Repos With Committed AI Config Files Show Half the Cognitive-Complexity Growth After Coding-Agent Adoption (+27% vs +53%)

arXiv 2608.25241·high signalXBlueskyLinkedInCopy link

Denisov-Blanch et al. introduce RAMP, a four-level maturity model scored purely from version-controlled AI configuration artifacts, and apply it to 441 repositories; independent human annotation reproduced RAMP's labels on 97% of a held-out sample. Agents raise commit volume 28-38% at every maturity level, but among agent-first repos those with no committed AI configuration show roughly twice the increase in cognitive complexity (+53% vs +27%) and 1.7x the increase in static-analysis warnings. Adoption is set-and-forget: 73.8% of these artifacts are committed once and never modified, and the authors flag the result as observational and hypothesis-generating.