arXiv:2608. 19922v1 Announce Type: new Abstract: 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.
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.
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.
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.
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.
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.
arXiv:2607. 07146v1 Announce Type: new Abstract: The Internal Waves Service screens the Sentinel-1 Wave-mode archive for internal solitary waves, routing detections to experts whose adjudication time is the resource the effort exists to conserve.
The paper investigates how standard evaluations of detectors for hijacked large language model (LLM) agents may conceal important weaknesses. By auditing eleven evaluations on the AgentDrift and AgentDojo benchmarks, the authors show that detectors often perform poorly when faced with rare attacks, unseen tools, or data from a different benchmark, with precision dropping dramatically at low attack rates and rankings reversing as tool coverage falls. They also find that a released LLM action monitor, despite high AUROC on its own data, would still generate many false alerts when applied to a new benchmark. "whyItMatters":"The study highlights that current evaluation practices can give a misleading sense of detector reliability, underscoring the need for more robust, cross‑benchmark testing to ensure real‑world safety of LLM agents."
arXiv:2608. 12652v1 Announce Type: cross Abstract: Benchmark contamination is diagnosed today with n-gram overlap, with likelihood-based membership inference, or with canary strings, and each needs something usually unavailable: the training corpus, a well-chosen test statistic, or foresight at dataset release.
The study investigates whether AI coding assistants check trust signals before installing software. Researchers pre‑registered a controlled experiment on six open‑source research projects, creating nine modified versions per project with varying trust signals and running 1,920 trials across three models and two operating modes. Results showed that verification of trust signals was almost nonexistent—only 0.5% of trials involved any signal inspection, and no trial executed a verification command, indicating that publishing signals alone does not ensure secure behavior.
arXiv:2608.31017v1 Announce Type: cross Abstract: Ambient AI scribes draft clinical notes under the reassurance that a clinician signs every note. We audited three commercial AI scribes on the same 1...
The paper compares a non‑generative verifier (Jev) with a frontier LLM (Astra) on 495 human‑labelled claims from LLM‑AggreFact. Both models achieve statistically indistinguishable accuracy, yet Jev is 187‑fold cheaper and admits more unsupported claims. A confidence‑gated cascade that escalates uncertain claims improves cost‑efficiency, though the benefit comes mainly from the escalation share rather than the probability estimates.