Add files using upload-large-folder tool

2002.12229/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2002.12229",
+  "month": "2020_02",
+  "year": 2020,
+  "conference": "NEURIPS",
+  "title": "Woodbury Transformations for Deep Generative Flows",
+  "arxiv_url": "https://arxiv.org/abs/2002.12229",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_02/main_diagram_database/2002.12229",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_02/tex_files_extracted/2002.12229",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_02/main_diagram_database/2002.12229/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_02/main_diagram_database/2002.12229/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_02/main_diagram_database/2002.12229/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2002.12229/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2002.12229/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2002.12229/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2002.12229/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2002.12229/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2002.12229/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2004.00048/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2004.00048",
+  "month": "2020_04",
+  "year": 2021,
+  "conference": "NEURIPS",
+  "title": "Mimicking Evolution with Reinforcement Learning",
+  "arxiv_url": "https://arxiv.org/abs/2004.00048",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_04/main_diagram_database/2004.00048",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_04/tex_files_extracted/2004.00048",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_04/main_diagram_database/2004.00048/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_04/main_diagram_database/2004.00048/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_04/main_diagram_database/2004.00048/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2004.00048/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2004.00048/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2004.00048/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2004.00048/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2004.00048/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2004.00048/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2006.08085/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2006.08085",
+  "month": "2020_06",
+  "year": 2021,
+  "conference": "ICML",
+  "title": "Optimal Complexity in Decentralized Training",
+  "arxiv_url": "https://arxiv.org/abs/2006.08085",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.08085",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/tex_files_extracted/2006.08085",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.08085/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.08085/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.08085/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.08085/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.08085/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.08085/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.08085/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.08085/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.08085/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2006.10562/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2006.10562",
+  "month": "2020_06",
+  "year": 2021,
+  "conference": "ICLR",
+  "title": "Uncertainty in Gradient Boosting via Ensembles",
+  "arxiv_url": "https://arxiv.org/abs/2006.10562",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.10562",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/tex_files_extracted/2006.10562",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.10562/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.10562/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.10562/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.10562/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.10562/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.10562/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.10562/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.10562/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.10562/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2006.11132/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2006.11132",
+  "month": "2020_06",
+  "year": 2020,
+  "conference": "NEURIPS",
+  "title": "Deep Transformation-Invariant Clustering",
+  "arxiv_url": "https://arxiv.org/abs/2006.11132",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.11132",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/tex_files_extracted/2006.11132",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.11132/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.11132/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.11132/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.11132/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.11132/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.11132/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.11132/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.11132/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.11132/latex_source"
+  },
+  "status": {
+    "copy_drawio": "ok",
+    "copy_png": "ok",
+    "diagram_pdf": "ok",
+    "intro_method": "ok",
+    "paper_pdf": "ok",
+    "latex": "ok"
+  }
+}

2006.12745/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2006.12745",
+  "month": "2020_06",
+  "year": 2020,
+  "conference": "NEURIPS",
+  "title": "Learning Physical Constraints with Neural Projections",
+  "arxiv_url": "https://arxiv.org/abs/2006.12745",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.12745",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/tex_files_extracted/2006.12745",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.12745/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.12745/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_06/main_diagram_database/2006.12745/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.12745/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.12745/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.12745/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.12745/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.12745/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2006.12745/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2007.06702/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2007.06702",
+  "month": "2020_07",
+  "year": 2021,
+  "conference": "AAAI",
+  "title": "Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning",
+  "arxiv_url": "https://arxiv.org/abs/2007.06702",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_07/main_diagram_database/2007.06702",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_07/tex_files_extracted/2007.06702",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_07/main_diagram_database/2007.06702/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_07/main_diagram_database/2007.06702/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_07/main_diagram_database/2007.06702/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2007.06702/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2007.06702/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2007.06702/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2007.06702/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2007.06702/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2007.06702/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2008.00441/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2008.00441",
+  "month": "2020_08",
+  "year": 2020,
+  "conference": "AAAI",
+  "title": "Relation Extraction with Convolutional Network over Learnable Syntax-Transport Graph",
