CauSight: Learning to Supersense for Visual Causal Discovery

Yize Zhang; Meiqi Chen; Sirui Chen; Bo Peng; Yanxi Zhang; Tianyu Li; Chaochao Lu

CauSight: Learning to Supersense for Visual Causal Discovery

Yize Zhang, Meiqi Chen, Sirui Chen, Bo Peng, Yanxi Zhang, Tianyu Li, Chaochao Lu

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Abstract

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).

Anthology ID:: 2026.emnlp-main.1824
Volume:: Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:: EMNLP
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 41556–41580
Language:
URL:: https://aclanthology.org/2026.emnlp-main.1824/
DOI:
Bibkey:
Cite (ACL):: Yize Zhang, Meiqi Chen, Sirui Chen, Bo Peng, Yanxi Zhang, Tianyu Li, and Chaochao Lu. 2026. CauSight: Learning to Supersense for Visual Causal Discovery. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 41556–41580, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: CauSight: Learning to Supersense for Visual Causal Discovery (Zhang et al., EMNLP 2026)
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PDF:: https://aclanthology.org/2026.emnlp-main.1824.pdf

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@inproceedings{zhang-etal-2026-causight,
    title = "{C}au{S}ight: Learning to Supersense for Visual Causal Discovery",
    author = "Zhang, Yize  and
      Chen, Meiqi  and
      Chen, Sirui  and
      Peng, Bo  and
      Zhang, Yanxi  and
      Li, Tianyu  and
      Lu, Chaochao",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.emnlp-main.1824/",
    pages = "41556--41580",
    ISBN = "979-8-89176-459-0",
    abstract = "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)."
}

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%0 Conference Proceedings
%T CauSight: Learning to Supersense for Visual Causal Discovery
%A Zhang, Yize
%A Chen, Meiqi
%A Chen, Sirui
%A Peng, Bo
%A Zhang, Yanxi
%A Li, Tianyu
%A Lu, Chaochao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhang-etal-2026-causight
%X 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).
%U https://aclanthology.org/2026.emnlp-main.1824/
%P 41556-41580

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Markdown (Informal)

[CauSight: Learning to Supersense for Visual Causal Discovery](https://aclanthology.org/2026.emnlp-main.1824/) (Zhang et al., EMNLP 2026)

CauSight: Learning to Supersense for Visual Causal Discovery (Zhang et al., EMNLP 2026)

ACL

Yize Zhang, Meiqi Chen, Sirui Chen, Bo Peng, Yanxi Zhang, Tianyu Li, and Chaochao Lu. 2026. CauSight: Learning to Supersense for Visual Causal Discovery. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 41556–41580, Budapest, Hungary. Association for Computational Linguistics.