Revealing Interpretable Failure Modes of VLMs
A new paper titled "Revealing Interpretable Failure Modes of VLMs" by Isha Chaudhary et al. has been published on arXiv. This research explores various failure modes within Vision-Language Models (VLMs).
A new research paper, "Revealing Interpretable Failure Modes of VLMs," authored by Isha Chaudhary and four co-authors, has been made available on the arXiv preprint server. The paper, submitted on May 12, 2026, focuses on understanding and identifying various failure modes within Vision-Language Models (VLMs). This study is categorized under Computer Science (cs.AI).
Readers can access a PDF version of the full text directly from the arXiv platform. Additionally, experimental HTML and TeX Source formats are available for review. For those interested in citation and bibliographic management, tools such as BibTeX, NASA ADS, Google Scholar, and Semantic Scholar are supported.
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