OmniVL-Guard Pro: A Tool-Augmented Agent for Omnibus Vision-Language Forensics

• URL: http://arxiv.org/abs/2605.16962v2
• Date: Wed, 20 May 2026 08:01:00 GMT
• Title: OmniVL-Guard Pro: A Tool-Augmented Agent for Omnibus Vision-Language Forensics
• Abstract summary: We propose textbf OmniVL-Guard Pro, a tool-augmented agent that extends unified forensics from closed-world prediction to open-world clues-driven reasoning.<n>To generate high-quality tool-reasoning trajectories, we introduce textbfTree-Structured Self-Evolving Tool Trajectory Generation.<n>We also propose textbfChecker-Guided Agentic Reinforcement Learning, which provides process-level supervision to penalize cases where the answer is correct but the reasoning is distorted.
• Score: 63.13200245209719
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 training corpora, and limited perceptual resolution, creating a practical ceiling in dynamic open-world forensics -- particularly for real-time event verification requiring external clues and forgery segmentation demanding fine-grained scrutiny of local manipulations. To address these limitations, we shift from scaling up the self-contained model toward reaching beyond it. We propose \textbf{OmniVL-Guard Pro}, a tool-augmented agent that extends unified forensics from closed-world prediction to open-world clues-driven reasoning. OmniVL-Guard Pro integrates a tool environment spanning real-time event search, local cropping and zooming, edge-anomaly screening, face detection, video frame extraction, and SAM3-based segmentation. To generate high-quality tool-reasoning trajectories, we introduce \textbf{Tree-Structured Self-Evolving Tool Trajectory Generation}, which produces diverse trajectories through seed guidance, guider-free self-evolution, and weakly-hinted hard sample synthesis, yielding the Full-Spectrum Tool Reasoning (FSTR) dataset for training. We further propose \textbf{Checker-Guided Agentic Reinforcement Learning} (CGARL), which provides process-level supervision to penalize cases where the answer is correct but the reasoning is distorted. Extensive experiments demonstrate that OmniVL-Guard Pro achieves state-of-the-art performance across various tasks, and exhibits strong zero-shot generalization. The FSTR dataset and code for OmniVL-Guard Pro will be publicly released at https://github.com/shen8424/OmniVL-Guard-Pro.

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