Boris Tolstokulakov
Author directory2026
FactRot: Auditing Typed Fact Survival Under Iterative LLM Rewriting
Vladislav Kulikov | Mikhail Mozikov | Aleksei Stepin | Boris Tolstokulakov | Artyom Kabanov | Nasonov Ivan | Nikita Glazkov | Ilya Makarov
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Vladislav Kulikov | Mikhail Mozikov | Aleksei Stepin | Boris Tolstokulakov | Artyom Kabanov | Nasonov Ivan | Nikita Glazkov | Ilya Makarov
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
FactRot measures whether typed factual mentions survive iterative LLM rewriting. We follow each ledger fact through every hop of a rewrite chain and score it separately, instead of measuring aggregate drift in the rewritten text. We score 1,113 ledger facts from 25 Wikipedia passages across 4 models, 4 rewrite operations, and 3 prompt conditions, with chains reaching depth 12. A GEE with passage clusters finds per-hop decay (OR=0.743 per additional hop, p<.001). Type-specific faithfulness instructions do not measurably outperform generic faithfulness at hop 5 (𝛥=0.026, 95% CI [0.022, 0.073]); both improve survival over neutral prompting by roughly 0.13-0.16 absolute. On the depth-12 neutral subset, answer-restate rewriting is much more destructive than direct paraphrasing, and the frontier preview model has the highest hop-12 survival among the four models. Exact fact-type rankings, however, are unstable across models (Kendall W=0.082 at hop 5).