Generative Universal Verifier as Multimodal Meta-Reasoner

• URL: http://arxiv.org/abs/2510.13804v1
• Date: Wed, 15 Oct 2025 17:59:24 GMT
• Title: Generative Universal Verifier as Multimodal Meta-Reasoner
• Authors: Xinchen Zhang, Xiaoying Zhang, Youbin Wu, Yanbin Cao, Renrui Zhang, Ruihang Chu, Ling Yang, Yujiu Yang,
• Abstract summary: Generative Universal Verifier is a novel concept and plugin designed for next-generation multimodal reasoning in vision-language models and unified multimodal models.<n>We build ViVerBench, a benchmark spanning 16 categories of critical tasks for evaluating visual outcomes in multimodal reasoning.<n>We train OmniVerifier-7B, the first omni-capable generative verifier trained for universal visual verification.
• Score: 71.34250480838473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Generative Universal Verifier, a novel concept and plugin designed for next-generation multimodal reasoning in vision-language models and unified multimodal models, providing the fundamental capability of reflection and refinement on visual outcomes during the reasoning and generation process. This work makes three main contributions: (1) We build ViVerBench, a comprehensive benchmark spanning 16 categories of critical tasks for evaluating visual outcomes in multimodal reasoning. Results show that existing VLMs consistently underperform across these tasks, underscoring a substantial gap from human-level capability in reliable visual verification. (2) We design two automated pipelines to construct large-scale visual verification data and train OmniVerifier-7B, the first omni-capable generative verifier trained for universal visual verification and achieves notable gains on ViVerBench(+8.3). Through training, we identify three atomic capabilities in visual verification and demonstrate how they generalize and interact synergistically. (3) We propose OmniVerifier-TTS, a sequential test-time scaling paradigm that leverages the universal verifier to bridge image generation and editing within unified models, enhancing the upper bound of generative ability through iterative fine-grained optimization. Beyond generation, we extend universal verifier to broader world-modeling interleaved reasoning scenarios. Empirically, OmniVerifier-TTS achieves improvements on T2I-ReasonBench(+3.7), and GenEval++(+4.3), outperforming existing parallel test-time scaling methods, such as Best-of-N. By endowing multimodal reasoning with reliable visual verification, OmniVerifier advances both reliable reflection during generation and scalable test-time refinement, marking a step toward more trustworthy and controllable next-generation reasoning systems.

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