+  "arxiv_url": "https://arxiv.org/abs/2008.00441",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_08/main_diagram_database/2008.00441",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_08/tex_files_extracted/2008.00441",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_08/main_diagram_database/2008.00441/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_08/main_diagram_database/2008.00441/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_08/main_diagram_database/2008.00441/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2008.00441/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2008.00441/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2008.00441/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2008.00441/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2008.00441/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2008.00441/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2009.08566/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2009.08566",
+  "month": "2020_09",
+  "year": 2020,
+  "conference": "EMNLP",
+  "title": "MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering",
+  "arxiv_url": "https://arxiv.org/abs/2009.08566",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_09/main_diagram_database/2009.08566",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_09/tex_files_extracted/2009.08566",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_09/main_diagram_database/2009.08566/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_09/main_diagram_database/2009.08566/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_09/main_diagram_database/2009.08566/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2009.08566/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2009.08566/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2009.08566/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2009.08566/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2009.08566/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2009.08566/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2009.10007/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2009.10007",
+  "month": "2020_09",
+  "year": 2022,
+  "conference": "IJCAI",
+  "title": "Learning  Realistic Patterns from Visually Unrealistic Stimuli: Generalization and Data Anonymization (Extended Abstract)",
+  "arxiv_url": "https://arxiv.org/abs/2009.10007",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_09/main_diagram_database/2009.10007",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_09/tex_files_extracted/2009.10007",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_09/main_diagram_database/2009.10007/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_09/main_diagram_database/2009.10007/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_09/main_diagram_database/2009.10007/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2009.10007/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2009.10007/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2009.10007/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2009.10007/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2009.10007/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2009.10007/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2010.12007/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2010.12007",
+  "month": "2020_10",
+  "year": 2020,
+  "conference": "NEURIPS",
+  "title": "PRANK: motion Prediction based on RANKing",
+  "arxiv_url": "https://arxiv.org/abs/2010.12007",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_10/main_diagram_database/2010.12007",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_10/tex_files_extracted/2010.12007",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_10/main_diagram_database/2010.12007/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_10/main_diagram_database/2010.12007/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_10/main_diagram_database/2010.12007/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2010.12007/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2010.12007/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2010.12007/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2010.12007/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2010.12007/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2010.12007/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2011.00105/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2011.00105",
+  "month": "2020_11",
+  "year": 2020,
+  "conference": "EMNLP",
+  "title": "Learning Structured Representations of Entity Names using Active Learning and Weak Supervision",
+  "arxiv_url": "https://arxiv.org/abs/2011.00105",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/main_diagram_database/2011.00105",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/tex_files_extracted/2011.00105",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/main_diagram_database/2011.00105/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/main_diagram_database/2011.00105/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/main_diagram_database/2011.00105/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.00105/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.00105/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.00105/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.00105/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.00105/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.00105/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2011.01549/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2011.01549",
+  "month": "2020_11",
+  "year": 2020,
+  "conference": "EMNLP",
+  "title": "DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks",
+  "arxiv_url": "https://arxiv.org/abs/2011.01549",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/main_diagram_database/2011.01549",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/tex_files_extracted/2011.01549",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/main_diagram_database/2011.01549/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/main_diagram_database/2011.01549/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/main_diagram_database/2011.01549/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.01549/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.01549/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.01549/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.01549/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.01549/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.01549/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2011.08939/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2011.08939",
+  "month": "2020_11",
+  "year": 2021,
+  "conference": "CVPR",
+  "title": "Dual-Stream Multiple Instance Learning Network for Whole Slide Image Classification With Self-Supervised Contrastive Learning",
+  "arxiv_url": "https://arxiv.org/abs/2011.08939",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/main_diagram_database/2011.08939",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/tex_files_extracted/2011.08939",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/main_diagram_database/2011.08939/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/main_diagram_database/2011.08939/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_11/main_diagram_database/2011.08939/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.08939/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.08939/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.08939/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.08939/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.08939/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2011.08939/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2012.07489/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2012.07489",
