@inproceedings{gao-etal-2026-feedback,
    title = "Feedback to Reasoning: {LLM}-Assisted Molecular Optimization with Domain Feedback and Historical Reasoning",
    author = "Gao, Wenhan  and
      Fan, Xiran  and
      Yeh, Chin-Chia Michael  and
      Sun, Jiarui  and
      Chen, Yuzhong  and
      Pan, Menghai  and
      Das, Mahashweta  and
      Liu, Yi",
    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.619/",
    pages = "12735--12754",
    ISBN = "979-8-89176-395-1",
    abstract = "The success of large language models (LLMs) across domains highlights their potential in scientific tasks, with molecular optimization being a promising frontier. Traditionally, this optimization relies on iterative expert feedback to refine molecules toward desired properties, a process well aligned with LLMs' strengths. **As an experience-driven task, molecular optimization depends critically on the domain feedback and accumulation of historical knowledge. However, none of the existing methods fully leverages such feedback and historical knowledge with reasoning traces and chemical insights.** In this work, we propose F2R: Feedback to Reasoning, a conversational molecular optimization pipeline that enables LLMs to accumulate and retrieve past actions, rationales, and feedback. Like humans, LLMs can generate imperfect reasoning; F2R is the first framework to use detailed domain feedback to critique and improve this reasoning. This transforms LLMs from passive text generators into agentic experts that learn both actions and reasoning from experience. Consequently, F2R shows remarkable performance."
}