That matches what I’ve seen as well — generation is the easy part, validation is the bottleneck.
I’ve been experimenting with a small sparse-regression system that infers governing equations from raw data, and it can produce a lot of plausible candidates quickly. The hard part is filtering out the ones that look right but violate underlying constraints.
For example, it recovered the Sun’s rotation (~25.1 days vs 27 actual) from solar wind data, but most candidate equations were subtly wrong until you enforced consistency checks.
Feels like systems that treat verification as the source of truth (not just an afterthought) are the ones that will actually scale.
I’ve been experimenting with a small sparse-regression system that infers governing equations from raw data, and it can produce a lot of plausible candidates quickly. The hard part is filtering out the ones that look right but violate underlying constraints.
For example, it recovered the Sun’s rotation (~25.1 days vs 27 actual) from solar wind data, but most candidate equations were subtly wrong until you enforced consistency checks.
Feels like systems that treat verification as the source of truth (not just an afterthought) are the ones that will actually scale.