+  "month": "2020_12",
+  "year": 2021,
+  "conference": "ICCV",
+  "title": "Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU",
+  "arxiv_url": "https://arxiv.org/abs/2012.07489",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_12/main_diagram_database/2012.07489",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_12/tex_files_extracted/2012.07489",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_12/main_diagram_database/2012.07489/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_12/main_diagram_database/2012.07489/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_12/main_diagram_database/2012.07489/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2012.07489/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2012.07489/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2012.07489/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2012.07489/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2012.07489/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2012.07489/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2012.14022/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2012.14022",
+  "month": "2020_12",
+  "year": 2021,
+  "conference": "AAAI",
+  "title": "ALP-KD: Attention-Based Layer Projection for Knowledge Distillation",
+  "arxiv_url": "https://arxiv.org/abs/2012.14022",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_12/main_diagram_database/2012.14022",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_12/tex_files_extracted/2012.14022",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_12/main_diagram_database/2012.14022/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_12/main_diagram_database/2012.14022/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2020_12/main_diagram_database/2012.14022/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2012.14022/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2012.14022/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2012.14022/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2012.14022/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2012.14022/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2012.14022/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2102.00405/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2102.00405",
+  "month": "2021_02",
+  "year": 2022,
+  "conference": "NAACL",
+  "title": "DadmaTools: Natural Language Processing Toolkit for Persian Language",
+  "arxiv_url": "https://arxiv.org/abs/2102.00405",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/main_diagram_database/2102.00405",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/tex_files_extracted/2102.00405",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/main_diagram_database/2102.00405/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/main_diagram_database/2102.00405/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/main_diagram_database/2102.00405/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.00405/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.00405/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.00405/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.00405/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.00405/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.00405/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2102.05714/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2102.05714",
+  "month": "2021_02",
+  "year": 2021,
+  "conference": "AAAI",
+  "title": "Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation",
+  "arxiv_url": "https://arxiv.org/abs/2102.05714",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/main_diagram_database/2102.05714",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/tex_files_extracted/2102.05714",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/main_diagram_database/2102.05714/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/main_diagram_database/2102.05714/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/main_diagram_database/2102.05714/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.05714/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.05714/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.05714/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.05714/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.05714/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.05714/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2102.09850/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2102.09850",
+  "month": "2021_02",
+  "year": 2022,
+  "conference": "ICLR",
+  "title": "Model-Invariant State Abstractions for Model-Based Reinforcement Learning",
+  "arxiv_url": "https://arxiv.org/abs/2102.09850",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/main_diagram_database/2102.09850",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/tex_files_extracted/2102.09850",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/main_diagram_database/2102.09850/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/main_diagram_database/2102.09850/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_02/main_diagram_database/2102.09850/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.09850/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.09850/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.09850/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.09850/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.09850/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2102.09850/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2103.00180/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2103.00180",
+  "month": "2021_03",
+  "year": 2024,
+  "conference": "ICML",
+  "title": "Incorporating probabilistic domain knowledge into deep multiple instance learning",
+  "arxiv_url": "https://arxiv.org/abs/2103.00180",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.00180",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/tex_files_extracted/2103.00180",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.00180/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.00180/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.00180/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.00180/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.00180/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.00180/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.00180/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.00180/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.00180/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2103.02062/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2103.02062",
+  "month": "2021_03",
+  "year": 2021,
+  "conference": "ICML",
+  "title": "Variance Reduced Training with Stratified Sampling for Forecasting Models",
+  "arxiv_url": "https://arxiv.org/abs/2103.02062",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.02062",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/tex_files_extracted/2103.02062",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.02062/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.02062/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.02062/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.02062/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.02062/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.02062/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.02062/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.02062/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.02062/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2103.03501/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2103.03501",
+  "month": "2021_03",
+  "year": 2021,
+  "conference": "CVPR",
