Under the US Lead and Copper Rule Revisions, a utility may determine a service line's material with a predictive model instead of inspecting it. New York State publishes, per address, which method was used.
The study audits AI "humanizers"—tools that rewrite machine‑generated text to evade detection—by checking whether factual units such as numbers, dates, names, and quoted spans survive each rewrite. Using a rule‑based checker validated by AI annotators, the authors found that in the 2026 public leaderboard, 35–65% of documents had at least one factual unit altered or dropped, with quoted spans most affected. An open 12B humanizer retained most numbers and names but reworded 67–78% of quotes, and a zero‑shot detector (Jev) could distinguish human originals from humanized rewrites with an AUROC of 0.864, outperforming several existing detectors.
By Ziyuan Wang, Genliang Zhu, Xiaobo Chen
The study presents a reproducible spatial simulation audit that combines a generative adversarial network for patrol allocation with a probabilistic detection model to assess patrol detection disparities in Baltimore and Chicago. Using archived crime records and census data, the authors compare GAN-based allocation to empirical and uniform deployment across multiple random seeds, finding detection-rate differences between Black-majority and White-majority areas ranging from –2.03 to 0.75 percentage points and coverage between 1.78% and 7.50%. The audit also evaluates a conditional tabular generative model for synthetic replacement, noting that geographic consistency fails in many cases, and highlights how simulated disparities depend on observation processes, allocation policies, training budgets, and randomness.
By Pronob Kumar Barman, Pronoy Kumar Barman
The paper introduces a new taxonomy for benchmark contamination that categorizes leakage by the mitigation it defeats—direct, derivative, temporal, distributional, and acquired—covering both training‑time and evaluation‑time scenarios. It proposes a four‑field disclosure protocol to record contamination status alongside benchmark scores, and provides a JSON schema, validator, and examples. An empirical study of 41 documents using a pre‑registered instrument shows limited reporting of contamination types and variable reliability, highlighting gaps in current disclosure practices.
By Johanna Angulo, V\'ictor Yeste, Hector Espinos-Morato
The paper introduces a data‑onboarding pipeline that detects and repairs defects in building meter data before training machine‑learning demand‑forecasting models. Controlled experiments on hourly electricity data from twelve U.S. buildings show that the pipeline restores forecast accuracy to clean‑data levels, even when defects affect up to 1.6% of training rows, and works across multiple models and horizons. The study also documents a first‑version over‑cleaning issue, its cause, and a per‑building calibration that corrects it, with all results reproducible from publicly pinned inputs.
By Yixuan Liang
arXiv:2608. 14903v1 Announce Type: new Abstract: Quantitative forecasts of frontier artificial intelligence often connect dated targets to trends in benchmark scores, training compute, release time, or expert belief.
By Fabricio F Costa