Telling the agent to write clean code buys a level shift, not a slope change

The benchmark makes agents repeatedly extend their own prior work under evolving specifications, measuring structural erosion and verbosity across the trajectory. Over 36 problems, 196 checkpoints and 15 coding agents, "no agent fully solves any problem end-to-end, and the best agent passes 14.8% of checkpoints," attributed to GPT 5.5. Erosion rises in 77% of trajectories and verbosity in 75.5%. Against 473 open-source Python repositories, agent code is 2.3 times more verbose and 2.0 times more eroded. The An anti-slop prompt lowers erosion by 34.3% to 57.6% depending on the model, but costs an average 2.4 percentage points of strict solve rate and raises cost per checkpoint 12.1%. The authors: "prompting strategies trade capabilities for better initial quality, with little impact on the iterative degradation."

A dating note, because the wrong numbers are circulating: the v1 preprint of March 25 reported 20 problems, 93 checkpoints and a 17.2% best strict solve rate, so anything quoting 17.2% is quoting a superseded run. This edition read the current PDF's abstract, results, prompting section and conclusion, and not the full paper.

Why it matters: "Just tell it to write clean code" is the common remedy for agent-generated maintenance debt. This measures it: better code at checkpoint one, the same decay curve afterwards, at a cost in correctness and spend.