Emirhan CAMCI

After a dependency upgrade, some Python code keeps passing its tests and quietly returns different results: pandas 3 makes datetime-to-integer conversions 1000 times smaller, NumPy 2 turns the string "0" into True. PatchPilot lists such functions, runs them on the old and the new library, and a Meta model proposes a patch. The patch is accepted only if it passes three gates: the visible tests, hidden tests the model never sees, and a differential check against the original behavior on the old library. Your file changes only when you press Apply, and Undo restores it. We ran it on one function in each of two real open-source projects (indsl, traceml). In traceml a function filled missing values with the mean on pandas 2.3 and left them empty on pandas 3.0; the project's own tests pass either way. A Meta model repaired it behind the three gates (live today with the local model: first attempt, 4,043 tokens, 54 s) and we reported it upstream (issue 33). We also tested the gates with no model on 69 patches we wrote: a visible-tests-only workflow would have accepted 29 of the 52 wrong ones, the three gates none, and all 17 correct patches were accepted (docs/GATE_MATRIX.md). A local browser app runs everything: Check (no model), Repair, a race between models on the same functions, and a history of saved races. PatchPilot's own code uses only the Python standard library and was written during the hackathon. One function per project, our inputs, small samples, no success rates. (Kısa sürüm, yaklaşık 110 kelime: ilk paragraf + "We ran it on one function in each of two real open-source projects (indsl, traceml); the traceml break is reported upstream (issue 33). Code is standard library only. One function per project, not a rate.")