{
  "schema_version": "1.0",
  "paper": {
    "arxiv_id": "2604.26505",
    "versioned_id": "2604.26505v1",
    "original_title": "Quantamination: Dynamic Quantization Leaks Your Data Across the Batch",
    "pdf_title": "Quantamination: Dynamic Quantization Can Leak Your Data Across the Batch",
    "pdf_sha256": "771fab5dd09ed5b0ba936d410bf74ad088dd94d28e6878f90bbb5a25ee5de19a",
    "manifest": "assets/provenance.json",
    "pdf_page_count": 13
  },
  "outline_verified_against_pdf": true,
  "sections": [
    {
      "key": "abstract",
      "parent": null,
      "original_number": null,
      "original_heading": "Abstract",
      "kind": "front_matter",
      "status": "summarized",
      "html_anchor": "overview",
      "pdf_pages": [
        1
      ],
      "source_spans": [
        {
          "path": "main.tex",
          "lines": [
            59,
            67
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [],
      "limitations": []
    },
    {
      "key": "s1",
      "parent": null,
      "original_number": "1",
      "original_heading": "Introduction",
      "kind": "section",
      "status": "summarized",
      "html_anchor": "section-1",
      "pdf_pages": [
        1,
        2
      ],
      "source_spans": [
        {
          "path": "content/01_introduction.tex",
          "lines": [
            1,
            19
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [
        "fig-1"
      ],
      "limitations": []
    },
    {
      "key": "s2",
      "parent": null,
      "original_number": "2",
      "original_heading": "Related Works",
      "kind": "section",
      "status": "summarized",
      "html_anchor": "section-2",
      "pdf_pages": [
        2,
        3
      ],
      "source_spans": [
        {
          "path": "content/02_related_works.tex",
          "lines": [
            1,
            9
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [],
      "limitations": []
    },
    {
      "key": "s3",
      "parent": null,
      "original_number": "3",
      "original_heading": "Background",
      "kind": "section",
      "status": "summarized",
      "html_anchor": "section-3",
      "pdf_pages": [
        3,
        4
      ],
      "source_spans": [
        {
          "path": "content/02_related_works.tex",
          "lines": [
            9,
            11
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [],
      "limitations": []
    },
    {
      "key": "s3.1",
      "parent": "s3",
      "original_number": "3.1",
      "original_heading": "Post-Training Quantization",
      "kind": "subsection",
      "status": "summarized",
      "html_anchor": "section-3-1",
      "pdf_pages": [
        3
      ],
      "source_spans": [
        {
          "path": "content/02_related_works.tex",
          "lines": [
            11,
            42
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [],
      "limitations": []
    },
    {
      "key": "s3.2",
      "parent": "s3",
      "original_number": "3.2",
      "original_heading": "Framework Deployment Landscape",
      "kind": "subsection",
      "status": "summarized",
      "html_anchor": "section-3-2",
      "pdf_pages": [
        3,
        4
      ],
      "source_spans": [
        {
          "path": "content/02_related_works.tex",
          "lines": [
            42,
            114
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [
        "table-1"
      ],
      "limitations": []
    },
    {
      "key": "s4",
      "parent": null,
      "original_number": "4",
      "original_heading": "Methodology",
      "kind": "section",
      "status": "summarized",
      "html_anchor": "section-4",
      "pdf_pages": [
        4,
        5,
        6,
        7
      ],
      "source_spans": [
        {
          "path": "content/03_methodology.tex",
          "lines": [
            1,
            3
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [],
      "limitations": []
    },
    {
      "key": "s4.1",
      "parent": "s4",
      "original_number": "4.1",
      "original_heading": "Threat Model",
      "kind": "subsection",
      "status": "summarized",
      "html_anchor": "section-4-1",
      "pdf_pages": [
        4,
        5
      ],
      "source_spans": [
        {
          "path": "content/03_methodology.tex",
          "lines": [
