Useful Features, Backward Scores: OOD in Language-Model Trajectories

Out-of-distribution (OOD) detectors prioritize inputs for closer inspection. Yet features that distinguish input groups need not yield a useful anomaly ranking. We analyze this gap in language-model trajectories under text-length control and fixed score directions. On Spam development data, an input adaptation of D^2HScore falls from raw AUROC 0.919 to 0.530 after length matching. On length-matched, held-out HateSpeech inputs, the same features yield AUROC 0.644 for a labeled linear classifier but 0.444 for an ID-fitted distance score. ToxicChat shows the same contrast. Feature-selection and backbone controls retain the main reversal pattern. Frozen Civil Comments and TweetEval irony tests also reverse (0.467 and 0.435), extending the finding beyond toxicity. In these contrasts, anomalous groups have farther centers but tighter spread. A labeled, fixed-center feature-space intervention changes rankings: equalizing spread helps some tasks and harms others. OOD evaluation must check the chosen score's ranking even when its features distinguish the classes.