AI RESEARCH
OmniVL-Guard Pro: A Tool-Augmented Agent for Omnibus Vision-Language Forensics
arXiv CS.CV
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ArXi:2605.16962v1 Announce Type: new Existing vision-language forgery detection and grounding methods operate under a closed-world paradigm, assuming verification can be completed by the model alone. However, self-contained MLLMs are constrained by finite parametric knowledge, static