arXiv Artificial Intelligence

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

3 hours ago 14.08.2026 07:00 👁 2 views
MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification

Quick summary

arXiv:2608.13463v1 Announce Type: cross 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

Key takeaways

  • arXiv:2608.13463v1 Announce Type: cross 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.

Why it matters

“MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