@inproceedings{li-etal-2026-beyond-surface,
    title = "Beyond Surface-Level Pattern Trap: {LLM} Agents for Faster and Smarter Cross-Architecture Code Migration",
    author = "Li, Weijia  and
      Gao, KE  and
      Chen, Pengfei  and
      Li, Jiajie  and
      Wang, Xinyu  and
      Le, Yiran  and
      Wu, Yize  and
      Li, Ling",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.148/",
    pages = "3029--3042",
    ISBN = "979-8-89176-395-1",
    abstract = "The problem of surface-level pattern mapping represents a critical yet underexplored failure mode in large language model (LLM) reasoning, and is particularly acute in cross-architecture code migration of high-performance libraries. On low-resource, low-level code, insufficient coverage in pretraining data often leads LLMs to rely on superficial name- or type-based correspondences, rather than principled refactorization and reasoning grounded in core functional semantics and architecture-specific optimization intents. This tendency severely hampers the effectiveness of LLMs in complex migration scenarios.To address these challenges, we propose FSCM, a multi-agent framework for cross-architecture migration. FSCM decouples complex implementation details through functional mining and code refactoring, guiding LLMs to focus on invariant semantic anchors across architectures. By mitigating surface-level pattern traps, FSCM improves both functional correctness and performance when targeting emerging architectures. Extensive experiments on the challenging real-world OpenCV library migration tasks demonstrate substantial improvements over state-of-the-art baselines, achieving up to 22{\%} higher correctness rates over Copilot and 43.04x speedup on RISC-V platforms. Code and data are available at: https://anonymous.4open.science/r/code-F8D4."
}