+  "title": "Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach",
+  "arxiv_url": "https://arxiv.org/abs/2103.03501",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.03501",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/tex_files_extracted/2103.03501",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.03501/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.03501/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.03501/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.03501/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.03501/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.03501/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.03501/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.03501/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.03501/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2103.15812/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2103.15812",
+  "month": "2021_03",
+  "year": 2022,
+  "conference": "ICLR",
+  "title": "LatentKeypointGAN: Controlling GANs via Latent Keypoints",
+  "arxiv_url": "https://arxiv.org/abs/2103.15812",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.15812",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/tex_files_extracted/2103.15812",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.15812/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.15812/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_03/main_diagram_database/2103.15812/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.15812/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.15812/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.15812/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.15812/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.15812/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2103.15812/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2104.00369/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2104.00369",
+  "month": "2021_04",
+  "year": 2022,
+  "conference": "AAAI",
+  "title": "Generation-Focused Table-Based Intermediate Pre-training for Free-Form Question Answering",
+  "arxiv_url": "https://arxiv.org/abs/2104.00369",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.00369",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/tex_files_extracted/2104.00369",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.00369/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.00369/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.00369/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.00369/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.00369/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.00369/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.00369/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.00369/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.00369/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2104.07098/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2104.07098",
+  "month": "2021_04",
+  "year": 2021,
+  "conference": "CVPR",
+  "title": "StEP: Style-Based Encoder Pre-Training for Multi-Modal Image Synthesis",
+  "arxiv_url": "https://arxiv.org/abs/2104.07098",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.07098",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/tex_files_extracted/2104.07098",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.07098/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.07098/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.07098/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.07098/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.07098/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.07098/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.07098/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.07098/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.07098/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2104.07186/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2104.07186",
+  "month": "2021_04",
+  "year": 2021,
+  "conference": "NAACL",
+  "title": "COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List",
+  "arxiv_url": "https://arxiv.org/abs/2104.07186",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.07186",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/tex_files_extracted/2104.07186",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.07186/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.07186/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.07186/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.07186/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.07186/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.07186/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.07186/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.07186/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.07186/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2104.08215/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2104.08215",
+  "month": "2021_04",
+  "year": 2021,
+  "conference": "AAAI",
+  "title": "Training Binary Neural Network without Batch Normalization for Image Super-Resolution",
+  "arxiv_url": "https://arxiv.org/abs/2104.08215",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.08215",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/tex_files_extracted/2104.08215",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.08215/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.08215/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_04/main_diagram_database/2104.08215/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.08215/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.08215/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.08215/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.08215/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.08215/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2104.08215/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2105.09437/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2105.09437",
+  "month": "2021_05",
+  "year": 2021,
+  "conference": "ICCV",
+  "title": "End-to-End Unsupervised Document Image Blind Denoising",
+  "arxiv_url": "https://arxiv.org/abs/2105.09437",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/main_diagram_database/2105.09437",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/tex_files_extracted/2105.09437",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/main_diagram_database/2105.09437/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/main_diagram_database/2105.09437/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/main_diagram_database/2105.09437/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.09437/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.09437/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.09437/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.09437/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.09437/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.09437/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2105.09821/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2105.09821",
+  "month": "2021_05",
+  "year": 2021,
+  "conference": "IJCAI",
+  "title": "DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization",
+  "arxiv_url": "https://arxiv.org/abs/2105.09821",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/main_diagram_database/2105.09821",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/tex_files_extracted/2105.09821",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/main_diagram_database/2105.09821/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/main_diagram_database/2105.09821/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/main_diagram_database/2105.09821/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.09821/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.09821/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.09821/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.09821/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.09821/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.09821/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2105.13022/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2105.13022",
