ISO-Bench: Benchmarking Multimodal Causal Reasoning in Visual–Language Models through Procedural Plans

Understanding causal relationships across modalities is a core challenge for multimodal models operating in real-world environments.We introduce ISO-BENCH, a benchmark for evaluating whether models can infer causal dependencies between visual observations and procedural text.Each example presents an image of a task step and a text snippet from a plan, with the goal of deciding whether the visual step occurs before or after the referenced text step.Evaluation results on ten frontier visionlanguage models show underwhelming performance: the best zero-shot F1 is only 0.57, and chain-of-thought reasoning yields only modest gains (up to 0.62 F1), largely behind humans (0.98 F1).Our analysis further highlights concrete directions for improving causal understanding in multimodal models.