@inproceedings{hu-etal-2026-k,
    title = "K-{GIP}: Diagnosing Logical Fractures in Large Vision-Language Models via Verification Scene Graphs and Sequential Pruning",
    author = "Hu, Yujun  and
      Zhou, Xiaoyu  and
      Wang, Changbo  and
      He, Gaoqi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.497/",
    pages = "10222--10236",
    ISBN = "979-8-89176-395-1",
    abstract = "Diagnosing fine-grained hallucinations in Large Vision-Language Models (LVLMs) can greatly advance their reliable deployment in real-world applications. Nevertheless, current benchmarks predominantly employ flat metrics that treat errors in isolation, leaving a gap in evaluating the complex causal dependencies between visual perception and textual reasoning. Motivated by this, we introduce the Knowledge-Guided In-Context Probing (K-GIP) framework to fill this gap. Specifically, K-GIP constructs a high-fidelity dual-perception ground truth to transform abstract priors into multi-granularity queries. Furthermore, we propose a Verification Scene Graph metric equipped with a Sequential Logic Pruning protocol, which explicitly models existence-attribute dependencies to strictly penalize logical fractures. We conduct comprehensive evaluations of mainstream LVLMs across three datasets using K-GIP. The experimental results highlight that our methodology successfully isolates deep reasoning failures from simple perceptual misses. We hope K-GIP can serve as a valuable and rigorous standard to assess logical robustness in multimodal systems."
}