@inproceedings{deik-etal-2026-programming,
    title = "Programming over Thinking: Efficient and Robust Multi-Constraint Planning",
    author = "Deik, Derrick Goh Xin  and
      Long, Quanyu  and
      Liu, Zhengyuan  and
      Chen, Nancy F.  and
      Wang, Wenya",
    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.2028/",
    pages = "43785--43837",
    ISBN = "979-8-89176-390-6",
    abstract = "Multi-constraint planning involves identifying, evaluating, and refining candidate plans while satisfying multiple, potentially conflicting constraints. Existing large language model (LLM) approaches face fundamental limitations in this domain. Pure reasoning paradigms, which rely on long natural language chains, are prone to inconsistency, error accumulation, and prohibitive cost as constraints compound. Conversely, LLMs combined with coding- or solver-based strategies lack flexibility: they often generate problem-specific code from scratch or depend on fixed solvers, failing to capture generalizable logic across diverse problems.To alleviate these issues, we introduce the Scalable Code Planning Engine (SCOPE), a systematic framework that disentangles query-specific problem reasoning from generic code execution. SCOPE first transforms input queries into optimized structured representations, capturing the interdependent constraints, and then autonomously generates reusable solver functions (Combination, Filter, and Deliver) that provide consistent and reliable execution across diverse problems. SCOPE achieves state-of-the-art performance while lowering cost and latency. For example, with GPT-4o, it reaches 93.1{\%} success on TravelPlanner, a 61.6{\%} gain over the best baseline (CoT) while cutting inference cost by 1.4 times and time by approximately 4.67 times. Code is available at https://github.com/DerrickGXD/SCOPE."
}