Benchmark RadarImagingBench
ImagingBench evaluates agentic AI systems on 20 computational imaging tasks spanning ray and wave optics, image signal processing, inverse reconstruction, computational sensing, and calibration, across three settings: Expert, Planner, and Forward.
- Released
- 2026-07-08
- Readiness
- Inspectable
- Primary field
- General AI
Why it matters
Reveals the gap between semantic visual competence and physically grounded imaging performance, providing a unified testbed to measure progress in agentic AI for computational imaging.
Motivation
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.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.