@inproceedings{feng-etal-2026-motifagent,
    title = "{M}otif{A}gent: Learning Molecular Assembly through Multi-Agent Collaboration for Chemical Language Understanding",
    author = "Feng, Jinjia  and
      Wang, Wenda  and
      Wei, Zhewei",
    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.2023/",
    pages = "40700--40739",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) have shown great potential in molecular understanding by aligning molecular representations with text. However, existing approaches remain limited to static motif recognition without comprehending the generative principles{---}the connection rules governing how motifs assemble into valid topological structures. To address this challenge, we introduce **MotifAgent**, a multi-agent reinforcement learning framework inspired by emergent collective intelligence. We formulate molecular assembly as a collaborative problem where each motif is represented by an agent sharing a common LLM backbone, learning connection rules through explicit inter-motif negotiation rather than implicit sequence memorization. Key innovations include: (1) dynamic inter-agent negotiation for modeling motif connections; (2) Set-based Behavioral Cloning for learning multiple topologically equivalent assembly paths; (3) topology-aware reward shaping with MAPPO to maintain chemical validity while optimizing target properties. Extensive experiments demonstrate that MotifAgent achieves state-of-the-art performance across molecular property prediction, description generation, and reaction prediction tasks, with our generalist model surpassing specialized expert models."
}