We present CAML for multilingual hallucination localization at character level in SHROOM-Visions 2026. The system combines intermediate hidden states and four uncertainty features from a frozen Qwen3.5-4B evaluator, mapping supplied responses to character probabilities and five error categories. Probes from five cross-validation folds use temperature scaling and probability averaging, with about 3.37M trainable parameters in total. We compare readout depths under a common training and evaluation protocol. Selection on validation yields fixed layer 19, with macro character IoU of 0.3704; bootstrap intervals do not resolve its small differences from adjacent depth policies. Separately, the official challenge summary for the team gives macro IoU of 0.3825 and Spearman correlation of 0.4670, averaged over four languages. Independently trained Gemma probes also support localization, with fixed L24 scoring above both tested Gemma submissions combining layers. Across backbones, a single intermediate-layer readout consistently proves effective for localization, although the best depth remains backbone-specific rather than universal.

Scene text recognition systems must decide what to do when an image contains no reliably readable text. We compare two abstention contracts for a vision-language scene-text recognizer. Under implicit refusal, the model signals unreadability inside its own transcription output. Under explicit state gating, a separate five-way state classifier decides whether a transcription is emitted at all. Across four training variants sharing the same backbone, direct OCR yields similar accuracy on readable text; the two contracts differ mainly in how they handle inputs with no readable text. Gating reduces false transcription (emitting text where none is readable) from 45.9% to 1.7%, at the cost of falsely abstaining on 3.8% of readable inputs. Which contract is preferable, however, cannot be determined without first specifying how much a correct abstention is worth. When rejected inputs are excluded from scoring, all gated configurations show similar risk; when a correct abstention is credited as a useful outcome, gating lowers risk on the diagnostic set. We present this preliminary analysis not as a context-free model ranking, but as a framework for choosing an abstention contract at deployment time. These results motivate future systems that use the predicted state to select a recovery action before abstaining.