Research
Peer-reviewed work on how AI search reshapes the content it ranks — published in full, so anyone can check it.
Papers
COLM 2026
arXiv:2608.30466
31 Aug 2026
Qianwen Gao, Zichang Su, Yiwen Hou, Arlen Kumar, Leanid Palkhouski
A controlled simulation of what happens when creators keep rewriting documents to chase an LLM ranking signal. Over twenty rounds in six domains, the documents that move closest to the ranking feature profile drift furthest from independently judged quality — and a random-target control shows the drift comes from the ranking incentive, not from rewriting alone.
Rank–citation AUC 0.853 ± 0.093 · 20 rounds · mean Spearman shift −0.068