2601.09949v1.pdf

{
  "dorsal/arxiv": [
    {
      "date_modified": "2026-02-17T05:53:23.752000Z",
      "hash": "f0a6ad9bb9282deff1de8eb455a42264a41dbbb850fcec6771bca1fb83c1c837",
      "id": "a50c4227-0e96-4c80-9dcf-0755c8870c78",
      "source": {
        "execution_id": "a4543daf-9ed1-4784-82cd-68ee7ab056ea",
        "id": "arXiv Dataset",
        "type": "Model",
        "variant": "snapshot-2026-01-17",
        "version": "0.1.0"
      },
      "user_id": 1000002
    }
  ],
  "file/base": {
    "record": {
      "extension": ".pdf",
      "hash": "f0a6ad9bb9282deff1de8eb455a42264a41dbbb850fcec6771bca1fb83c1c837",
      "media_type": "application/pdf",
      "media_type_prefix": "application",
      "name": "2601.09949v1.pdf",
      "size": 4173834
    },
    "source": {
      "execution_id": "71a49004-6d26-4b20-8a9a-de3e45fcdb5a",
      "id": "file/base",
      "type": "Model",
      "version": "1.0.0"
    }
  },
  "file/pdf": {
    "private": false,
    "record": {
      "keywords": [],
      "page_count": 20,
      "producer": "pikepdf 8.15.1",
      "title": "Kinematic Tokenization: Optimization-Based Continuous-Time Tokens for Learnable Decision Policies in Noisy Time Series",
      "version": "1.7"
    },
    "source": {
      "execution_id": "9610e1b8-61cd-43f1-97aa-13d079bdfb0e",
      "id": "dorsal/pdf",
      "type": "Model",
      "version": "1.1.0"
    }
  }
}