Evaluating multimodal large language models (MLLMs) is becoming increasingly expensive as benchmarks grow in scale and crossmodality 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 interviewlike 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, Auto-Judger 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 A 2 -Judger, a novel MLLM-based Agentic instantiation of AutoJudger equipped with semantic-aware retrieval and dynamic memory.Experiments on four representative multimodal benchmarks show that A 2 -Judger significantly improves sample efficiency while maintaining reliable evaluation results.