Compete, Don't Erase: Contradictory Fine-Tuning Masks Rather Than Deletes Knowledge

Does fine-tuning a language model on a fact that contradicts what it already knows delete the old value, or merely let a new value out-compete it? Reading the model's own log-probabilities of the old and the new value across training answers it directly: across three models in two families (Qwen2.5-1.5B/3B and Llama-3.2-3B, the latter on two independent fact draws), contradictory fine-tuning leaves the old value within ~1-1.5 nats of its pre-contradiction reference — zero of the contradicted facts are erased — while the new value wins by rising, not by the old one falling. The same readout, applied to a localized weight edit (constrained FT-L on a different model, GPT-2 XL), instead reads erasure (log-probability of the old value collapses ~20 nats, no return under paraphrase), so the diagnostic discriminates: it reports an erasure when a real erasure occurs. The new value is up-weighted in proportion to the surprise of the correction (the error-proportional cross-entropy signature made directly visible); a stronger prior resists correction more (a moderate, per-fact reactance effect); and an across-layer tuned-lens reading localizes the change as a shallow late patch on an intact old-value trunk — the early and middle layers still compute the old value, and the new value is written almost entirely into the final layers. On the masked model the old value behaves like masked, not deleted, content: under a matched budget it is relearned several times faster than it was first installed, and a context shift recovers part of its margin (the rapid-reacquisition and renewal signatures documented for extinction in behavioural neuroscience). A pre-registered per-fact prediction about recovery reversed and is reported as a disconfirmation. The contribution is a lens, a validated mask-versus-erase diagnostic (with a continuous, threshold-insensitive erase index), and a cross-substrate unification — not a new unlearning method and no state-of-the-art forgetting number. The readout is the direct mask-versus-erase metric the unlearning literature has explicitly said it lacks. Series position. Paper in the Friction Theory paper-series; the competing-routes substrate is developed in the series' foundational papers, and the confident-wrong-at-the-near-tie reading is shared with the measurement-model companion. Data and code. The synthetic-fact generators, training and evaluation scripts, and per-checkpoint logit trajectories are released with the publication.