arXiv:2504.10893v1 Announce Type: new
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 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%.
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ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search