            3,
            50
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [],
      "limitations": []
    },
    {
      "key": "s4.2",
      "parent": "s4",
      "original_number": "4.2",
      "original_heading": "Dynamic Quantization Mechanics in Batched Inference",
      "kind": "subsection",
      "status": "summarized",
      "html_anchor": "section-4-2",
      "pdf_pages": [
        5,
        6
      ],
      "source_spans": [
        {
          "path": "content/03_methodology.tex",
          "lines": [
            50,
            71
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [
        "eq-1",
        "eq-2"
      ],
      "limitations": []
    },
    {
      "key": "s4.3",
      "parent": "s4",
      "original_number": "4.3",
      "original_heading": "Attack Methodology: LLM Token Recovery",
      "kind": "subsection",
      "status": "summarized",
      "html_anchor": "section-4-3",
      "pdf_pages": [
        6,
        7
      ],
      "source_spans": [
        {
          "path": "content/03_methodology.tex",
          "lines": [
            71,
            128
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [
        "eq-3",
        "eq-4",
        "eq-5",
        "eq-6"
      ],
      "limitations": []
    },
    {
      "key": "s4.4",
      "parent": "s4",
      "original_number": "4.4",
      "original_heading": "Attack Methodology: Classification Setting",
      "kind": "subsection",
      "status": "summarized",
      "html_anchor": "section-4-4",
      "pdf_pages": [
        7
      ],
      "source_spans": [
        {
          "path": "content/03_methodology.tex",
          "lines": [
            128,
            152
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [
        "eq-7",
        "eq-8"
      ],
      "limitations": []
    },
    {
      "key": "s5",
      "parent": null,
      "original_number": "5",
      "original_heading": "Evaluation",
      "kind": "section",
      "status": "summarized",
      "html_anchor": "section-5",
      "pdf_pages": [
        7,
        8,
        9,
        10
      ],
      "source_spans": [
        {
          "path": "content/04_evaluation.tex",
          "lines": [
            1,
            3
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [],
      "limitations": []
    },
    {
      "key": "s5.1",
      "parent": "s5",
      "original_number": "5.1",
      "original_heading": "LLM Token Recovery",
      "kind": "subsection",
      "status": "summarized",
      "html_anchor": "section-5-1",
      "pdf_pages": [
        7,
        8,
        9
      ],
      "source_spans": [
        {
          "path": "content/04_evaluation.tex",
          "lines": [
            3,
            71
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [
        "table-2",
        "table-3",
        "fig-2"
      ],
      "limitations": []
    },
    {
      "key": "s5.2",
      "parent": "s5",
      "original_number": "5.2",
      "original_heading": "Classification Model Class Recovery",
      "kind": "subsection",
      "status": "summarized",
      "html_anchor": "section-5-2",
      "pdf_pages": [
        9,
        10
      ],
      "source_spans": [
        {
          "path": "content/04_evaluation.tex",
          "lines": [
            71,
            73
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [],
      "limitations": []
    },
    {
      "key": "s5.2.1",
      "parent": "s5.2",
      "original_number": "5.2.1",
      "original_heading": "Experimental Setup",
      "kind": "subsubsection",
      "status": "summarized",
      "html_anchor": "section-5-2-1",
      "pdf_pages": [
        9
      ],
      "source_spans": [
        {
          "path": "content/04_evaluation.tex",
          "lines": [
            73,
            77
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [],
      "limitations": []
    },
    {
      "key": "s5.2.2",
      "parent": "s5.2",
      "original_number": "5.2.2",
      "original_heading": "Results: Secret in Candidate Set",
      "kind": "subsubsection",
      "status": "summarized",
      "html_anchor": "section-5-2-2",
      "pdf_pages": [
        9,
        10
      ],
      "source_spans": [
        {
          "path": "content/04_evaluation.tex",
          "lines": [
            77,
            96
