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The propensity of large language models (LLMs) to generate hallucinations and non-factual content undermines their reliability in high-stakes domains, where rigorous control over Type I errors (the conditional probability of incorrectly classifying hallucinations as truthful content) is essential. Despite its importance, formal verification of LLM factuality with such guarantees remains largely unexplored. In this paper, we introduce FACTTEST, a novel framework that statistically assesses whether an LLM can provide correct answers to given questions with high-probability correctness guarantees. We formulate hallucina- tion detection as a hypothesis testing problem to enforce an upper bound of Type I errors at user-specified significance levels. Notably, we prove that FACTTEST also ensures strong Type II error control under mild conditions and can be extended to maintain its effectiveness when covariate shifts exist. FACTTEST is distribution-free and and model-agnostic. It works for any number of human-annotated samples and applies to any black-box or white-box LM. Extensive experiments demonstrate that FACTTEST effectively detects hallucinations and enable LLMs to abstain from answering unknown questions, leading to an over 40% accuracy improvement. 

A<sc>bstract</sc> Inclusive and differential cross sections for Higgs boson production in proton-proton collisions at a centre-of-mass energy of 13.6 TeV are measured using data collected with the CMS detector at the LHC in 2022, corresponding to an integrated luminosity of 34.7 fb⁻¹. Events with the diphoton final state are selected, and the measured inclusive fiducial cross section is$${\sigma }_{\text{fid}}={74}\pm {11}{\left({\text{stat}}\right)}_{-4}^{+5}\left({\text{syst}}\right)$$fb, in agreement with the standard model prediction of 67.8 ± 3.8 fb. Differential cross sections are measured as functions of several observables: the Higgs boson transverse momentum and rapidity, the number of associated jets, and the transverse momentum of the leading jet in the event. Within the uncertainties, the differential cross sections agree with the standard model predictions. 

A measurement is presented of the cross section in proton-proton collisions for the production of two $W$bosons and one $Z$boson. It is based on data recorded by the CMS experiment at the CERN LHC at center-of-mass energies $\sqrt{s}=13$and 13.6 TeV, corresponding to an integrated luminosity of $200{\mathrm{fb}}^{-1}$. Events with four charged leptons (electrons or muons) in the final state are selected. Both nonresonant $WWZ$production and $ZH$production, with the Higgs boson decaying into two $W$bosons, are reported. For the first time, the two processes are measured separately in a simultaneous fit. Combining the two modes, signal strengths relative to the standard model (SM) predictions of ${0.75}_{-0.29}^{+0.34}$and ${1.74}_{-0.60}^{+0.71}$are measured for $\sqrt{s}=13$and 13.6 TeV, respectively. The observed (expected) significance for the triboson signal is 3.8 (2.5) standard deviations for $\sqrt{s}=13.6\mathrm{TeV}$, thus providing the first evidence for triboson production at this center-of-mass energy. Combining the two modes and the two center-of-mass energies, the inclusive signal strength relative to the SM prediction is measured to be ${1.03}_{-0.28}^{+0.31}$, with an observed (expected) significance of 4.5 (5.0) standard deviations. 

Abstract The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online data quality monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. In addition, the first results from deploying the autoencoder-based system in the CMS online data quality monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system. 

Abstract We present 294 pulsars found in GeV data from the Large Area Telescope (LAT) on the Fermi Gamma-ray Space Telescope. Another 33 millisecond pulsars (MSPs) discovered in deep radio searches of LAT sources will likely reveal pulsations once phase-connected rotation ephemerides are achieved. A further dozen optical and/or X-ray binary systems colocated with LAT sources also likely harbor gamma-ray MSPs. This catalog thus reports roughly 340 gamma-ray pulsars and candidates, 10% of all known pulsars, compared to ≤11 known before Fermi. Half of the gamma-ray pulsars are young. Of these, the half that are undetected in radio have a broader Galactic latitude distribution than the young radio-loud pulsars. The others are MSPs, with six undetected in radio. Overall, ≥236 are bright enough above 50 MeV to fit the pulse profile, the energy spectrum, or both. For the common two-peaked profiles, the gamma-ray peak closest to the magnetic pole crossing generally has a softer spectrum. The spectral energy distributions tend to narrow as the spindown power $\dot{E}$decreases to its observed minimum near 10³³erg s⁻¹, approaching the shape for synchrotron radiation from monoenergetic electrons. We calculate gamma-ray luminosities when distances are available. Our all-sky gamma-ray sensitivity map is useful for population syntheses. The electronic catalog version provides gamma-ray pulsar ephemerides, properties, and fit results to guide and be compared with modeling results.