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      "code": null,
      "public_data": "https://huggingface.co/FUSE-verifiers",
      "low_cost_component_opportunity": "预生成分数上的无标签集成本身可CPU运行；应拒绝gold标签入估计器，检查条件独立/退化，不冒称新模型生成能力",
      "full_experiment_disposition": "成本过高，不复现（审查 DONE）",
      "cost_basis": "Appendix F 原始HLE/IMO生成/验证使用8×H100-80GB；离线分数集成本身论文明确无需GPU。因此只延期完整生成实验，不能用8H100否定低成本集成算法。",
      "evidence_locations": [
        "arXiv abs Submission history v1",
        "HTML authors",
        "§2.1-2.3",
        "Appendix D",
        "Appendix F Compute requirements",
        "Appendix G Replicability",
        "https://research.google/pubs/fuse-scaling-verification-with-zero-labeled-data/"
      ],
      "inherited_artifact": "docs/research-audits/oct15-review-closure/google-source-review.json",
      "fulltext_review_completed": true,
      "full_article_read": false,
      "read_scope": "Named method, assumptions, compute and release passages."
    },
    {
      "id": "2602.21103",
      "title": "Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning",
      "source": "https://arxiv.org/html/2602.21103v1",
      "affiliation": "Google (both authors Sanket Badhe / Deep Shah)",
      "date": "2026-02-24T17:03:21Z",
      "current_status": "official_historical_pool_not_catalogued",
      "decision": "priority_historical_omission_confirmed",
      "method": "训练标签用于教师抽象指令，按语义向量DBSCAN去噪聚类、教师合成规则，再用训练误差做冲突修订；validation用于收敛，最终只向学生提供规则。",
      "code_status": "所读原文未找到作者实现仓库",
      "code": null,
      "low_cost_component_opportunity": "DBSCAN和训练/验证/测试隔离的规则编译接口可低成本验证；真实教师抽象和冲突解决不可由手写规则替代",
      "full_experiment_disposition": "成本过高，不复现（审查 DONE）",
      "cost_basis": "§4 Gemini3Flash提取、Gemini3Pro规则合成/冲突修复、GeminiEmbedding；Gemma3-4B/Gemini2Flash在多个数据集多轮推理。闭源API全流程成本无法廉价固定，按预算门槛关闭完整实验，不下载大模型探成本。",
      "evidence_locations": [
        "arXiv abs Submission history v1; v2 2026-06-02 identity only",
        "HTML authors",
        "§3.1-3.4",
        "§4.1 Setup",
        "Appendix C clustering",
        "https://research.google/pubs/prompt-level-distillation-a-non-parametric-alternative-to-model-fine-tuning-for-efficient-reasoning/"
      ],
      "inherited_artifact": "docs/research-audits/oct15-review-closure/google-source-review.json",
      "fulltext_review_completed": true,
      "full_article_read": false,
      "read_scope": "Named method/setup passages in v1, not all v2 contents."
    }
  ],
  "recent_official_reverse_checks": [
    {
      "url": "https://research.google/pubs/",
      "checked_on": "2026-10-20",
      "read_scope": "First rendered page (15 records), current directory count 421 for 2026; not all pages re-fetched",
      "result": "SA-DPO and other historical publications still prominently listed; directory year ordering is not original publication order."
    },
    {
      "url": "https://deepmind.google/research/publications/",
      "checked_on": "2026-10-20",
      "read_scope": "First page lists all 27 2026-dated records in the existing 265-record directory snapshot",
      "result": "No later listing than 2026-09-16 on this rendered directory; this is not evidence there are no October DeepMind papers."
    }
  ],
  "limitations": [
    "102 Google and 27 DeepMind historical source rows were fully enumerated and reconciled, not all read in full text.",
    "The three date-only Google affiliation confirmations use actual author blocks; author-name matches only helped recall and must not be used as final affiliation proof.",
    "7 substantive reviews read named algorithm, evaluation, cost and release passages, not each complete article or every revision.",
    "Unconfirmed entries remain nonblocking metadata-only archives, not substantive review DONE, implemented or automatically queued.",
    "No global exhaustive Google/Meta claim and no review-watermark advance; Meta review belongs to the independent parent task."
  ]
}