{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T04:13:37Z","timestamp":1746072817236,"version":"3.40.4"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031906428","type":"print"},{"value":"9783031906435","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":120,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>\n            <jats:italic>Machine-assisted theorem proving<\/jats:italic> refers to the process of conducting structured reasoning to automatically generate proofs for mathematical theorems. Recently, there has been a surge of interest in using machine learning models\u00a0in conjunction with proof assistants to perform this task. In this paper,\u00a0we introduce Pantograph, a tool that provides a versatile interface to the Lean\u00a04 proof assistant and enables efficient proof search via powerful search algorithms such as Monte Carlo Tree Search. In addition, Pantograph enables high-level reasoning by enabling a more robust handling of Lean\u00a04\u2019s inference steps. We provide an overview of Pantograph\u2019s architecture and features. We also report on an illustrative use case: using machine learning models and proof sketches to prove Lean\u00a04 theorems. Pantograph\u2019s innovative features pave the way for more advanced machine learning models to perform complex proof searches and high-level reasoning, equipping future researchers to design more versatile and powerful theorem provers.<\/jats:p>","DOI":"10.1007\/978-3-031-90643-5_6","type":"book-chapter","created":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T06:08:10Z","timestamp":1745993290000},"page":"104-123","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pantograph: A Machine-to-Machine Interaction Interface for Advanced Theorem Proving, High Level Reasoning, and Data Extraction in Lean 4"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6033-9140","authenticated-orcid":false,"given":"Leni","family":"Aniva","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9226-3688","authenticated-orcid":false,"given":"Chuyue","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5031-0126","authenticated-orcid":false,"given":"Brando","family":"Miranda","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9522-3084","authenticated-orcid":false,"given":"Clark","family":"Barrett","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4023-419X","authenticated-orcid":false,"given":"Sanmi","family":"Koyejo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,1]]},"reference":[{"key":"6_CR1","unstructured":"Hello gpt-4o. https:\/\/openai.com\/index\/hello-gpt-4o\/ (9 2024)"},{"key":"6_CR2","unstructured":"Introducing openai o1-preview. https:\/\/openai.com\/index\/introducing-openai-o1-preview\/ (9 2024)"},{"key":"6_CR3","unstructured":"Aya developers: The aya proof assistant. https:\/\/www.aya-prover.org (2021)"},{"key":"6_CR4","doi-asserted-by":"publisher","unstructured":"Browne, C.B., Powley, E., Whitehouse, D., Lucas, S.M., Cowling, P.I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., Colton, S.: A survey of monte carlo tree search methods. 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