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

• URL: http://arxiv.org/abs/2608.13463v1
• Date: Thu, 13 Aug 2026 16:45:24 GMT
• Title: MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification
• Abstract summary: ARMDIL is an Adaptive Router for Multi-Domain Image classification with LLMs.<n>Our ensemble employs convolutional neural networks (ResNets), self-language representation learners (SSL), and vision-supervised models (VLMs)<n> Empirical evaluations illuminate the distinct capabilities and vulnerabilities of each architecture across disparate visual domains.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern image classification models excel when trained on single task-specific datasets but often struggle to generalize across domains and difficulty levels. We propose ARMDIL, an Adaptive Router for Multi-Domain Image classification with LLMs. ARMDIL is an ensemble that uses a multimodal large language model (MLLM) agent to dynamically route each image to the most suitable vision backbone. Our diverse ensemble employs convolutional neural networks (ResNets), self-supervised representation learners (SSL), and vision-language models (VLMs), each trained on a unified label space constructed from multiple image datasets with differing distributions and characteristics. Empirical evaluations illuminate the distinct capabilities and vulnerabilities of each architecture across disparate visual domains. Crucially, we show that ARMDIL effectively navigates these trade-offs, performing competitively with specialized training-based routers. Furthermore, it drastically improves adaptability by allowing new information to be integrated via simple prompt modifications, while enhancing interpretability through natural language reasoning traces. These advances in cross-dataset image classification pave the way for more reliable general-purpose vision systems such as AI assistants and autonomous robots.

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