2601.06959v1.pdf

{
  "dorsal/arxiv": [
    {
      "date_modified": "2026-02-17T05:53:08.155000Z",
      "hash": "7f66606479955092e5f3ec17a3612f35d24ca19fc58a205a2ba8ee28bdb48a04",
      "id": "44a126e8-1d79-4652-b2b3-9df4d63fc06a",
      "source": {
        "execution_id": "e0545cb3-4b37-4b4b-b3e4-ad2ce9a28a8d",
        "id": "arXiv Dataset",
        "type": "Model",
        "variant": "snapshot-2026-01-17",
        "version": "0.1.0"
      },
      "user_id": 1000002
    }
  ],
  "file/base": {
    "record": {
      "extension": ".pdf",
      "hash": "7f66606479955092e5f3ec17a3612f35d24ca19fc58a205a2ba8ee28bdb48a04",
      "media_type": "application/pdf",
      "media_type_prefix": "application",
      "name": "2601.06959v1.pdf",
      "size": 344315
    },
    "source": {
      "execution_id": "24806b85-2592-41c6-9a2e-a47bce01eeb1",
      "id": "file/base",
      "type": "Model",
      "version": "1.0.0"
    }
  },
  "file/pdf": {
    "private": false,
    "record": {
      "keywords": [],
      "page_count": 5,
      "producer": "pikepdf 8.15.1",
      "title": "HAS-VQ: Hessian-Adaptive Sparse Vector Quantization for High-Fidelity LLM Compression",
      "version": "1.7"
    },
    "source": {
      "execution_id": "0e92a1c4-60cb-4bae-820f-07b3118ee6bd",
      "id": "dorsal/pdf",
      "type": "Model",
      "version": "1.1.0"
    }
  }
}