{"ID":2845876,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03617","arxiv_id":"2511.03617","title":"Visualization Biases MLLM's Decision Making in Network Data Tasks","abstract":"We evaluate how visualizations can influence the judgment of MLLMs about the presence or absence of bridges in a network. We show that the inclusion of visualization improves confidence over a structured text-based input that could theoretically be helpful for answering the question. On the other hand, we observe that standard visualization techniques create a strong bias towards accepting or refuting the presence of a bridge -- independently of whether or not a bridge actually exists in the network. While our results indicate that the inclusion of visualization techniques can effectively influence the MLLM's judgment without compromising its self-reported confidence, they also imply that practitioners must be careful of allowing users to include visualizations in generative AI applications so as to avoid undesired hallucinations.","short_abstract":"We evaluate how visualizations can influence the judgment of MLLMs about the presence or absence of bridges in a network. We show that the inclusion of visualization improves confidence over a structured text-based input that could theoretically be helpful for answering the question. On the other hand, we observe that...","url_abs":"https://arxiv.org/abs/2511.03617","url_pdf":"https://arxiv.org/pdf/2511.03617v1","authors":"[\"Timo Brand\",\"Henry Förster\",\"Stephen G. Kobourov\",\"Jacob Miller\"]","published":"2025-11-05T16:34:12Z","proceeding":"cs.GR","tasks":"[\"cs.GR\",\"cs.AI\"]","methods":"[\"Large Language Model\"]","has_code":false}