2601.06559v1.pdf

{
  "dorsal/arxiv": [
    {
      "date_modified": "2026-02-17T05:53:07.769000Z",
      "hash": "88668e98189081fa82283944412bf517c699abc199c91430afa5431375a59453",
      "id": "d24287d0-e6a7-4666-a582-e0ca1f663346",
      "source": {
        "execution_id": "be0cc1f3-65b1-4781-a758-a52f3a5265a1",
        "id": "arXiv Dataset",
        "type": "Model",
        "variant": "snapshot-2026-01-17",
        "version": "0.1.0"
      },
      "user_id": 1000002
    }
  ],
  "file/base": {
    "record": {
      "extension": ".pdf",
      "hash": "88668e98189081fa82283944412bf517c699abc199c91430afa5431375a59453",
      "media_type": "application/pdf",
      "media_type_prefix": "application",
      "name": "2601.06559v1.pdf",
      "size": 5692285
    },
    "source": {
      "execution_id": "b082707c-89c7-49e3-9e7b-895f9226eb01",
      "id": "file/base",
      "type": "Model",
      "version": "1.0.0"
    }
  },
  "file/pdf": {
    "private": false,
    "record": {
      "keywords": [],
      "page_count": 14,
      "producer": "pikepdf 8.15.1",
      "title": "ArrowGEV: Grounding Events in Video via Learning the Arrow of Time",
      "version": "1.7"
    },
    "source": {
      "execution_id": "76e27a9b-b4f6-4b4e-ad1a-a0d9e200eb36",
      "id": "dorsal/pdf",
      "type": "Model",
      "version": "1.1.0"
    }
  }
}