+  "month": "2021_05",
+  "year": 2021,
+  "conference": "ACL",
+  "title": "Adaptive Nearest Neighbor Machine Translation",
+  "arxiv_url": "https://arxiv.org/abs/2105.13022",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/main_diagram_database/2105.13022",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/tex_files_extracted/2105.13022",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/main_diagram_database/2105.13022/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/main_diagram_database/2105.13022/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_05/main_diagram_database/2105.13022/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.13022/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.13022/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.13022/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.13022/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.13022/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2105.13022/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2106.01342/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2106.01342",
+  "month": "2021_06",
+  "year": 2022,
+  "conference": "ICLR",
+  "title": "SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training",
+  "arxiv_url": "https://arxiv.org/abs/2106.01342",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.01342",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/tex_files_extracted/2106.01342",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.01342/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.01342/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.01342/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.01342/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.01342/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.01342/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.01342/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.01342/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.01342/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2106.02740/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2106.02740",
+  "month": "2021_06",
+  "year": 2022,
+  "conference": "CVPR",
+  "title": "ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes",
+  "arxiv_url": "https://arxiv.org/abs/2106.02740",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.02740",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/tex_files_extracted/2106.02740",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.02740/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.02740/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.02740/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.02740/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.02740/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.02740/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.02740/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.02740/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.02740/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2106.04420/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2106.04420",
+  "month": "2021_06",
+  "year": 2022,
+  "conference": "ICLR",
+  "title": "Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future",
+  "arxiv_url": "https://arxiv.org/abs/2106.04420",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.04420",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/tex_files_extracted/2106.04420",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.04420/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.04420/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.04420/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.04420/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.04420/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.04420/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.04420/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.04420/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.04420/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2106.05087/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2106.05087",
+  "month": "2021_06",
+  "year": 2022,
+  "conference": "ICLR",
+  "title": "Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL",
+  "arxiv_url": "https://arxiv.org/abs/2106.05087",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.05087",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/tex_files_extracted/2106.05087",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.05087/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.05087/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.05087/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.05087/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.05087/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.05087/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.05087/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.05087/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.05087/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2106.08827/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2106.08827",
+  "month": "2021_06",
+  "year": 2022,
+  "conference": "CVPR",
+  "title": "JRDB-Act: A Large-Scale Dataset for Spatio-Temporal Action, Social Group and Activity Detection",
+  "arxiv_url": "https://arxiv.org/abs/2106.08827",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.08827",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/tex_files_extracted/2106.08827",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.08827/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.08827/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.08827/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.08827/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.08827/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.08827/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.08827/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.08827/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.08827/latex_source"
+  },
+  "status": {
+    "copy_drawio": "ok",
+    "copy_png": "ok",
+    "diagram_pdf": "ok",
+    "intro_method": "ok",
+    "paper_pdf": "ok",
+    "latex": "ok"
+  }
+}

2106.12108/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2106.12108",
+  "month": "2021_06",
+  "year": 2021,
+  "conference": "ICML",
+  "title": "Near-Optimal Linear Regression under Distribution Shift",
+  "arxiv_url": "https://arxiv.org/abs/2106.12108",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.12108",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/tex_files_extracted/2106.12108",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.12108/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.12108/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.12108/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.12108/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.12108/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.12108/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.12108/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.12108/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.12108/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2106.13195/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2106.13195",
+  "month": "2021_06",
+  "year": 2022,
+  "conference": "ICLR",
+  "title": "FitVid: High-Capacity Pixel-Level Video Prediction",
