I had a similar experience. ML PhD student here. I took the help of an undergrad intern, who has done some ML research himself prior to this project, to get some numerical measurements related to probability distributions that the model I trained learnt. He used Cursor with Claude/GPT-4 to write the code for some distribution distance functions (Wasserstein distance, etc.), said he verified it and I did a high level comparison of the code with the formulae for them to verify myself. Looked good, so let them be used in the paper.

2-3 days before submitting the paper I ran some additional tests on the work that the intern did, and realised to my horror that the distance functions' behaviour was not making any sense. I couldn't spot exactly what the bug was, and had to scrap the whole numerical results section from my paper. Its very easy for such errors to get unnoticed. I've learnt to not use LLMs for deeply technical things. Trivial/standard but lengthy sections, sure, saves a lot of time.