{
  "schema_version": 1,
  "id": "evt-2022-thor",
  "slug": "thor-language-model-hammer-integration",
  "title": {
    "en": "Thor integrates language models with automated theorem-prover hammers",
    "zh-CN": "Thor 将语言模型与自动定理证明器 Hammer 集成"
  },
  "dates": {
    "event": "2022-05-22",
    "first_public_claim": "2022-05-22",
    "paper_release": "2022-05-22",
    "artifact_release": null,
    "independently_verified": null,
    "added_to_chronicle": "2026-09-10",
    "last_updated": "2026-09-10"
  },
  "event_types": [
    "proof",
    "system"
  ],
  "significance": {
    "tier": "H2",
    "rationale": {
      "en": "Thor was an early explicit neuro-symbolic architecture in which language models delegated premise-selection bottlenecks to classical automated provers.",
      "zh-CN": "Thor 是较早明确采用神经符号分工的架构之一：语言模型把前提选择瓶颈交给传统自动定理证明器处理。"
    }
  },
  "mathematical_novelty": {
    "type": "tooling_only"
  },
  "interfaces": [
    "formal",
    "symbolic",
    "hybrid"
  ],
  "claim": {
    "en": "Thor increased a language model's success on the PISA dataset from 39% to 57% and matched leading miniF2F methods with less compute.",
    "zh-CN": "Thor 将语言模型在 PISA 数据集上的成功率从 39% 提升到 57%，并以更少算力在 miniF2F 上达到当时领先方法水平。"
  },
  "summary": {
    "en": "The framework uses theorem-prover hammers for premise selection while leaving other proof decisions to the language model.",
    "zh-CN": "该框架使用定理证明器 hammer 进行前提选择，其余证明决策由语言模型完成。"
  },
  "why_it_matters": {
    "en": "It highlighted a durable design principle: use symbolic tools where exhaustive reliability is strong and learned models where generation is needed.",
    "zh-CN": "它体现了一条长期有效的设计原则：把符号工具用于可靠的结构化搜索，把学习模型用于需要生成与启发式判断的环节。"
  },
  "ai_role": {
    "level": "ai_primary_human_verified",
    "description": {
      "en": "The language model generated proof actions while automated provers supplied premise-search assistance.",
      "zh-CN": "语言模型生成证明动作，自动定理证明器负责提供前提搜索支持。"
    }
  },
  "human_contribution": {
    "description": {
      "en": "Researchers designed the integration protocol and evaluation across theorem-proving environments.",
      "zh-CN": "研究者设计集成协议并在多个定理证明环境中进行评测。"
    }
  },
  "systems": [
    "thor"
  ],
  "organizations": [
    "university-of-cambridge",
    "microsoft-research"
  ],
  "people": [],
  "problems": [
    "pisa",
    "minif2f"
  ],
  "methods": [
    "hammer",
    "premise-selection",
    "language-model"
  ],
  "verification": {
    "evidence_level": "E2",
    "status": "paper_released",
    "formal_assurance": "none",
    "history": [
      {
        "date": "2022-05-22",
        "status": "paper_released",
        "evidence_level": "E2",
        "note": {
          "en": "The Chronicle records this event from the cited primary or official source; the status does not imply independent Chronicle verification.",
          "zh-CN": "Chronicle 依据所引 primary / official source 记录该事件；该状态不表示 Chronicle 已完成独立验证。"
        }
      }
    ]
  },
  "sources": [
    {
      "id": "arxiv-thor-2205.10893",
      "tier": "S1",
      "type": "paper",
      "title": "Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers",
      "url": "https://arxiv.org/abs/2205.10893",
      "primary": true,
      "published_date": "2022-05-22",
      "accessed_at": "2026-09-10"
    }
  ],
  "artifacts": [],
  "relationships": {
    "predecessors": [
      "evt-2021-pact",
      "evt-2021-minif2f"
    ],
    "successors": [],
    "related": []
  },
  "timeline": {
    "featured": true,
    "significance": "primary"
  },
  "tags": [
    "theorem-proving",
    "neuro-symbolic",
    "hammer",
    "premise-selection"
  ],
  "editorial": {
    "created_at": "2026-09-10",
    "last_reviewed": "2026-09-11",
    "reviewers": [
      "Charlie-Wang-03"
    ]
  },
  "corrections": []
}