{
  "schema_version": 1,
  "id": "evt-2007-malarea",
  "slug": "malarea-learning-reasoning-loop",
  "title": {
    "en": "MaLARea closes a machine-learning and theorem-proving feedback loop over large theories",
    "zh-CN": "MaLARea 在大型理论上建立机器学习与定理证明反馈循环"
  },
  "dates": {
    "event": "2007-06-27",
    "first_public_claim": "2007-06-27",
    "paper_release": "2007-06-27",
    "artifact_release": null,
    "independently_verified": null,
    "added_to_chronicle": "2026-09-15",
    "last_updated": "2026-09-15"
  },
  "event_types": [
    "system"
  ],
  "significance": {
    "tier": "H2",
    "rationale": {
      "en": "MaLARea made large-theory reasoning explicitly iterative: machine learning selects useful knowledge, ATP systems attempt proofs, and successful results are fed back into the learner for subsequent problems.",
      "zh-CN": "MaLARea 将大型理论推理明确组织为迭代循环：机器学习选择有用知识，ATP 尝试证明，成功结果再反馈给学习器用于后续问题。"
    }
  },
  "mathematical_novelty": {
    "type": "tooling_only"
  },
  "interfaces": [
    "formal",
    "symbolic"
  ],
  "claim": {
    "en": "Josef Urban's MaLARea paper was published with the ESARLT 2007 workshop proceedings on 27 June 2007 and described a metasystem combining machine learning, automated theorem proving and model finding for large theories.",
    "zh-CN": "Josef Urban 的 MaLARea 论文于 2007 年 6 月 27 日随 ESARLT 2007 proceedings 发布，描述一个结合机器学习、自动定理证明与模型查找的大型理论元系统。"
  },
  "summary": {
    "en": "The system repeatedly trains relevance models from existing proofs, invokes automated provers on selected premises and incorporates newly found proofs into subsequent learning rounds.",
    "zh-CN": "系统反复从已有证明训练相关性模型，在筛选前提上调用自动证明器，并把新找到的证明纳入后续学习轮次。"
  },
  "why_it_matters": {
    "en": "The deduction–learning feedback loop became a core pattern for later self-improving theorem-proving systems and anticipated modern search-and-learning agents for formal mathematics.",
    "zh-CN": "“演绎—学习”反馈循环后来成为自我改进式定理证明系统的核心范式，并预示现代形式数学中的 search-and-learning agents。"
  },
  "ai_role": {
    "level": "autonomous_primary",
    "description": {
      "en": "Learned premise-selection models and automated provers interacted in an autonomous loop that improved from previous proof results.",
      "zh-CN": "学习式前提选择模型与自动证明器在自主循环中协作，并从此前证明结果持续改进。"
    }
  },
  "human_contribution": {
    "description": {
      "en": "Josef Urban designed the metasystem, learning/reasoning loop, data interfaces and experiments.",
      "zh-CN": "Josef Urban 设计元系统、学习/推理循环、数据接口与实验。"
    }
  },
  "systems": [
    "malarea"
  ],
  "organizations": [
    "charles-university"
  ],
  "people": [
    "josef-urban"
  ],
  "problems": [
    "large-theory-theorem-proving"
  ],
  "methods": [
    "machine-learning",
    "premise-selection",
    "automated-theorem-proving",
    "model-finding",
    "feedback-loop"
  ],
  "verification": {
    "evidence_level": "E2",
    "status": "paper_released",
    "formal_assurance": "none",
    "history": [
      {
        "date": "2007-06-27",
        "status": "paper_released",
        "evidence_level": "E2",
        "note": {
          "en": "The CEUR proceedings establish the MaLARea system and its learning/reasoning architecture.",
          "zh-CN": "CEUR proceedings 确认 MaLARea 系统及其学习/推理架构。"
        }
      }
    ]
  },
  "sources": [
    {
      "id": "ceur-malarea-2007",
      "tier": "S1",
      "type": "paper",
      "title": "MaLARea: a Metasystem for Automated Reasoning in Large Theories",
      "url": "https://ceur-ws.org/Vol-257/05_Urban.pdf",
      "primary": true,
      "published_date": "2007-06-27",
      "accessed_at": "2026-09-15"
    }
  ],
  "artifacts": [],
  "relationships": {
    "predecessors": [],
    "successors": [],
    "related": [
      "evt-2007-mptp-challenge"
    ]
  },
  "timeline": {
    "featured": true,
    "significance": "primary"
  },
  "tags": [
    "malarea",
    "machine-learning",
    "theorem-proving",
    "large-theory-reasoning"
  ],
  "editorial": {
    "created_at": "2026-09-15",
    "last_reviewed": "2026-09-15",
    "reviewers": [
      "Charlie-Wang-03"
    ]
  },
  "corrections": []
}