{
  "id": 16236,
  "url": "https://arxiv.org/abs/2608.01827",
  "title": "DeepVoyager-VL: Incentivizing Vision-in-the-Loop Search for Long-Horizon Multimodal Agents",
  "summary": "Multimodal large language models (MLLMs) have advanced visual understanding and reasoning, yet their static parametric knowledge limits their ability to address knowledge-intensive and dynamically evolving open-world problems. To move beyond this limitation, multimodal deep search has emerged as a key direction for open-world information access, evolving from single-turn factual retrieval toward long-horizon, multi-turn search guided by visual evidence. However, existing methods typically confin",
  "authors": "Huanyao Zhang, Jiepeng Zhou, Runhao Zhao, Yanzhe Shan, Jiaoyang Chen, Bowen Zhou",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-02T20:00:00.000Z",
  "fetched_at": "2026-08-05T05:10:44.550Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/16236",
  "original_url": "https://arxiv.org/abs/2608.01827",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}