{"type":"ScholarlyArticle","title":"Online Self-Calibration Against Hallucination in Vision-Language Models","authors":[{"name":"Minghui Chen","slug":"minghui-chen","given":"Minghui","family":"Chen","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences"},{"name":"Chenxu Yang","slug":"chenxu-yang","given":"Chenxu","family":"Yang","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences"},{"name":"Hengjie Zhu","slug":"hengjie-zhu","given":"Hengjie","family":"Zhu","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences"},{"name":"Dayan Wu","slug":"dayan-wu","given":"Dayan","family":"Wu","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences"},{"name":"Qingyi Si","slug":"qingyi-si","given":"Qingyi","family":"Si","affiliation":"JD.COM"},{"name":"Zheng Lin","slug":"zheng-lin","given":"Zheng","family":"Lin","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences"}],"abstract":"Large Vision-Language Models (LVLMs) often suffer from hallucinations, generating descriptions that include visual details absent from the input image. Recent preference alignment methods typically rely on supervision distilled from stronger models such as GPT. However, this offline paradigm introduces a Supervision-Perception Mismatch: the student model is forced to align with fine-grained details beyond its perceptual capacity, learning to guess rather than to see. To obtain reliable self-supervision for online learning, we identify a Generative-Discriminative Gap within LVLMs, where models exhibit higher accuracy on discriminative verification than open-ended generation. Leveraging this capability, we propose Online Self-CAlibRation (OSCAR), a framework that integrates Monte Carlo Tree Search with a Dual-Granularity Reward Mechanism to construct preference data and iteratively refines the model via Direct Preference Optimization. Extensive experiments demonstrate that OSCAR achieves state-of-the-art performance on hallucination benchmarks while preserving general multimodal capabilities.","doi":"10.24963/ijcai.2026/111","url":"https://ijcai.org/proceedings/2026/111","pdf":"https://ijcai.org/proceedings/2026/0111.pdf","markdown":"https://ijcai.org/proceedings/2026/111.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/111","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":111,"pages":{"first":989,"last":997},"keywords":["Natural Language Processing: Applications","Natural Language Processing: Language models","Computer Vision: Multimodal learning"],"videos":[],"references":[{"position":1,"raw":"[Achiam et al., 2023] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Alt- man, Shyamal Anadkat, et al. 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