@inproceedings{zhang-etal-2025-arise,
    title = "{AR}ise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search",
    author = "Zhang, Yize  and
      Wang, Tianshu  and
      Chen, Sirui  and
      Wang, Kun  and
      Zeng, Xingyu  and
      Lin, Hongyu  and
      Han, Xianpei  and
      Sun, Le  and
      Lu, Chaochao",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.538/",
    pages = "10978--10995",
    ISBN = "979-8-89176-251-0",
    abstract = "Large language models (LLMs) have demonstrated impressive capabilities and are receiving increasing attention to enhance their reasoning through scaling test-time compute. However, their application in open-ended, knowledge-intensive, complex reasoning scenarios is still limited. Reasoning-oriented methods struggle to generalize to open-ended scenarios due to implicit assumptions of complete world knowledge. Meanwhile, knowledge-augmented reasoning (KAR) methods fails to address two core challenges: 1) error propagation, where errors in early steps cascade through the chain, and 2) verification bottleneck, where the explore{--}exploit trade-off arises in multi-branch decision processes. To overcome these limitations, we introduce ARise, a novel framework that integrates risk assessment of intermediate reasoning states with dynamic retrieval-augmented generation (RAG) within a Monte Carlo tree search paradigm. This approach enables effective construction and optimization of reasoning plans across multiple maintained hypothesis branches. Experimental results show that ARise significantly outperforms the state-of-the-art KAR methods by up to 23.10{\%}, and the latest RAG-equipped large reasoning models by up to 25.37{\%}. Our project page is at https://opencausalab.github.io/ARise."
}