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [
        "table-4"
      ],
      "limitations": []
    },
    {
      "key": "s5.2.3",
      "parent": "s5.2",
      "original_number": "5.2.3",
      "original_heading": "Results Excluding Secret From Candidate Set",
      "kind": "subsubsection",
      "status": "summarized",
      "html_anchor": "section-5-2-3",
      "pdf_pages": [
        10
      ],
      "source_spans": [
        {
          "path": "content/04_evaluation.tex",
          "lines": [
            96,
            122
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [
        "fig-3"
      ],
      "limitations": []
    },
    {
      "key": "s6",
      "parent": null,
      "original_number": "6",
      "original_heading": "Discussion",
      "kind": "section",
      "status": "summarized",
      "html_anchor": "section-6",
      "pdf_pages": [
        11
      ],
      "source_spans": [
        {
          "path": "content/05_discussion.tex",
          "lines": [
            1,
            13
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [
        "fig-4"
      ],
      "limitations": []
    },
    {
      "key": "acknowledgements",
      "parent": null,
      "original_number": null,
      "original_heading": "Acknowledgements",
      "kind": "acknowledgments",
      "status": "identified_non_substantive",
      "html_anchor": "acknowledgements",
      "pdf_pages": [
        11
      ],
      "source_spans": [
        {
          "path": "main.tex",
          "lines": [
            77,
            85
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [],
      "limitations": []
    },
    {
      "key": "references",
      "parent": null,
      "original_number": null,
      "original_heading": "References",
      "kind": "references",
      "status": "identified_non_substantive",
      "html_anchor": "references",
      "pdf_pages": [
        11,
        12,
        13
      ],
      "source_spans": [
        {
          "path": "main.tex",
          "lines": [
            80,
            81
          ],
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        },
        {
          "path": "references.bib",
          "source_archive_url": "https://arxiv.org/src/2604.26505v1"
        }
      ],
      "evidence": [],
      "limitations": []
    }
  ],
  "evidence": [
    {
      "key": "eq-1",
      "kind": "equation",
      "original_number": "1",
      "pdf_page": 5,
      "source_path": "content/03_methodology.tex",
      "source_archive_url": "https://arxiv.org/src/2604.26505v1",
      "source_lines": [
        56,
        59
      ],
      "exact_tex": "\\alpha = \\frac{ \\max{(\\mathbf{X}) - \\min{(\\mathbf{X})}}}{255}\\;,\n   \\beta = -\\mathrm{round}(\\frac{\\min{(\\mathbf{X})}}{\\alpha}).",
      "required_macros": {},
      "display_path": "assets/equation-1.png",
      "transformation": {
        "type": "pdf_crop",
        "page": 5,
        "units": "points",
        "box": [
          190,
          665,
          545,
          698
        ],
        "scale": 5
      },
      "rights_basis": "CC BY 4.0; https://creativecommons.org/licenses/by/4.0/",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=5"
    },
    {
      "key": "eq-2",
      "kind": "equation",
      "original_number": "2",
      "pdf_page": 6,
      "source_path": "content/03_methodology.tex",
      "source_archive_url": "https://arxiv.org/src/2604.26505v1",
      "source_lines": [
        64,
        66
      ],
      "exact_tex": "\\mathbf{X}_q = \\mathrm{clip}\\left( \\mathrm{round}( \\frac{\\mathbf{X}}{\\alpha} + \\beta),\\, 0,\\, 255 \\right)",
      "required_macros": {},
      "display_path": "assets/equation-2.png",
      "transformation": {
        "type": "pdf_crop",
        "page": 6,
        "units": "points",
        "box": [
          213,
          87,
          545,
          120
        ],
        "scale": 5
      },
      "rights_basis": "CC BY 4.0; https://creativecommons.org/licenses/by/4.0/",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=6"
    },
    {
      "key": "eq-3",
      "kind": "equation",
      "original_number": "3",
      "pdf_page": 6,
      "source_path": "content/03_methodology.tex",
      "source_archive_url": "https://arxiv.org/src/2604.26505v1",
      "source_lines": [
        91,
        93
      ],
