We describe the VisionEval submissions to ImageEval 2026, covering Task 1b in the English and MSA tracks and Task 2 (CRAI-Bench). For Task 1b the three statements of an item share a stem and exactly one is grounded, so we replace the three independent True/False calls of the released baseline with a single three-way forced choice, read from the logits of the candidate digit tokens. The output then satisfies the one-true constraint by construction, and contrastive instability falls from the baseline 0.2674 to 0.0670 in English and from 0.4277 to 0.1010 in MSA. The organisers’ baseline is Qwen2.5-VL-3B prompted one statement at a time, and it identifies the grounded statement about as often as our 7B system does in English (0.9310 against 0.9330), so most of that gap is the output format rather than perception or scale. Task 2 is handled by a six-parameter model over the caption version, which placed first of five teams at Spearman 0.8258; an analysis of what that model reads shows the version field is recoverable from the released instance identifiers, so the result characterises the benchmark more than the system. We also show that the five reported Task 1b columns collapse to one degree of freedom for any system marking one statement true per item, an identity satisfied exactly by all fourteen rows of the final standings.

This paper describes CalibGuard, our system for the ArGuard 2026 Shared Task on harmful content detection in Arabic memes and large language model prompts. We participate in all four subtasks: binary and fine-grained hateful meme classification (A1, A2) and binary and harm-domain prompt classification (B1, B2). For Task A, we extract frozen embeddings from three vision-text encoder pairs and train a joint two-headed classifier under 5-fold cross-validation with seed averaging. For Task B, we fine-tune AraBERTv02 with class-weighted cross-entropy. CalibGuard obtains 75.53% macro-F1 on subtask A1, 36.53% on A2, 65.38% on B1 and 68.85% on B2, ranking 11th, 3rd, 15th and 13th respectively. We further show that the B1 test split contains 49.3% unsafe prompts against 79.8% in training, and that this distribution shift accounts for a 29-point gap between our development and test scores, concentrated entirely in Modern Standard Arabic. Rethresholding to the test prior recovers 13.7 points without retraining. We additionally report two negative results: constrained decoding marginally reduces A2 performance, and restricting B2 training to unsafe prompts costs 5.7 points.