Heh, me too. I'm on my third rewrite after a bunch of promising false starts.

Unfortunately geometric kernels are one of those things where unknown unknowns will bite you in the ass really hard because none of the content is really in the training data for LLMs and pathological/degenerate cases aren't just common but expected. IME it's not something that can be vibe coded with current models, if ever, without intimately understanding the algorithms.

I can't do a thorough review of waffle iron right now but just off the top of my head: it doesn't look like you have a tolerance context? The tolerances look like hard coded constants (TAU_MODEL/TAU_WORK/MATCH_TOLERANCE/etc) but that's fundamentally unworkable. Each operation and vertex/edge/etc needs to track accumulating errors and apply them to downstream point classification. Interfaces like Kernel::boolean_union(a, b) are the wrong abstraction because it's missing tons of information/functionality like accumulating FP errors, evidence/proofs, rollback, etc.

Keep working at it! It's worth the challenge.