ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search

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 fail 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 tradeoff 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%.

@article{zhang2025_2504.10893,
  title={ ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search },
  author={ Yize Zhang and Tianshu Wang and Sirui Chen and Kun Wang and Xingyu Zeng and Hongyu Lin and Xianpei Han and Le Sun and Chaochao Lu },
  journal={arXiv preprint arXiv:2504.10893},
  year={ 2025 }
}