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Self-Poisoning in Adaptive Out-of-Distribution Detection: A Sharp-Threshold Theory and Certified Label-Free Calibration

arXiv 2607.21673 ↗First posted 23 Jul 2026cs.LG cs.AI cs.CRcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 35 references that are in the map. First posted on arXiv 23 Jul 2026.

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20 of 35 references.

References · 20

  1. 2023OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detectioncited by 178
  2. 2024Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Datacited by 143
  3. 2021Testing for Outliers with Conformal p-valuescited by 133
  4. 2022Is Out-of-Distribution Detection Learnable?cited by 109
  5. 2023On the Stability of Iterative Retraining of Generative Models on their own Datacited by 98
  6. 2022Training OOD Detectors in their Natural Habitatscited by 86
  7. 2022Selection by Prediction with Conformal p-valuescited by 66
  8. 2024Strong Model Collapsecited by 62
  9. 2023RDumb: A simple approach that questions our progress in continual test-time adaptationcited by 50
  10. 2024How Does Unlabeled Data Provably Help Out-of-Distribution Detection?cited by 36
  11. 2025OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionarycited by 16
  12. 2022Adaptive novelty detection with false discovery rate guaranteecited by 14
8 more
  1. 2025Robust Conformal Outlier Detection under Contaminated Reference Datacited by 12
  2. 2023Model-free Test Time Adaptation for Out-Of-Distribution Detectioncited by 9
  3. 2025Noisy Test-Time Adaptation in Vision-Language Modelscited by 8
  4. 2025Selection from Hierarchical Data with Conformal e-valuescited by 5
  5. 2024On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoningcited by 4
  6. 2022Test-time Recalibration of Conformal Predictors Under Distribution Shift Based on Unlabeled Examplescited by 3
  7. 2025Anytime-valid FDR control with the stopped e-BH procedurecited by 3
  8. 2026When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contaminationcited by 1

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