CauSight: Learning to Supersense for Visual Causal Discovery
Causal thinking enables humans to understand not just what is seen, but why it happens. To replicate this capability in modern AI systems, we introduce the task of visual causal discovery. It requires models to infer cause-and-effect relations among visual entities across diverse scenarios instead of merely perceiving their presence. To this end, we first construct the Visual Causal Graph dataset (VCG-32K), a large-scale collection of over 32,000 images annotated with entity-level causal graphs, and further develop CauSight, a novel vision-language model to perform visual causal discovery through causally aware reasoning. Our training recipe integrates three components: (1) training data curation from VCG-32K, (2) Tree-of-Causal-Thought (ToCT) for synthesizing reasoning trajectories, and (3) reinforcement learning with a designed causal reward to refine the reasoning policy. Experiments show that CauSight outperforms GPT-4.1 on visual causal discovery, achieving over a threefold performance boost (21% absolute gain). Our code, model, and dataset are fully open-sourced at project page: https://github.com/OpenCausaLab/CauSight.
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Yize Zhang
admin_assigned
2025-12-03T17:35:41.326Z
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Yize Zhang (SII)
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ez2407
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Meiqi Chen
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Sirui Chen
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2025-12-08T08:33:20.349Z
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Sirui Chen
SiruiChen
user
SiruiChen
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Bo Peng
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Yanxi Zhang
claimed_verified
2026-01-22T08:47:59.413Z
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Yanxi Zhang
zhangyx2001
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Tianyu Li
claimed_verified
2025-12-02T16:42:10.708Z
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Tianyu Li
sephyli
user
sephyli
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Chaochao Lu
CauSight: Learning to Supersense for Visual Causal Discovery
Causal thinking enables humans to understand not just what is seen, but why it happens. To replicate this capability in modern AI systems, we introduce the task of visual causal discovery. It requires models to infer cause-and-effect relations among visual entities across diverse scenarios instead of merely perceiving their presence. To this end, we first construct the Visual Causal Graph dataset (VCG-32K), a large-scale collection of over 32,000 images annotated with entity-level causal graphs, and further develop CauSight, a novel vision-language model to perform visual causal discovery through causally aware reasoning. Our training recipe integrates three components: (1) training data curation from VCG-32K, (2) Tree-of-Causal-Thought (ToCT) for synthesizing reasoning trajectories, and (3) reinforcement learning with a designed causal reward to refine the reasoning policy. Experiments show that CauSight outperforms GPT-4.1 on visual causal discovery, achieving over a threefold performance boost (21% absolute gain). Our code, model, and dataset are fully open-sourced at project page: https://github.com/OpenCausaLab/CauSight.
A novel vision-language model, CauSight, performs visual causal discovery by inferring cause-and-effect relationships in images, outperforming GPT-4.1 with a significant performance boost.
visual causal discovery Visual Causal Graph dataset VCG-32K CauSight Tree-of-Causal-Thought ToCT reinforcement learning causal reward reasoning trajectories