Self-Poisoning in Adaptive Out-of-Distribution Detection: A Sharp-Threshold Theory and Certified Label-Free Calibration
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Abstract (arXiv)
Test-time adaptive out-of-distribution (OOD) detectors update a memory bank from the unlabelled stream. We show this adaptation obeys a provable dynamical law. Modelling bank impurity as a generalized P'olya urn, we prove almost-sure convergence to a mean-field equilibrium whose slope acts as a reproduction number. Below one, impurity stays benign. Above one, the bank is fully poisoned and the detector collapses. The measured admission kernel is affine (R2 ≥ 0.996) with slope just below one in every encoder family (a protocol signature), so this detector class is near-critical by design, and across 96 settings the predicted threshold matches the empirical collapse, where ungated dictionaries lose up to 0.163 AUROC. We then prove that a certified admission gate, reading only a frozen reserve, severs the feedback loop and removes the transition at every contamination rate, even adversarially, while controlling false positives label-free. For the complementary static-calibration failure under drift we give CDC, which restores nominal FPR label-free on all tested drift-affected cells. Finally we prove a two-world impossibility theorem. Drift and contamination are indistinguishable without labels, forcing a closed-form power ceiling our procedure approaches. Together these give a complete possibility/impossibility characterization of label-free adaptive OOD detection.
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References · 20
- 2023OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detectioncited by 178
- 2024Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Datacited by 143
- 2021Testing for Outliers with Conformal p-valuescited by 133
- 2022Is Out-of-Distribution Detection Learnable?cited by 109
- 2023On the Stability of Iterative Retraining of Generative Models on their own Datacited by 98
- 2022Training OOD Detectors in their Natural Habitatscited by 86
- 2022Selection by Prediction with Conformal p-valuescited by 66
- 2024Strong Model Collapsecited by 62
- 2023RDumb: A simple approach that questions our progress in continual test-time adaptationcited by 50
- 2024How Does Unlabeled Data Provably Help Out-of-Distribution Detection?cited by 36
- 2025OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionarycited by 16
- 2022Adaptive novelty detection with false discovery rate guaranteecited by 14
8 more
- 2025Robust Conformal Outlier Detection under Contaminated Reference Datacited by 12
- 2023Model-free Test Time Adaptation for Out-Of-Distribution Detectioncited by 9
- 2025Noisy Test-Time Adaptation in Vision-Language Modelscited by 8
- 2025Selection from Hierarchical Data with Conformal e-valuescited by 5
- 2024On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoningcited by 4
- 2022Test-time Recalibration of Conformal Predictors Under Distribution Shift Based on Unlabeled Examplescited by 3
- 2025Anytime-valid FDR control with the stopped e-BH procedurecited by 3
- 2026When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contaminationcited by 1
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