Post-Acceptance Edit Behavior

Liang et al. (CMU, arXiv 2607.25130): DECODE, 53.6K in-IDE edits of accepted AI completions from 1,141 developers — the first measurement of what humans DO to AI code after accepting it, pre-commit rather than at PR level. Half of all edits land within 50 minutes and the volume collapses after 15; retention is bimodal (median 63% survives, but the mass sits at 0% and 100%); 31% of trajectories contain a removal edit, and the developer who first tries to CUSTOMIZE a completion is the likeliest to delete it next (23.4% vs 12.2% after a functionality change). Which of 20 models wrote it barely matters (eta-squared 0.002–0.007). The prediction half is weaker than its abstract: fine-tuned 3B models beat their own base by +0.23 F1 but the best frontier baseline by only +0.08, and the dominant edit type — changing functionality — tops out near 0.49 Levenshtein similarity for every model tried