Hi everyone! I'm working on an AI research project that experiments with *algorithm synthesis*—trying to discover or combine solution strategies automatically rather than manually designing them. As one experiment, the system generated a proposed Rubik's Cube solving method called DR-OPT (Diminishing Returns Optimal). Since I'm not actually a speedcuber, I can't confidently judge whether the synthesis is genuinely novel, practical, or simply rediscovering existing ideas. I'd really appreciate feedback from experienced cubers. Specifically: • Does this resemble an existing method? • Are there incorrect assumptions or impossible claims? • Does the overall reasoning make sense? • If you were to critique it, what would you immediately question? Document: github.com/tamzrod/kde/blob/…/DR-OPT-MANUAL.md I'm more interested in understanding whether the synthesis process produced something meaningful than proving that it's the "best" method. Any criticism is welcome.

Looks cool! I’m not a speedcuber either, but seems like a fun project 🙂