Robot world models now generate synthetic manipulation videos, but evaluating them is hard: realistic-looking outputs often violate physical laws, temporal consistency or task logic, while conventional metrics and monolithic Vision-Language Model (VLM) judges return scores that do not say what failed. We present RoboGaze, a training-free multi-agent VLM evaluator that grounds the task and scene, routes each subtask to dimension-specific specialists and verifies their findings with a critic, producing temporally localized diagnostic reports that state what failed, when, why and how severely, under a schema of 6 dimensions and 30 failure types whose annotation reliability and coverage we measure. To benchmark it, we introduce RoboGazeBench, 914 human-annotated clips from five generators, conditioned on simulated and real-world data and running from 4.9 to 148.9 s. Across eight open-source and proprietary VLM backbones, RoboGaze outperforms every baseline; even a published agentic detector given the same schema and a slightly larger inference budget trails it by 10.1-12.3 description-F1 points, a margin that blinded human raters confirm. On the strongest backbone it closes 83% of the gap between chain-of-thought prompting and human agreement, and its critic curbs the "cry-wolf" false positives of standard VLMs, lifting mean clean-clip accuracy from 17% to 85%. The advantage is largest on held-out generators and long clips, and carries over to selecting generated demonstrations (80% vs. 71% precision).

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