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    <title>AI-Driven Formal Theorem Proving in the Lean Ecosystem</title>

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                    <h1 class="title is-1 publication-title">AI-Driven Formal Theorem Proving</h1>

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                        <span class="author-block">California Institute of Technology, PI: <a target="_blank" href="http://tensorlab.cms.caltech.edu/users/anima/">Anima Anandkumar</a>, Grad Student: <a target="_blank" href="https://www.robertj1.com/">Robert Joseph George</a></span>
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                    <h2 class="title is-3"><span class="lean-infer">The Grand Challenge</span></h2>
                    <p style="font-size: 125%">The challenge is to formalize and verify everything from programs, to scientific experiments, to mathematics. Our lab is working on making verification accessible, mathematically rigorous, and practical by combining large language models with formal proof assistants like Lean. We want to bring formal methods to broader scientific computing, where correctness matters for reproducible research and trustworthy software.</p>
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	                    <h2 class="title is-3"><span class="dvima">Papers</span></h2>
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	                        <p><strong>TorchLean: Formalizing Neural Networks in Lean</strong><br>
	                        Robert Joseph George, Jennifer Cruden, Xiangru Zhong, Huan Zhang, Anima Anandkumar<br>
	                        <em>arXiv preprint, 2026</em><br>
	                        <a target="_blank" href="https://arxiv.org/abs/2602.22631">arXiv:2602.22631</a></p>

	                        <p><strong>BRIDGE: Building Representations In Domain Guided Program Synthesis</strong><br>
	                        Robert Joseph George, Carson Eisenach, Udaya Ghai, Dominique Perrault-Joncas, Anima Anandkumar, Dean Foster<br>
	                        <em>arXiv preprint, 2026</em><br>
	                        <a target="_blank" href="https://arxiv.org/abs/2511.21104">arXiv:2511.21104</a></p>

                        <p><strong>Mathematical Discovery and Formalization Towards the AC Conjecture</strong><br>
                        Caroline Zhang, Aaron Zhao, Robert Joseph George, Sergei Gukov, Anima Anandkumar<br>
                        <em>NeurIPS Mathematical Reasoning and AI, 2025</em><br>
                        <a target="_blank" href="https://neurips.cc/virtual/2025/workshop/109565">NeurIPS Workshop</a></p>

                        <p><strong>LeanDojo-v2: A Comprehensive Library for AI-Assisted Theorem Proving in Lean</strong><br>
                        Ryan Hsiang, Will Adkisson, Robert Joseph George, Anima Anandkumar<br>
                        <em>NeurIPS Mathematical Reasoning and AI, 2025</em><br>
                        <a target="_blank" href="https://neurips.cc/virtual/2025/workshop/109565">NeurIPS Workshop</a></p>

                        <p><strong>LeanProgress: Guiding Search for Neural Theorem Proving via Proof Progress Prediction</strong><br>
                        Robert Joseph George, Suozhi Huang, Anima Anandkumar et al.<br>
                        <em>Transactions on Machine Learning Research (TMLR), 2025</em><br>
                        <a target="_blank" href="https://arxiv.org/abs/2502.17925">arXiv:2502.17925</a></p>

                        <p><strong>LeanAgent: Lifelong Learning for Formal Theorem Proving</strong><br>
                        Adarsh Kumarappan, Mo Tiwari, Peiyang Song, Robert Joseph George, Chaowei Xiao, Anima Anandkumar<br>
                        <em>International Conference on Learning Representations (ICLR) 2025</em><br>
                        <a target="_blank" href="https://arxiv.org/abs/2410.06209">arXiv:2410.06209</a></p>

                        <p><strong>Lean Copilot: Large Language Models as Copilots for Theorem Proving in Lean</strong><br>
                        Peiyang Song, Kaiyu Yang, Anima Anandkumar<br>
                        <em>International Conference on Neuro-symbolic Systems (NeuS) 2025</em><br>
                        <a target="_blank" href="https://arxiv.org/abs/2404.12534">arXiv:2404.12534</a></p>

                        <p><strong>LeanDojo: Theorem Proving with Retrieval-Augmented Language Models</strong><br>
                        Kaiyu Yang, Aidan Swope, Alex Gu, Rahul Chalamala, Peiyang Song, Shixing Yu, Saad Godil, Ryan Prenger, Anima Anandkumar<br>
                        <em>Neural Information Processing Systems (NeurIPS) 2023</em><br>
                        <a target="_blank" href="https://arxiv.org/abs/2306.15626">arXiv:2306.15626</a></p>
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