Evaluating multimodal large language models (MLLMs) is becoming increasingly expensive as benchmarks grow in scale and cross-modality complexity. Inspired by structuralism in cognitive psychology, we tackle this difficulty with an adaptive evaluation framework for efficient benchmarking, namely **AutoJudger**. Instead of passively scoring on a fixed test set, AutoJudger treats evaluation as an interview-like process by keeping a hypothesized ability structure of the evaluated model and actively selecting the informative questions so as to refine these ability boundaries. Specifically, AutoJudger has three core components: **ability decomposition** to organize evaluation along meaningful capability dimensions, **ability estimation** to maintain an up-to-date quantitative profile of the model competence, and **adaptive question selection** to choose the most informative questions. To operationalize this paradigm, we introduce **A²-Judger**, a novel MLLM-based **A**gentic instantiation of **A**uto**Judger** equipped with semantic-aware retrieval and dynamic memory. Experiments on four representative multimodal benchmarks show that A²-Judger significantly improves sample efficiency while maintaining reliable evaluation results.