ImagingBench benchmark shows agentic models generate visually plausible but physically incorrect outputs on computational imaging, revealing the gap between semantic understanding and inverse-problem solving.
Summary
If you're building vision systems for optics, sensing, or computational photography, VLMs alone won't handle the physics constraints—you'll need task-specific baselines. This quantifies where multimodal models actually break down in your domain.
Why it matters
If you're building vision systems for optics, sensing, or computational photography, VLMs alone won't handle the physics constraints—you'll need task-specific baselines. This quantifies where multimodal models actually break down in your domain.
Implementation verdict
VLMs don't replace specialized solvers for inverse problems (lensless imaging, holography, ToF reconstruction). ImagingBench is a testbed, not a library. Worth consulting if you're evaluating whether to use GPT/Gemini for physics-forward tasks—the answer is no for production accuracy.
Sources
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