{
  "schema_version": 1,
  "title": "Repro - How Far Can LLM Agents Reason with Tables? Benchmarking Multi-Turn Agentic Table Question Answering in the Wild",
  "emoji": "📊",
  "space_id": "Sor0ush/repro-tableagent-icml2026",
  "paper": {
    "openreview_id": "5yZAgkjGQ0",
    "paper_number": 33174,
    "title": "How Far Can LLM Agents Reason with Tables? Benchmarking Multi-Turn Agentic Table Question Answering in the Wild"
  },
  "tags": [
    "icml2026-repro",
    "paper-5yZAgkjGQ0"
  ],
  "updated_at": "2026-07-16T04:59:14+00:00",
  "root": {
    "slug": "index",
    "title": "Repro - How Far Can LLM Agents Reason with Tables? Benchmarking Multi-Turn Agentic Table Question Answering in the Wild",
    "file": "pages/index.md",
    "children": [
      {
        "slug": "claim-1-1310-dialogues-and-2275-tables",
        "title": "Claim 1: 1310 dialogues and 2275 tables",
        "file": "pages/claim-1-1310-dialogues-and-2275-tables/page.md",
        "children": []
      },
      {
        "slug": "claim-2-gemini-3-pro-preview-53-4-ic",
        "title": "Claim 2: Gemini-3-Pro-Preview 53.4% IC",
        "file": "pages/claim-2-gemini-3-pro-preview-53-4-ic/page.md",
        "children": []
      },
      {
        "slug": "conclusion",
        "title": "Conclusion",
        "file": "pages/conclusion/page.md",
        "children": []
      }
    ]
  },
  "agent_view_tokens": 1487,
  "revision": "1784177954768111797"
}