MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification
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 learn
Record details
Published: 13 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Agents & autonomy
Retrieved: 14 August 2026
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ethics.ai (13 August 2026), “MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification,” evidence record 19437, https://ethics.ai/record/19437 (originally published by arXiv cs.AI).
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