      "exact_tex": "\\Delta_1(t) = \\|\\text{logits}(a_1, s_1) - \\text{logits}(a_1, [t^{(j)}])\\|_2.",
      "required_macros": {},
      "display_path": "assets/equation-3.png",
      "transformation": {
        "type": "pdf_crop",
        "page": 6,
        "units": "points",
        "box": [
          224,
          458,
          545,
          481
        ],
        "scale": 5
      },
      "rights_basis": "CC BY 4.0; https://creativecommons.org/licenses/by/4.0/",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=6"
    },
    {
      "key": "eq-4",
      "kind": "equation",
      "original_number": "4",
      "pdf_page": 6,
      "source_path": "content/03_methodology.tex",
      "source_archive_url": "https://arxiv.org/src/2604.26505v1",
      "source_lines": [
        96,
        98
      ],
      "exact_tex": "\\hat{s}_1 = \\argmin_{t \\in V} \\Delta_1(t) \\quad \\text{subject to} \\quad \\Delta_1(\\hat{s}_1) < \\epsilon,",
      "required_macros": {
        "argmin": "\\DeclareMathOperator*{\\argmin}{arg\\,min}"
      },
      "display_path": "assets/equation-4.png",
      "transformation": {
        "type": "pdf_crop",
        "page": 6,
        "units": "points",
        "box": [
          220,
          499,
          545,
          527
        ],
        "scale": 5
      },
      "rights_basis": "CC BY 4.0; https://creativecommons.org/licenses/by/4.0/",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=6"
    },
    {
      "key": "eq-5",
      "kind": "equation",
      "original_number": "5",
      "pdf_page": 6,
      "source_path": "content/03_methodology.tex",
      "source_archive_url": "https://arxiv.org/src/2604.26505v1",
      "source_lines": [
        111,
        113
      ],
      "exact_tex": "\\Delta_i(t^{(j)}) = \\|\\text{logits}(a_i, s_i) - \\text{logits}(a_i, t^{(j)})\\|_2.",
      "required_macros": {},
      "display_path": "assets/equation-5.png",
      "transformation": {
        "type": "pdf_crop",
        "page": 6,
        "units": "points",
        "box": [
          220,
          644,
          545,
          666
        ],
        "scale": 5
      },
      "rights_basis": "CC BY 4.0; https://creativecommons.org/licenses/by/4.0/",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=6"
    },
    {
      "key": "eq-6",
      "kind": "equation",
      "original_number": "6",
      "pdf_page": 6,
      "source_path": "content/03_methodology.tex",
      "source_archive_url": "https://arxiv.org/src/2604.26505v1",
      "source_lines": [
        116,
        118
      ],
      "exact_tex": "\\hat{s}_i = t^{(j)} \\quad \\text{if} \\quad \\Delta_i(t^{(j)}) < \\epsilon.",
      "required_macros": {},
      "display_path": "assets/equation-6.png",
      "transformation": {
        "type": "pdf_crop",
        "page": 6,
        "units": "points",
        "box": [
          254,
          685,
          545,
          706
        ],
        "scale": 5
      },
      "rights_basis": "CC BY 4.0; https://creativecommons.org/licenses/by/4.0/",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=6"
    },
    {
      "key": "eq-7",
      "kind": "equation",
      "original_number": "7",
      "pdf_page": 7,
      "source_path": "content/03_methodology.tex",
      "source_archive_url": "https://arxiv.org/src/2604.26505v1",
      "source_lines": [
        136,
        138
      ],
      "exact_tex": "\\text{score}(c_i) = \\|\\text{logits}(a, s) - \\text{logits}(a, c_i)\\|_2.",
      "required_macros": {},
      "display_path": "assets/equation-7.png",
      "transformation": {
        "type": "pdf_crop",
        "page": 7,
        "units": "points",
        "box": [
          210,
          369,
          545,
          386
        ],
        "scale": 5
      },
      "rights_basis": "CC BY 4.0; https://creativecommons.org/licenses/by/4.0/",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=7"
    },
    {
      "key": "eq-8",
      "kind": "equation",
      "original_number": "8",
      "pdf_page": 7,
      "source_path": "content/03_methodology.tex",
      "source_archive_url": "https://arxiv.org/src/2604.26505v1",
      "source_lines": [
        141,
        143
      ],
      "exact_tex": "\\hat{c} = \\argmin_{c_i \\in C} \\text{score}(c_i).",
      "required_macros": {
        "argmin": "\\DeclareMathOperator*{\\argmin}{arg\\,min}"
