Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks
arXiv:2607.07189v1 Announce Type: new Abstract: Vision-language models (VLMs) and agentic AI have shown strong performance on semantic visual tasks, but it remains unclear whether they can handle the physics and inverse problems that underlie computational imaging. We present ImagingBench, a benchmark of 20 computational imaging tasks spanning five categories: ray and wave optics, image signal processing, inverse reconstruction, computational sensing, and calibration. ImagingBench evaluates three complementary settings: Expert, fixed expert-guided inverse reconstruction; Planner, planner-guide
The proliferation of vision-language models and agentic AI necessitates a systematic evaluation of their capabilities beyond semantic tasks, particularly in scientific domains like computational imaging.
This benchmark directly addresses a critical gap in understanding whether AI agents can handle complex physics and inverse problems, which is crucial for their application in scientific discovery and industrial processes.
The explicit benchmarking of AI's ability to 'understand' imaging physics, rather than just semantic content, defines new frontiers for AI agent development and application.
- · AI research labs focused on scientific discovery
- · Developers of agentic AI platforms
- · Computational imaging industry
- · High-performance computing providers
- · AI models lacking robust physics-based reasoning
- · Traditional imaging simulation software without AI integration
ImagingBench will guide the next generation of AI agent development to incorporate more sophisticated physical understanding.
AI agents could accelerate breakthroughs in materials science, medical diagnostics, and astrophysics by handling complex imaging tasks autonomously.
The demonstrated capability of AI agents in 'understanding' physics could lead to a paradigm shift in scientific methodology, with AI becoming an essential co-pilot for research.
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Read at arXiv cs.AI