+  "arxiv_url": "https://arxiv.org/abs/2106.13195",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.13195",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/tex_files_extracted/2106.13195",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.13195/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.13195/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_06/main_diagram_database/2106.13195/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.13195/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.13195/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.13195/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.13195/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.13195/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2106.13195/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2107.11879/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2107.11879",
+  "month": "2021_07",
+  "year": 2022,
+  "conference": "AAAI",
+  "title": "Hybrid Autoregressive Inference for Scalable Multi-Hop Explanation Regeneration",
+  "arxiv_url": "https://arxiv.org/abs/2107.11879",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_07/main_diagram_database/2107.11879",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_07/tex_files_extracted/2107.11879",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_07/main_diagram_database/2107.11879/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_07/main_diagram_database/2107.11879/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_07/main_diagram_database/2107.11879/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2107.11879/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2107.11879/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2107.11879/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2107.11879/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2107.11879/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2107.11879/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2108.02180/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2108.02180",
+  "month": "2021_08",
+  "year": 2022,
+  "conference": "ACMMM",
+  "title": "Ordered Attention for Coherent Visual Storytelling",
+  "arxiv_url": "https://arxiv.org/abs/2108.02180",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_08/main_diagram_database/2108.02180",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_08/tex_files_extracted/2108.02180",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_08/main_diagram_database/2108.02180/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_08/main_diagram_database/2108.02180/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_08/main_diagram_database/2108.02180/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2108.02180/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2108.02180/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2108.02180/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2108.02180/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2108.02180/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2108.02180/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2108.08815/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2108.08815",
+  "month": "2021_08",
+  "year": 2021,
+  "conference": "ICCV",
+  "title": "Click To Move: Controlling Video Generation With Sparse Motion",
+  "arxiv_url": "https://arxiv.org/abs/2108.08815",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_08/main_diagram_database/2108.08815",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_08/tex_files_extracted/2108.08815",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_08/main_diagram_database/2108.08815/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_08/main_diagram_database/2108.08815/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_08/main_diagram_database/2108.08815/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2108.08815/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2108.08815/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2108.08815/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2108.08815/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2108.08815/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2108.08815/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2109.02832/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2109.02832",
+  "month": "2021_09",
+  "year": 2021,
+  "conference": "ICML",
+  "title": "Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks",
+  "arxiv_url": "https://arxiv.org/abs/2109.02832",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/main_diagram_database/2109.02832",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/tex_files_extracted/2109.02832",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/main_diagram_database/2109.02832/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/main_diagram_database/2109.02832/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/main_diagram_database/2109.02832/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.02832/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.02832/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.02832/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.02832/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.02832/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.02832/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2109.04335/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2109.04335",
+  "month": "2021_09",
+  "year": 2022,
+  "conference": "AAAI",
+  "title": "UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer",
+  "arxiv_url": "https://arxiv.org/abs/2109.04335",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/main_diagram_database/2109.04335",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/tex_files_extracted/2109.04335",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/main_diagram_database/2109.04335/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/main_diagram_database/2109.04335/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/main_diagram_database/2109.04335/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.04335/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.04335/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.04335/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.04335/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.04335/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.04335/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2109.10637/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2109.10637",
+  "month": "2021_09",
+  "year": 2022,
+  "conference": "AAAI",
+  "title": "Facilitating Human-Wildlife Cohabitation through Conflict Prediction",
+  "arxiv_url": "https://arxiv.org/abs/2109.10637",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/main_diagram_database/2109.10637",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/tex_files_extracted/2109.10637",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/main_diagram_database/2109.10637/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/main_diagram_database/2109.10637/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_09/main_diagram_database/2109.10637/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.10637/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.10637/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.10637/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.10637/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.10637/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2109.10637/latex_source"
+  },
+  "status": {
+    "copy_drawio": "ok",
+    "copy_png": "ok",
+    "diagram_pdf": "ok",
+    "intro_method": "ok",
+    "paper_pdf": "ok",
+    "latex": "ok"
+  }
+}

2110.01823/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2110.01823",
+  "month": "2021_10",
+  "year": 2021,
