mlsanityData to 7 Oct 2026

Separating Shortcut Transition from Cross-Family OOD Failure in a Minimal Model

arXiv 2605.12945 ↗First posted 13 May 2026cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Shortcut features are often invoked to explain out-of-distribution (OOD) failure, but training correlation, learned shortcut use, and test-time failure need not coincide. We study a minimal binary model with one invariant coordinate and one family-dependent shortcut coordinate. In the deterministic regime, positive average shortcut correlation pulls logistic ERM toward positive shortcut weight, but ridge regularization keeps the classifier invariant-dominated and prevents deterministic OOD failure. When the invariant coordinate is noisy, ridge-logistic ERM switches to the shortcut rule once the training shortcut signal exceeds the invariant signal. Whether that transition causes failure depends on the held-out family: weaker shortcut correlation yields positive excess risk, and sign-flipped families yield above-chance error. Synthetic checks match these analytic regimes and show that the same training-side transition can have different held-out consequences. The model separates shortcut attraction, shortcut-rule transition, and cross-family OOD failure.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 17 references that are in the map. First posted on arXiv 13 May 2026.

Builds on

12 of 17 references.

References · 12

  1. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  2. 2020Shortcut Learning in Deep Neural Networkscited by 1,928
  3. 2019Invariant Risk Minimizationcited by 1,832
  4. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,366
  5. 2019Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalizationcited by 1,232
  6. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,194
  7. 2020In Search of Lost Domain Generalizationcited by 1,042
  8. 2020Out-of-Distribution Generalization via Risk Extrapolation (REx)cited by 737
  9. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  10. 2020The Risks of Invariant Risk Minimizationcited by 262
  11. 2020Learning from Failure: Training Debiased Classifier from Biased Classifiercited by 133
  12. 2021Does Invariant Risk Minimization Capture Invariance?cited by 116

Built on by

Not cited yet.