Grounding the Score: Explicit Visual Premise Verification for Reliable Vision-Language Process Reward Models
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917476338499584 |
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| author | Wang, Junxin Guan, Dai Qiu, Weijie Li, Zhihang Gai, Yongbo Yang, Zhengyi Zhou, Mengyu Zhao, Erchao Jiang, Xiaoxi Jiang, Guanjun |
| author_facet | Wang, Junxin Guan, Dai Qiu, Weijie Li, Zhihang Gai, Yongbo Yang, Zhengyi Zhou, Mengyu Zhao, Erchao Jiang, Xiaoxi Jiang, Guanjun |
| contents | Vision-language process reward models (VL-PRMs) are increasingly used to score intermediate reasoning steps and rerank candidates under test-time scaling. However, they often function as black-box judges: a low step score may reflect a genuine reasoning mistake or simply the verifier's misperception of the image. This entanglement between perception and reasoning leads to systematic false positives (rewarding hallucinated visual premises) and false negatives (penalizing correct grounded statements), undermining both reranking and error localization. We introduce Explicit Visual Premise Verification (EVPV), a lightweight verification interface that conditions step scoring on the reliability of the visual premises a step depends on. The policy is prompted to produce a step-wise visual checklist that makes required visual facts explicit, while a constraint extractor independently derives structured visual constraints from the input image. EVPV matches checklist claims against these constraints to compute a scalar visual reliability signal, and calibrates PRM step rewards via reliability gating: rewards for visually dependent steps are attenuated when reliability is low and preserved when reliability is high. This decouples perceptual uncertainty from logical evaluation without per-step tool calls. Experiments on VisualProcessBench and six multimodal reasoning benchmarks show that EVPV improves step-level verification and consistently boosts Best-of-N reranking accuracy over strong baselines. Furthermore, injecting controlled corruption into the extracted constraints produces monotonic performance degradation, providing causal evidence that the gains arise from constraint fidelity and explicit premise verification rather than incidental prompt effects. Code is available at: https://github.com/Qwen-Applications/EVPV-PRM |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_16253 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Grounding the Score: Explicit Visual Premise Verification for Reliable Vision-Language Process Reward Models Wang, Junxin Guan, Dai Qiu, Weijie Li, Zhihang Gai, Yongbo Yang, Zhengyi Zhou, Mengyu Zhao, Erchao Jiang, Xiaoxi Jiang, Guanjun Computer Vision and Pattern Recognition Artificial Intelligence I.2.7; I.4.8; H.3.3 Vision-language process reward models (VL-PRMs) are increasingly used to score intermediate reasoning steps and rerank candidates under test-time scaling. However, they often function as black-box judges: a low step score may reflect a genuine reasoning mistake or simply the verifier's misperception of the image. This entanglement between perception and reasoning leads to systematic false positives (rewarding hallucinated visual premises) and false negatives (penalizing correct grounded statements), undermining both reranking and error localization. We introduce Explicit Visual Premise Verification (EVPV), a lightweight verification interface that conditions step scoring on the reliability of the visual premises a step depends on. The policy is prompted to produce a step-wise visual checklist that makes required visual facts explicit, while a constraint extractor independently derives structured visual constraints from the input image. EVPV matches checklist claims against these constraints to compute a scalar visual reliability signal, and calibrates PRM step rewards via reliability gating: rewards for visually dependent steps are attenuated when reliability is low and preserved when reliability is high. This decouples perceptual uncertainty from logical evaluation without per-step tool calls. Experiments on VisualProcessBench and six multimodal reasoning benchmarks show that EVPV improves step-level verification and consistently boosts Best-of-N reranking accuracy over strong baselines. Furthermore, injecting controlled corruption into the extracted constraints produces monotonic performance degradation, providing causal evidence that the gains arise from constraint fidelity and explicit premise verification rather than incidental prompt effects. Code is available at: https://github.com/Qwen-Applications/EVPV-PRM |
| title | Grounding the Score: Explicit Visual Premise Verification for Reliable Vision-Language Process Reward Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence I.2.7; I.4.8; H.3.3 |
| url | https://arxiv.org/abs/2603.16253 |