Causal thinking enables humans to understand not just what is seen, but why it happens. To bring this ability to modern AI systems, we introduce visual causal discovery, the task of inferring visually verifiable direct causal dependencies among entities in a single static image. We construct the Visual Causal Graph dataset (VCG-32K), a large-scale collection of over 32,000 images annotated with entity-level causal graphs, and develop Causight, a causally aware vision-language model for this task. Our training recipe combines data curation from VCG-32K, Tree-of-Causal-Thought (ToCT) reasoning synthesis, and reinforcement learning with a graph-structured causal reward. Experiments show that Causight outperforms GPT-4.1, achieving over a threefold performance boost (21% absolute gain).

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.