Laguna M.1/XS.2 Technical Report

Computer Science > Artificial Intelligence [Submitted on 26 May 2026] Laguna M.1/XS.2 Technical Report Julien Abadji, Marah Abdin, Connor Adams, Eric Alcaide, Mustafa Altun, Michele Artoni, Junze Bao, Uday Barar, Vassilis Bekiaris, Arkadii Bessonov, Benjamin Bütikofer, Jonathan Chang, Yen-Chun Chen, Dmitry Chernenkov, Yang Chi, Filippos Christianos, Fenia Christopoulou, Razvan-Andrei Ciocoiu, Tzachi Cohen, Yohann Coppel, Dmitrii Emelianenko, Brandon Fergerson, Brian Fitzgerald, Matthias Gallé, Alex Golonzovskyi, George Grigorev, Yiyang Hao, Christian Hensel, Jan Huenermann, Ye Ji, Sarthak Joshi, Eiso Kant, Kabir Khandpur, Seonghyeon Kim, Vladimir Kirichenko, Umut Kocasarac, Ilya Kochik, Ivan Komarov, Chaerin Kong, Anurag Koul, François-Joseph Lacroix, Sergei Laktionov, Waren Long, Quentin Malartic, Vadim Markovtsev, Afonso Marques, Robert McHardy, Carlos Mocholí, Dmitry Monakhov, Adam Morris, Martin Muller, Christian Mürtz, Robin Nabel, Thien Nguyen, Rok Novosel, Szymon Ozog, Aalhad Patankar, Aleksei Petrov, Alexandre Piché, Arthur Pignet, Teodor Poncu, Phil Potter, Alexander Rakowski, Pierre-Yves Ritschard, Jay Roberts, Joe Rowell, Piotr Sarna, Pierre-André Savalle, Uladzislau Sazanovich, Nikita Shapovalov, Arsenii Shevchenko, Mikhail Shilkov, Andrei Sokol, Mohamed Soliman, Jack Stephenson, Victor Storchan, Dragos-Constantin Tantaru, Artem Tyurin, Adrian Wälchli, Pengming Wang, Jianxiao Yang, Renat Zayashnikov, Alexander Zelenka Martin, Nikolay Zinov, Caroline Bercier, José Caldeira, Margarida Garcia, Tom George, Kabeer Gharzai, Glenn Hitchcock, Carson Klingenberg, Ivo Pinto, Varun Randery, Noah Smith, Arina Sugako, Jason Warner We present Laguna M.1 and Laguna XS.2, two Mixture-of-Experts foundation models built for long-horizon, agentic coding: M.1 has 225.8B total parameters (23.4B activated per token) and XS.2 has 33.4B total (3B activated). Both models were trained from scratch end-to-end inside the same internal system that we refer to as our Model Factory: a tightly-integrated stack of versioned data, training, evaluation, and inference components that turn model development into an industrial process. We describe the principles and design choices of the Model Factory and also detail the end-to-end training process of our models, throughout pre-training data and architecture, post-training stages, evaluation, and quantization. On agentic software engineering and terminal benchmarks (SWE-bench Verified, SWE-bench Multilingual, SWE-Bench Pro, and Terminal-Bench 2.0) M.1 and XS.2 are competitive with state-of-the-art open models in their respective weight classes. Laguna XS.2 weights are released under Apache~2.0 at this https URL. Comments: Technical report to models released here: this https URL Subjects: Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2605.27605 [cs.AI]   (or arXiv:2605.27605v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.27605 Focus to learn more Submission history From: Pengming Wang [view email] [v1] Tue, 26 May 2026 19:23:24 UTC (2,020 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)