{
  "schema_version": 1,
  "id": "evt-2023-leandojo",
  "slug": "leandojo-retrieval-augmented-theorem-proving",
  "title": {
    "en": "LeanDojo opens a reproducible environment for LLM theorem proving in Lean",
    "zh-CN": "LeanDojo 建立可复现的 Lean 大模型定理证明环境"
  },
  "dates": {
    "event": "2023-06-27",
    "first_public_claim": "2023-06-27",
    "paper_release": "2023-06-27",
    "artifact_release": null,
    "independently_verified": null,
    "added_to_chronicle": "2026-09-10",
    "last_updated": "2026-09-15"
  },
  "event_types": [
    "infrastructure",
    "dataset",
    "system",
    "formalization"
  ],
  "significance": {
    "tier": "H2",
    "rationale": {
      "en": "LeanDojo lowered the reproducibility barrier for learning-based theorem proving by combining programmatic Lean interaction, extracted proof data, a large benchmark and an open retrieval-augmented prover.",
      "zh-CN": "LeanDojo 将可编程 Lean 交互、证明数据抽取、大规模基准和开放的检索增强证明器整合起来，显著降低了学习式定理证明的复现门槛。"
    }
  },
  "mathematical_novelty": {
    "type": "tooling_only"
  },
  "interfaces": [
    "formal",
    "symbolic"
  ],
  "claim": {
    "en": "LeanDojo released an open toolkit and benchmark for interacting with Lean and training theorem provers, together with ReProver, a retrieval-augmented language-model prover evaluated on nearly one hundred thousand mathlib theorems.",
    "zh-CN": "LeanDojo 公开了用于与 Lean 交互和训练定理证明器的工具与基准，并发布检索增强语言模型证明器 ReProver，在近十万个 mathlib 定理构成的基准上进行评测。"
  },
  "summary": {
    "en": "The project extracts proof states, tactics and premise information from Lean repositories and exposes a programmatic proof environment. ReProver retrieves relevant premises before generating tactics, directly addressing the library-scale premise-selection problem.",
    "zh-CN": "该项目从 Lean 仓库抽取证明状态、战术与前提信息，并提供可编程证明环境。ReProver 在生成战术前检索相关前提，直接处理大规模数学库中的前提选择问题。"
  },
  "why_it_matters": {
    "en": "LeanDojo helped turn Lean theorem proving into an accessible machine-learning research platform and supplied infrastructure that later formal-math agents could reuse rather than rebuilding environment integration from scratch.",
    "zh-CN": "LeanDojo 推动 Lean 定理证明成为更易进入的机器学习研究平台，并提供了后续形式数学 Agent 可复用的基础设施，避免重复搭建证明环境集成。"
  },
  "ai_role": {
    "level": "autonomous_primary",
    "description": {
      "en": "ReProver used retrieval and language-model generation to choose premises and propose Lean tactics, with the proof assistant determining whether generated proof steps were valid.",
      "zh-CN": "ReProver 使用检索与语言模型生成选择前提并提出 Lean 战术，由证明助手判断生成的证明步骤是否有效。"
    }
  },
  "human_contribution": {
    "description": {
      "en": "Researchers engineered the Lean extraction and interaction tooling, built the benchmark, trained the retriever and generator, and designed reproducible evaluation splits.",
      "zh-CN": "研究者开发 Lean 数据抽取与交互工具、构建基准、训练检索器与生成器，并设计可复现的评测划分。"
    }
  },
  "systems": [
    "leandojo",
    "reprover",
    "lean"
  ],
  "organizations": [
    "caltech",
    "nvidia"
  ],
  "people": [
    "kaiyu-yang",
    "aidan-swope",
    "alex-gu",
    "anima-anandkumar"
  ],
  "problems": [
    "lean-theorem-proving"
  ],
  "methods": [
    "retrieval-augmented-generation",
    "premise-selection",
    "formal-theorem-proving",
    "language-modeling"
  ],
  "verification": {
    "evidence_level": "E3",
    "status": "paper_released",
    "formal_assurance": "artifact_available",
    "history": [
      {
        "date": "2023-06-27",
        "status": "paper_released",
        "evidence_level": "E2",
        "note": {
          "en": "The paper described LeanDojo, ReProver and the benchmark methodology.",
          "zh-CN": "论文公开说明了 LeanDojo、ReProver 与基准方法。"
        }
      },
      {
        "date": "2023-06-27",
        "status": "paper_released",
        "evidence_level": "E3",
        "note": {
          "en": "Open-source tooling, benchmark resources and prover code were released for reproduction and extension.",
          "zh-CN": "开源工具、基准资源与证明器代码被公开，可用于复现与扩展。"
        }
      }
    ]
  },
  "sources": [
    {
      "id": "arxiv-leandojo-2306.15626",
      "tier": "S1",
      "type": "paper",
      "title": "LeanDojo: Theorem Proving with Retrieval-Augmented Language Models",
      "url": "https://arxiv.org/abs/2306.15626",
      "primary": true,
      "published_date": "2023-06-27",
      "accessed_at": "2026-09-10"
    },
    {
      "id": "github-lean-dojo",
      "tier": "S1",
      "type": "repository",
      "title": "LeanDojo repository",
      "url": "https://github.com/lean-dojo/LeanDojo",
      "primary": true,
      "published_date": null,
      "accessed_at": "2026-09-10"
    }
  ],
  "artifacts": [
    {
      "id": "leandojo-repository",
      "kind": "code",
      "title": "LeanDojo theorem-proving toolkit",
      "url": "https://github.com/lean-dojo/LeanDojo"
    }
  ],
  "relationships": {
    "predecessors": [
      "evt-2017-mathlib3",
      "evt-2019-coqgym",
      "evt-2020-gpt-f",
      "evt-2021-minif2f"
    ],
    "successors": [
      "evt-2024-deepseek-prover-v1-5",
      "evt-2026-alphaproof-nexus"
    ],
    "related": [
      "evt-2023-copra"
    ]
  },
  "timeline": {
    "featured": true,
    "significance": "primary"
  },
  "tags": [
    "lean",
    "theorem-proving",
    "retrieval",
    "infrastructure",
    "benchmark",
    "open-source"
  ],
  "editorial": {
    "created_at": "2026-09-10",
    "last_reviewed": "2026-09-15",
    "reviewers": [
      "Charlie-Wang-03"
    ]
  },
  "corrections": []
}