MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification

Daniel Perkins, John Squires, Janou Milligan, Chandra Raskoti, Linda Ungerboeck|August 13, 2026arXiv

Key Takeaway

Instead of training separate models for each domain, you can use an MLLM router to dynamically select which vision backbone handles each image, getting both better generalization and easier updates without retraining.

Summary

ARMDIL uses a multimodal language model to intelligently route images to the best-suited vision model (CNNs, self-supervised learners, or vision-language models) within an ensemble.

multimodal architecture evaluation

Key Terms

multi-agent-ensemble vision-language-models self-supervised-pretraining domain-adaptation routing-mechanism