@inproceedings{wang-etal-2026-mimesis,
    title = "From Mimesis to Metamorphosis: Evolving {VLM} Judges via In-Context Comparing and Knowledge Internalization",
    author = "Wang, Juntuo  and
      Qiao, Yuming  and
      Yang, Yifan  and
      Yuan, Lunxi  and
      Luo, Liang  and
      Meng, Dan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.631/",
    pages = "12957--12971",
    ISBN = "979-8-89176-395-1",
    abstract = "Vision-language models (VLMs) are increasingly adopted as judges for subjective assessment, yet absolute scoring remains brittle due to inconsistent scales and inherent preference biases. To bridge this gap, we propose $\text{S}^2\text{AD}$ (**Semantic-Anchored Scale-Agnostic Distillation**), a novel easy-to-hard framework that operationalizes subjective assessment as comparative analysis, conceptualizing the judge{'}s evolution from mimesis to metamorphosis. In Stage 1 (Mimesis), we introduce Dynamic Soft Positioning (DSP) to train the judge to compare a query against retrieved reference images, establishing a relative evaluation space that ensures consistent ordering under heterogeneous scales. In Stage 2 (Metamorphosis), this comparative capability is internalized via Language Buttons{---}discrete semantic levels serving as a retrieval-free internal reference. Optimized with Group Relative Policy Optimization (GRPO), $\text{S}^2\text{AD}$ achieves efficient, scale-steerable inference that adapts to diverse grading standards. Our framework reaches state-of-the-art performance across multiple benchmarks, validating the effectiveness of internalized comparative priors for robust, rank-invariant, and scale-steerable evaluation. The code is available at: https://github.com/SpatialVision-Research/SSAD{\_}ACL2026{\_}Findings."
}