      },
      "display_path": "assets/equation-8.png",
      "transformation": {
        "type": "pdf_crop",
        "page": 7,
        "units": "points",
        "box": [
          258,
          399,
          545,
          424
        ],
        "scale": 5
      },
      "rights_basis": "CC BY 4.0; https://creativecommons.org/licenses/by/4.0/",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=7"
    },
    {
      "key": "fig-1",
      "kind": "figure",
      "original_number": "1",
      "original_label": "fig:quantization_overview",
      "pdf_page": 2,
      "source_path": "content/01_introduction.tex",
      "source_lines": [
        13,
        18
      ],
      "exact_caption_tex": "Overview of the per-tensor dynamic activation quantization side channel. A victim and an adversary's inputs are batched together in a shared forward pass. During per-tensor dynamic quantization, a global scale factor is computed from the combined activations of both inputs, causing the victim's data to influence the quantization applied to the adversary's activations. By observing perturbations in its own output logits, the adversary can infer properties of the victim's input.",
      "original_assets": [
        {
          "path": "content/figures/main-diag.drawio.pdf",
          "sha256": "cc0c35c0a7d1174397e2d4cf3336a0c772b43334bcace30f3026152c58562a1a"
        }
      ],
      "display_path": "assets/figure-1.png",
      "transformation": {
        "type": "source_pdf_rasterization",
        "page": 1,
        "units": "points",
        "box": null,
        "scale": 2.5
      },
      "rights_basis": "CC BY 4.0; https://creativecommons.org/licenses/by/4.0/",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=2"
    },
    {
      "key": "fig-2",
      "kind": "figure",
      "original_number": "2",
      "original_label": "fig:position-efficiency",
      "pdf_page": 9,
      "source_path": "content/figures/token_position_scaling.tex",
      "source_lines": [
        1,
        98
      ],
      "exact_caption_tex": "Query efficiency improves dramatically with position as autoregressive context accumulates. The first token (no context) requires thousands of queries, while subsequent tokens leverage language priors to narrow the search space. TinyStories-1M shows the strongest efficiency gain on in-distribution data, while general-purpose models (Pythia, SmolLM2) maintain consistent mid-range efficiency. All models vastly outperform random search baseline ($\\sim$25,000 queries).",
      "original_assets": [
        {
          "path": "content/figures/token_position_scaling.tex",
          "sha256": "8e8a7e72a740ab0530d14893279cf6a17b3efbbbb736105822f7596f7101815e"
        }
      ],
      "display_path": "assets/figure-2.png",
      "transformation": {
        "type": "pdf_crop",
        "page": 9,
        "units": "points",
        "box": [
          94,
          69,
          515,
          228
        ],
        "scale": 4
      },
      "rights_basis": "CC BY 4.0; https://creativecommons.org/licenses/by/4.0/",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=9"
    },
    {
      "key": "fig-3",
      "kind": "figure",
      "original_number": "3",
      "original_label": "fig:class_scale_analysis",
      "pdf_page": 10,
      "source_path": "content/04_evaluation.tex",
      "source_lines": [
        98,
        109
      ],
      "exact_caption_tex": "\\textbf{From left to right:} Analysis of a simple 3-layer CNN, Resnet18, and Resnet50's per-layer dynamic quantization scales aggregated together. \\textbf{Upper row:} 2D t-SNE projection of each test sample's per-layer quantization scales, colored by true class. Distinct clusters indicate that different classes induce separable quantization behavior across layers. \\textbf{Lower row:} For each class, the average proportion of a sample's 10 nearest neighbors that belong to the same class, measured by similarity of per-layer quantization scales. Bars above the random baseline (red) indicate that samples with similar quantization profiles tend to share a class.",
      "original_assets": [
        {
          "path": "content/figures/simple_tsne_comb_scale.pdf",
          "sha256": "619984fa701e34db9dc5770a1476411eec5fa8e3c98c5b23f2f6d3670e40ba1e"
        },
        {
          "path": "content/figures/resnet18_tsne_comb_scale.pdf",
