ViGoR-Bench: How Far Are Visual Generative Models From Zero-Shot Visual Reasoners?
Summary: 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-Generative Reasoning-centric Benchmark), a unified framework designed to dismantle this mirage.
Introduction to ViGoR
ViGoR distinguishes itself through four key innovations:
- Holistic Cross-Modal Coverage: ViGoR bridges Image-to-Image and Video tasks, providing a comprehensive evaluation of visual generative models.
- Dual-Track Mechanism: This feature evaluates both intermediate processes and final results, offering a deeper understanding of model performance.
- Evidence-Grounded Automated Judge: Ensuring high human alignment, this automated judge uses real-world evidence to assess model outputs, making the evaluation process more robust.
- Granular Diagnostic Analysis: ViGoR provides a detailed breakdown of performance across fine-grained cognitive dimensions, helping to identify specific reasoning deficits.
Significance of ViGoR
As artificial intelligence continues to evolve, the focus has increasingly shifted from mere visual generation to advanced reasoning capabilities. Despite the impressive outputs of leading models, the underlying reasoning processes often reveal significant gaps. The introduction of ViGoR serves a dual purpose: it not only highlights these deficiencies but also establishes a critical framework for future developments in intelligent vision systems.
Experimental Findings
Experiments conducted on over 20 leading models have unveiled substantial reasoning deficits, even among state-of-the-art systems. These findings emphasize the necessity for a framework like ViGoR, which can function as a “stress test” for next-generation models. By using ViGoR, researchers can better understand where current systems fall short in terms of logical reasoning and cognitive performance.
Conclusion
The ViGoR benchmark represents a significant advancement in the evaluation of visual generative models. By addressing the logical and reasoning challenges that have long been overlooked, ViGoR opens new avenues for research and development in artificial intelligence.
For more details and to explore the demo, visit ViGoR-Bench Demo.