Tencent Hunyuan released EvolveScaler, a benchmark that tests whether a model can reconstruct accurate ground truth from event logs where individual records get retracted and corrected after the fact, the way real operational data behaves. On the hardest tier, frontier models' accuracy falls to 11.3 percent, a collapse from the scores those same models post on static, unchanging benchmarks. The benchmark also shows that training on this kind of evolving data raises out-of-distribution scores by 5.25 points, suggesting the failure is not fundamental but a gap in what models have been trained to handle. The result is a useful corrective for anyone evaluating a model on log analysis, monitoring, or any task where the underlying facts change after ingestion: a model that scores well on a fixed benchmark can still fail badly the moment the data it reasons over gets revised, which is closer to how production logs actually behave than most current evaluation sets.