{
  "id": 19437,
  "url": "https://arxiv.org/abs/2608.13463v1",
  "title": "MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification",
  "summary": "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",
  "authors": "Daniel Perkins, John Squires, Janou Milligan, Chandra Raskoti, Linda Ungerboeck",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T16:45:24.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19437",
  "original_url": "https://arxiv.org/abs/2608.13463v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}