@inproceedings{du-etal-2025-boost,
    title = "Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation",
    author = "Du, Kounianhua  and
      Wang, Hanjing  and
      Liu, Jianxing  and
      Chen, Jizheng  and
      Dai, Xinyi  and
      Wang, Yasheng  and
      Tang, Ruiming  and
      Yu, Yong  and
      Wang, Jun  and
      Zhang, Weinan",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.findings-acl.833/",
    doi = "10.18653/v1/2025.findings-acl.833",
    pages = "16194--16204",
    ISBN = "979-8-89176-256-5",
    abstract = "To address these limitations, we propose BDC, a novel framework that Boosts reasoning exploration via multi-agent collaboration, Disentangles heterogeneous data into specialized experts, and Customizes solutions through dynamic model composition. BDC integrates a Monte Carlo Tree-of-Agents algorithm, where multiple LLMs mutually verify and refine reasoning paths through reflection-guided pruning, enabling efficient exploration of high-quality solutions. To handle data diversity, we cluster problems by latent semantics, train composable LoRA experts on each cluster, and deploy an input-aware hypernetwork to dynamically merge these experts into tailored solvers. Experiments on APPS and CodeContest benchmarks demonstrate BDC{'}s superiority: it achieves up to 73.8{\%} accuracy on hard problems, outperforming state-of-the-art methods like LATS and RethinkMCTS by 9{--}15{\%}. This work lays the groundwork for advancing LLM capabilities in complex reasoning tasks, offering a novel System2-to-System1 solution."
}