          "sha256": "16e2da924e794695660d7fc568609eb503146930799f5464c3c53ce85301065a"
        },
        {
          "path": "content/figures/resnet50_tsne_comb_scale.pdf",
          "sha256": "a8016cdbe0c9394f407f91bd90ea07e240191201777dfd557d8c865618663334"
        },
        {
          "path": "content/figures/simple_per_class_neighbor_agreement.pdf",
          "sha256": "444184e9fa177a711f012a8a79384409e6394bdadd5f816733a12aae3de9fcc8"
        },
        {
          "path": "content/figures/resnet18_per_class_neighbor_agreement.pdf",
          "sha256": "976f7a06a040bfb9ba63dc9e620d24b8666bb149707aeabf494ba7034888a23e"
        },
        {
          "path": "content/figures/resnet50_per_class_neighbor_agreement.pdf",
          "sha256": "110d4d7ef89ea633f89ceb1e8920f2633ec162efb531d3c4962ecfe2671bc077"
        }
      ],
      "display_path": "assets/figure-3.png",
      "transformation": {
        "type": "pdf_crop",
        "page": 10,
        "units": "points",
        "box": [
          68,
          66,
          545,
          248
        ],
        "scale": 4
      },
      "rights_basis": "CC BY 4.0; https://creativecommons.org/licenses/by/4.0/",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=10"
    },
    {
      "key": "fig-4",
      "kind": "figure",
      "original_number": "4",
      "original_label": "fig:llama_logprobs",
      "pdf_page": 11,
      "source_path": "content/05_discussion.tex",
      "source_lines": [
        4,
        9
      ],
      "exact_caption_tex": "Llama Log prob analysis of the first 3 output tokens after querying \"Hello my name is\" 20 times with temperature 0 in OpenRouter with provider Crusoe. ",
      "original_assets": [
        {
          "path": "content/figures/llama_logprobs_analysis_new3.pdf",
          "sha256": "3b29a3ed7c6579aa32a52094756bd64b5f1c637e5973caca929ab2bfe7852a2e"
        }
      ],
      "display_path": "assets/figure-4.png",
      "transformation": {
        "type": "source_pdf_rasterization",
        "page": 1,
        "units": "points",
        "box": null,
        "scale": 2.5
      },
      "rights_basis": "CC BY 4.0; https://creativecommons.org/licenses/by/4.0/",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=11"
    },
    {
      "key": "table-1",
      "kind": "table",
      "original_number": "1",
      "pdf_page": 4,
      "display_path": "assets/table-1.png",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=4",
      "transformation": {
        "type": "original_pdf_crop_and_accessible_html_data_transcription"
      },
      "rights_basis": "CC BY 4.0"
    },
    {
      "key": "table-2",
      "kind": "table",
      "original_number": "2",
      "pdf_page": 8,
      "display_path": "assets/table-2.png",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=8",
      "transformation": {
        "type": "original_pdf_crop_and_accessible_html_data_transcription"
      },
      "rights_basis": "CC BY 4.0"
    },
    {
      "key": "table-3",
      "kind": "table",
      "original_number": "3",
      "pdf_page": 8,
      "display_path": "assets/table-3.png",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=8",
      "transformation": {
        "type": "original_pdf_crop_and_accessible_html_data_transcription"
      },
      "rights_basis": "CC BY 4.0"
    },
    {
      "key": "table-4",
      "kind": "table",
      "original_number": "4",
      "pdf_page": 9,
      "display_path": "assets/table-4.png",
      "source_link": "https://arxiv.org/pdf/2604.26505v1#page=9",
      "transformation": {
        "type": "original_pdf_crop_and_accessible_html_data_transcription"
      },
      "rights_basis": "CC BY 4.0"
    }
  ],
  "unread": [],
  "interpretation_notes": [
    "Metadata title and PDF/TeX title differ within the same v1; title follows current arXiv metadata.",
    "This v1 contains no appendix or separate Conclusion section.",
    "Token recovery accuracy/query means exclude incomplete runs.",
    "Framework configurations are the paper’s historical survey, not independently verified current-version claims.",
    "Chinese commentary, criticism and explanatory pseudocode are newly written. Original equations are rendered PDF crops, not re-typeset.",
    "No original research code, TeX or submission build scripts were executed."
  ]
}