+  "conference": "NEURIPS",
+  "title": "Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations",
+  "arxiv_url": "https://arxiv.org/abs/2110.01823",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.01823",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/tex_files_extracted/2110.01823",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.01823/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.01823/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.01823/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.01823/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.01823/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.01823/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.01823/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.01823/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.01823/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2110.03753/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2110.03753",
+  "month": "2021_10",
+  "year": 2022,
+  "conference": "ICLR",
+  "title": "From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness",
+  "arxiv_url": "https://arxiv.org/abs/2110.03753",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.03753",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/tex_files_extracted/2110.03753",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.03753/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.03753/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.03753/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.03753/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.03753/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.03753/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.03753/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.03753/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.03753/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2110.05448/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2110.05448",
+  "month": "2021_10",
+  "year": 2022,
+  "conference": "ICLR",
+  "title": "Unsupervised Neural Machine Translation with Generative Language Models Only",
+  "arxiv_url": "https://arxiv.org/abs/2110.05448",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.05448",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/tex_files_extracted/2110.05448",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.05448/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.05448/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.05448/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.05448/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.05448/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.05448/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.05448/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.05448/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.05448/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2110.07596/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2110.07596",
+  "month": "2021_10",
+  "year": 2022,
+  "conference": "ACL",
+  "title": "Retrieval-guided Counterfactual Generation for QA",
+  "arxiv_url": "https://arxiv.org/abs/2110.07596",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.07596",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/tex_files_extracted/2110.07596",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.07596/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.07596/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.07596/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.07596/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.07596/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.07596/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.07596/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.07596/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.07596/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2110.14805/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2110.14805",
+  "month": "2021_10",
+  "year": 2021,
+  "conference": "NEURIPS",
+  "title": "Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning",
+  "arxiv_url": "https://arxiv.org/abs/2110.14805",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.14805",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/tex_files_extracted/2110.14805",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.14805/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.14805/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_10/main_diagram_database/2110.14805/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.14805/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.14805/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.14805/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.14805/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.14805/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2110.14805/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2111.00160/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2111.00160",
+  "month": "2021_11",
+  "year": 2023,
+  "conference": "ACL",
+  "title": "DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models",
+  "arxiv_url": "https://arxiv.org/abs/2111.00160",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_11/main_diagram_database/2111.00160",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_11/tex_files_extracted/2111.00160",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_11/main_diagram_database/2111.00160/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_11/main_diagram_database/2111.00160/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_11/main_diagram_database/2111.00160/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2111.00160/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2111.00160/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2111.00160/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2111.00160/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2111.00160/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2111.00160/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}

2111.08919/record.json

@@ -0,0 +1,32 @@
+{
+  "arxiv_id": "2111.08919",
+  "month": "2021_11",
+  "year": 2022,
+  "conference": "CVPR",
+  "title": "EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching",
+  "arxiv_url": "https://arxiv.org/abs/2111.08919",
+  "source": {
+    "paper_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_11/main_diagram_database/2111.08919",
+    "tex_dir": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_11/tex_files_extracted/2111.08919",
+    "paper_md": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_11/main_diagram_database/2111.08919/paper_text/paper.md",
+    "metadata_json": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_11/main_diagram_database/2111.08919/metadata.json",
+    "intro_method_from": "/home/zling/lzl/ICML2026/Build_Dataset/data/2021_11/main_diagram_database/2111.08919/paper_text/paper.md",
+    "intro_method_from_kind": "markdown"
+  },
+  "files": {
+    "main_drawio": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2111.08919/main_diagram/main_diagram.drawio",
+    "main_png": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2111.08919/main_diagram/main_diagram.png",
+    "main_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2111.08919/main_diagram/main_diagram.pdf",
+    "intro_method_md": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2111.08919/paper_text/intro_method.md",
+    "paper_pdf": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2111.08919/paper.pdf",
+    "latex": "/home/zling/lzl/ICML2026/Build_Dataset/dataset/2111.08919/latex_source"
+  },
+  "status": {
+    "copy_drawio": "exists",
+    "copy_png": "exists",
+    "diagram_pdf": "pdf_exists",
+    "intro_method": "exists",
+    "paper_pdf": "exists",
+    "latex": "exists"
+  }
+}