Generative Universal Verifier as Multimodal Meta-Reasoner
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.
68f04ef636f8b025381e18d9
Xinchen Zhang
claimed_verified
2025-10-16T06:39:15.323Z
653e5d31ffd60206c8b64bb5
https://cdn-avatars.huggingface.co/v1/production/uploads/653e5d31ffd60206c8b64bb5/bgztraPC27L6culMlJw4s.png
Xinchen Zhang
comin
user
comin
68f04ef636f8b025381e18da
Xiaoying Zhang
68f04ef636f8b025381e18db
Youbin Wu
68f04ef636f8b025381e18dc
Yanbin Cao
68f04ef636f8b025381e18dd
Renrui Zhang
68f04ef636f8b025381e18de
Ruihang Chu
68f04ef636f8b025381e18df
Ling Yang
claimed_verified
2025-11-17T10:32:44.027Z
64fde4e252e82dd432b74ce9
https://cdn-avatars.huggingface.co/v1/production/uploads/64fde4e252e82dd432b74ce9/-CQZbBP7FsPPyawYrsi4z.jpeg
Ling Yang
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user
Lingaaaaaaa
68f04ef636f8b025381e18e0
Yujiu Yang
Generative Universal Verifier as Multimodal Meta-Reasoner
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.
Generative Universal Verifier enhances multimodal reasoning by providing reliable visual verification through ViVerBench, OmniVerifier-7B, and OmniVerifier-TTS, improving generation and refinement capabilities.
Generative Universal Verifier ViVerBench OmniVerifier-7B OmniVerifier-TTS multimodal reasoning vision-language models visual verification atomic capabilities sequential test-time scaling T2I-ReasonBench GenEval++