@inproceedings{chen-etal-2025-othello,
    title = "What if Othello-Playing Language Models Could See?",
    author = "Chen, Xinyi  and
      Yuan, Yifei  and
      Li, Jiaang  and
      Belongie, Serge  and
      de Rijke, Maarten  and
      S{\o}gaard, Anders",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.673/",
    doi = "10.18653/v1/2025.findings-emnlp.673",
    pages = "12598--12609",
    ISBN = "979-8-89176-335-7",
    abstract = "Language models are often said to face a symbol grounding problem. While some have argued the problem can be solved without resort to other modalities, many have speculated that grounded learning is more efficient. We explore this question in Othello, a simplified, rule-based world that offers a controlled and interpretable testbed for studying world understanding. Building on prior work, we introduce VISOTHELLO, a multi-modal model trained jointly on move sequences and board images. Using the Othello rule understanding task, we examine whether multi-modal learning provides advantages over text-only approaches. We further evaluate robustness under semantically irrelevant perturbations and analyze the consistency of cross-modal alignment. Our results suggest that multi-modal training not only improves performance and robustness but also promotes convergence toward shared internal representations across different model architectures."
}