{
  "schema_version": 2,
  "title": "Reproduction: Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training",
  "emoji": "🎯",
  "space_id": "amkkk/repro-spurious-correlation-preference-learning",
  "paper": null,
  "tags": [
    "icml2026-repro",
    "paper-Hpfybj9wkd"
  ],
  "updated_at": "2026-08-01T22:38:06+00:00",
  "root": {
    "slug": "index",
    "title": "Reproduction: Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training",
    "file": "pages/index.md",
    "children": [
      {
        "slug": "executive-summary",
        "title": "Executive summary",
        "file": "pages/executive-summary/page.md",
        "children": []
      },
      {
        "slug": "claim-1-theorem-4-1-spurious-equilibrium",
        "title": "Claim 1: Theorem 4.1 — spurious parameters at the DPO equilibrium",
        "file": "pages/claim-1-theorem-4-1-spurious-equilibrium/page.md",
        "children": []
      },
      {
        "slug": "claim-2-theorem-5-3-deployment-suboptimality",
        "title": "Claim 2: Theorem 5.3 — deployment suboptimality decomposition",
        "file": "pages/claim-2-theorem-5-3-deployment-suboptimality/page.md",
        "children": []
      },
      {
        "slug": "claim-3-theorem-6-2-tie-training-ratio",
        "title": "Claim 3: Theorem 6.2 — tie-training reduction ratio",
        "file": "pages/claim-3-theorem-6-2-tie-training-ratio/page.md",
        "children": []
      },
      {
        "slug": "claim-4-neural-network-spurious-gap",
        "title": "Claim 4: Neural network experiments — spurious gap / adversarial accuracy",
        "file": "pages/claim-4-neural-network-spurious-gap/page.md",
        "children": []
      },
      {
        "slug": "claim-5-llm-hotel-preferences",
        "title": "Claim 5: LLM synthetic Hotel Preferences benchmark",
        "file": "pages/claim-5-llm-hotel-preferences/page.md",
        "children": []
      },
      {
        "slug": "conclusion",
        "title": "Conclusion",
        "file": "pages/conclusion/page.md",
        "children": []
      }
    ]
  },
  "traces": [],
  "workspace": {
    "file": "workspace.json",
    "file_count": 0,
    "total_size": 0,
    "bucket_id": null
  },
  "agent_view_tokens": 7577,
  "trace_view_tokens": 10,
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  "revision": "041a353422f93119092f"
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