{
  "id": 827659,
  "title": "MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification",
  "url": "https://urgent.news/2026/08/13/mllm-routed-heterogeneous-ensembles-for-robust-cross-dataset-image",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-13T16:45:24.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.13463v1"
  },
  "original_language": "en",
  "account": null,
  "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}