@inproceedings{song-etal-2026-aot,
    title = "{AOT}*: Efficient Synthesis Planning via {LLM}-Empowered {AND}-{OR} Tree Search",
    author = "Song, Xiaozhuang  and
      Pan, Xuanhao  and
      Zhao, Xinjian  and
      Ye, Hangting  and
      Zhang, Shufei  and
      Tang, Jian  and
      Yu, Tianshu",
    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-workshops/2026.findings-acl.1734/",
    pages = "34727--34758",
    ISBN = "979-8-89176-395-1",
    abstract = "Retrosynthesis planning enables the discovery of viable synthetic routes for target molecules, playing a crucial role in domains like drug discovery and materials design. Multi-step retrosynthetic planning remains computationally challenging due to exponential search spaces and inference costs. While Large Language Models (LLMs) demonstrate chemical reasoning capabilities, their application to synthesis planning faces constraints on efficiency and cost. To address these challenges, we introduce AOT*, a framework that transforms retrosynthetic planning by integrating LLM-generated chemical synthesis pathways with systematic AND-OR tree search. To this end, AOT* maps the generated complete synthesis routes onto AND-OR tree components, with a mathematically sound design of reward assignment strategy and retrieval-based context engineering, thus enabling LLMs to efficiently navigate in the chemical space. Experimental evaluation on multiple synthesis benchmarks demonstrates that AOT* achieves SOTA performance with significantly improved search efficiency. AOT* exhibits competitive solve rates using 3-5$\times$ fewer iterations than existing LLM-based approaches, with the performance advantage becoming more pronounced on complex molecular targets. Our code is available at https://github.com/ShawnKS/AOTstar."
}