owls-on-wires

Physics and known-constant problems were definitely the focus. My ambition was to create a fast algorithm that could pull out a nice, “natural-looking” equation from (potentially noisy) data, if one existed. Model ranking and selection, coefficient snapping, noise robustness, and the search patterns were all designed to that end.

For that reason, it does terribly on the SRBench black-box track (it doesn’t like to produce long, overfit models), but (to my knowledge) it scores the highest on exact symbolic recovery, and even with noise it can often find the answer in under 10s (single-thread, no GPU).

Unfortunately no paper yet, but I’m considering publishing one here soon! As far as agents go, I just pointed Claude at the repo at asked it to explain, and it did a good job. It also helps that it’s not a ton of code, and a lot of it is structurally similar to patterns in training data (in this case, it can recognize a recursive beam-search).

Would highly recommended submitting a PR for SRBench! The maintainers were very responsive, despite one necessary correction mine was merged within 2 weeks.

Bookmarked. Thanks for sharing!

“…no practical purpose” Nonsense, I learned something about compilation today. Thank you for sharing.

This is a concept I’ve been playing around with as an alternative to using libraries to build plots inside Jupyter notebooks. I would like to extend it to work with a variety of different file types, especially for engineering-related file formats (like STL for CAD and GDS for chips).