@inproceedings{zhang-etal-2026-reap,
    title = "{REAP}: Towards Effective Training-Free Chemical Reasoning with Explicit Atomic Priors",
    author = "Zhang, Mingxu  and
      Shen, Dazhong  and
      Zhang, Qi  and
      Sun, Ying",
    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.97/",
    pages = "2037--2062",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) exhibit strong general reasoning but struggle in molecular science due to the lack of explicit priors required for precise chemical reasoning. Current solutions inject priors into parameters, but this static coupling hinders rapid knowledge updates and often compromises the model{'}s general capabilities. To address this, we introduce REAP, a training-free framework that equips LLMs with an external knowledge base, enabling them to reason over retrieved chemical priors dynamically. REAP implements a structured reasoning pipeline that autonomously selects relevant priors from our constructed atom-level knowledge base, retrieves analogue exemplars, and synthesizes these information to guide the LLM{'}s decision-making. This architecture ensures interpretability and adaptability while preserving the LLM{'}s intrinsic general intelligence. Experiments show that REAP outperforms current reasoning methods and rivals state-of-the-art training-based models, demonstrating the effectiveness of our framework."
}