@inproceedings{xu-etal-2026-mavencoder,
    title = "{M}aven{C}oder: Competitive Code Generation via Model Adaptive Planning Strategies and Multi-Perspective Verification Enhancement",
    author = "Xu, ZhenChun  and
      Cai, Yi  and
      Zheng, Dajun  and
      Yuan, li  and
      Zhao, Mengchen  and
      Wang, Qixiang  and
      Wang, Jiexin",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1403/",
    pages = "30415--30439",
    ISBN = "979-8-89176-390-6",
    abstract = "With the rapid advancement of large language models (LLMs), automated code generation has made remarkable progress. Recent studies explore multi-agent collaboration and adopt planning{--}coding{--}debugging workflows to enhance performance. However, these approaches are constrained by rigid, predefined workflows that fail to flexibly adjust their plans and lack effective verification of intermediate reasoning steps. In this work, we propose MavenCoder, a model-adaptive and verification{--}enhanced framework for competition-level code generation. MavenCoder leverages adaptive assessment aligned with the model{'}s capabilities to select planning strategies, while providing timely feedback and correction via multi-perspective verification. This adaptive problem-solving paradigm mitigates earlier limitations by enabling flexible planning and timely error correction. Compared with existing state-of-the-art approaches, MavenCoder achieves superior pass@1 results across multiple benchmarks, achieving 87.5{\%} on LiveCodeBench, 93.9{\%} on HumanEval+, 81.7{\%} on MBPP+, and 46.1{\%} on CodeContests, outperforming recent agent-based systems with improvement exceeding 3{\%}{--}40{\%}."
}