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
user
comin
68f04ef636f8b025381e18da
Xiaoying Zhang
68f04ef636f8b025381e18db
Youbin Wu
68f04ef636f8b025381e18dc
Yanbin Cao
68f04ef636f8b025381e18dd
Renrui Zhang
68f04ef636f8b025381e18de
Ruihang Chu
68f04ef636f8b025381e18df
Ling Yang
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++