🤖 AI Summary
This work addresses the labor-intensive and expertise-dependent nature of computational imaging system design by proposing a method that automatically generates verifiable forward models from natural language instructions. Leveraging a formal specification language (spec.md) and a multi-agent architecture comprising Plan, Judge, and Execute modules, the approach combines a finite primitive basis to translate single-sentence descriptions into imaging systems with bounded reconstruction error. The study introduces a novel “design-to-reality error decomposition theorem,” which decouples total error into five independently controllable components, enabling cross-modal composition of high-dimensional (3D–5D) primitives. Evaluated across six real-world data modalities, the method achieves expert-level quality with 98.1 ± 4.2% fidelity and successfully produces ten novel imaging designs that surpass the capabilities of any single modality.
📝 Abstract
Designing a computational imaging system -- selecting operators, setting parameters, validating consistency -- requires weeks of specialist effort per modality, creating an expertise bottleneck that excludes the
broader scientific community from prototyping imaging instruments. We introduce spec.md, a structured specification format, and three autonomous agents -- Plan, Judge, and Execute -- that translate a one-sentence
natural-language description into a validated forward model with bounded reconstruction error. A design-to-real error theorem decomposes total reconstruction error into five independently bounded terms, each linked
to a corrective action. On 6 real-data modalities spanning all 5 carrier families, the automated pipeline matches expert-library quality (98.1 +/- 4.2%). Ten novel designs -- composing primitives into chains from 3D
to 5D -- demonstrate compositional reach beyond any single-modality tool.