VisualTrust: Video Reasoning under On-Screen Claim Framing

Huachao Zhu, Junjielong Xu, Sicheng Li, Liang Yang, Mingtao Chen +3

On-screen text can describe what a video shows, or it can frame what the video is supposed to mean. Current benchmarks treat both alike, testing whether a model can read the text rather than whether it should trust it. We study claim deference, multimodal large language models' tendency to let an on-screen claim override conflicting visual evidence. We introduce VisualTrust, a counterfactual benchmark with 5,063 cases across 14 claim-fact classes. Each case pairs a question with three versions of the same video: one with no claim, one with an accurate claim, and one with a misleading claim that alters a single grounded fact. The option the misleading claim supports is the trap answer. Across seven open-weight and four proprietary models, mean accuracy falls from 90.1% without a claim to 51.9% with a misleading claim. Among initially correct responses, 42.8% flip to the trap answer and only 0.6% become other errors. Strong no-claim accuracy does not ensure resistance. We also introduce DEAR (Decoupled Evidence Arbitration and Reading), a training-free agent for video question answering that separates evidence arbitration from reading. An independent check reports conflicting…