Multimodal large language models (MLLMs) increasingly serve as judges in training and evaluation pipelines, often assessing answers together with explanations.
Although AI judges are sensitive to textual factors, it remains unclear whether ordinary plausible explanations can sway judgments requiring visual verification.
We introduce JUPE
We introduce JUPE, a visual-question-answering (VQA)-style benchmark that fixes answers while varying explanations.
Across 14 MLLM judges, ordinary explanations that sound plausible mislead judges, with effects comparable on average to deliberately persuasive arguments.
Plain justifications and chain-of-thought rationales reduce rejection of incorrect answers by 12.7 and 13.8 percentage points, respectively.
We call this failure plausibility capture.
Signal Detection Theory
Using signal detection theory, we find a significant mean shift in decision criterion toward acceptance, without a clear change in mean discriminability.
Explanations sway judgments across domains, explanation generators, and judging protocols, while tested mitigations offer limited, model-dependent protection.