fold between $κ=.5$ and $κ=.95$; after theorem-motivated normalization the endpoint values differ by approximately 5%. Held-out experiments with three VLM judges show positive exact-hiding windows in all 60 split-specific confidence-gate families, with 28/30 contracts chosen on audit-A remaining hidden on untouched audit-B. On a cleaner multi-annotator target, hiding is weaker and transfers in 3/10 resplits. Frozen image and multimodal classifiers show boundary behavior consistent with the IUT guarantee and strong power relative to practical certificates.