ViGoR-Bench: How Far Are Visual Generative Models From Zero-Shot Visual Reasoners?

📰 ArXiv cs.AI

ViGoR-Bench evaluates the limitations of visual generative models in zero-shot visual reasoning tasks

advanced Published 30 Mar 2026

Action Steps

Identify the limitations of current visual generative models in reasoning tasks
Develop a unified framework to evaluate visual generative models
Use ViGoR-Bench to assess the performance of models in zero-shot visual reasoning tasks
Analyze the results to inform future research and development directions

Who Needs to Know This

AI researchers and engineers working on visual generative models and computer vision tasks can benefit from ViGoR-Bench to identify areas for improvement, and product managers can use it to inform the development of more realistic benchmarks

Key Insight

💡 Current visual generative models struggle with tasks that require physical, causal, or complex spatial reasoning

Key Takeaways

ViGoR-Bench evaluates the limitations of visual generative models in zero-shot visual reasoning tasks

Full Article

Title: ViGoR-Bench: How Far Are Visual Generative Models From Zero-Shot Visual Reasoners?

Abstract:
arXiv:2603.25823v1 Announce Type: cross Abstract: Beneath the stunning visual fidelity of modern AIGC models lies a "logical desert", where systems fail tasks that require physical, causal, or complex spatial reasoning. Current evaluations largely rely on superficial metrics or fragmented benchmarks, creating a ``performance mirage'' that overlooks the generative process. To address this, we introduce ViGoR Vision-G}nerative Reasoning-centric Benchmark), a unified framework designed to dismantle

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