@inproceedings{ding-etal-2026-autojudger,
    title = "{A}uto{J}udger: An Agent-Driven Framework for Efficient Benchmarking of {MLLM}s",
    author = "Ding, Xuanwen  and
      Pan, Chengjun  and
      Li, Zejun  and
      Zhang, Jiwen  and
      Wang, Siyuan  and
      Wei, Zhongyu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.685/",
    pages = "15009--15034",
    ISBN = "979-8-89176-390-6",
    abstract = "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 **$A^2$-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 $A^2$-Judger significantly improves sample efficiency while maintaining reliable evaluation results."
}