How to compare an optical-AI benchmark

A benchmark headline is only as useful as its comparison. Before judging an optical-AI result, identify whether the systems solved the same task to the same accuracy requirement and with comparable precision and measurement boundaries. Start with the workload. Was the result a component operation, a model layer, an inference workload, a training step, or a simulation? Then record the input shape, output requirement, batch assumptions, and the comparator configuration.

Compare like with like

A different precision, relaxed accuracy target, omitted memory cost, or modeled scaling assumption can make a numerical comparison unsuitable for a broad conclusion. Preserve the source's reported value, but also name the differences that prevent a direct ranking.

Separate measured and modeled

A demonstrated device result and a projected system result are different evidence types. Label each one. A projection can be valuable for design discussion without becoming a verified performance claim. This framework is designed for readers of papers and technical announcements. It does not make a claim about the performance of any particular optical AI product.