Daily Papers

Evaluating test cases automatically generated by Large Language Models (LLMs) is a critical yet challenging task. Existing benchmarks often evaluate the exclusion ratio on large, unstructured collections of wrong codes, suffering from high computational costs and score inflation. Furthermore, they inadvertently reward generators that detect common, trivial bugs, while failing to penalize their inability to identify rare yet critical faults. In this work, we connect two fundamental questions: (1) What is the minimal set of wrong codes sufficient to represent the entire error space? and (2) What is the minimal set of test cases needed to distinguish them? We introduce a novel framework that formalizes benchmark construction as finding an optimal diagnostic basis in a binary code-test matrix, where rows represent wrong codes and columns represent test case results. The rank of this matrix specifies the minimal number of independent error patterns (wrong codes) and provides a tight upper bound on the number of test cases required for complete fault coverage. Our objective is to identify a basis of size equal to the matrix rank that maximizes internal diversity. To tackle this NP-hard problem, we propose WrongSelect, an efficient approximation algorithm to select maximally diverse wrong codes. Applying this framework to millions of competitive programming submissions, we construct TC-Bench, a compact, diverse, and inflation-resistant benchmark. Extensive experiments show that even the most advanced test case generation methods achieve only ~60% exclusion rates on TC-Bench, exposing a significant gap in their diagnostic power and highlighting substantial room for future improvement. Our dataset is available at: https://huggingface.co/datasets/Luoberta/TC-Bench and our code is at: https://github.com/Luowaterbi/TC-Bench.

Extreme-mass-ratio inspirals (EMRIs) are one of the primary targets for the recently adopted millihertz gravitational-wave (GW) observatory LISA. Some previous studies have argued that a fraction of all EMRIs form in matter-rich environments, and can potentially explain the dozens of soft X-ray band (sim 10^{-1} rm keV), low-frequency (sim 0.1 mHz) periodic phenomena known as quasi-periodic eruptions (QPEs) and quasi-periodic oscillations (QPOs). Here, using a representative EMRI population retrofitted with cutoffs on LISA-band SNRs and luminosity distances to account for the sensitivity of current instruments, we estimate the mean frequency band in which QPEs and QPOs originating from detectable LISA EMRIs may be emitting an X-ray signal ``today'' (i.e., in 2024) to be 0.46 pm 0.22 mHz. We also model the well-known QPO source, RE J1034+396, which falls in this frequency band, as an EMRI assuming its primary black hole mass to be 10^6-10^7 M_odot. Through a prior-predictive analysis, we estimate the orbiting compact object's mass to be 46^{+ 10}_{-40} M_odot and the source's LISA-band SNR as approx 14, highlighting it as a candidate multi-messenger EMRI target. We also highlight the role of current and near-future X-ray and UV observatories in enabling multi-messenger observations of EMRIs in conjunction with LISA, and conclude with a discussion of caveats of the current analysis, such as the exclusion of eccentricity and inclination from the model, and the measurability of sub-solar mass compact object EMRIs.

Magnitude of a finite metric space and the related notion of magnitude functions on metric spaces is an active area of research in algebraic topology. Magnitude originally arose in the context of biology, where it represents the number of effective species in an environment; when applied to a one-parameter family of metric spaces tX with scale parameter t, the magnitude captures much of the underlying geometry of the space. Prior work has mostly focussed on properties of magnitude in a global sense; in this paper we restrict the sets to finite subsets of Euclidean space and investigate its individual components. We give an explicit formula for the corrected inclusion-exclusion principle, and define a quantity associated with each point, called the moment which gives an intrinsic ordering to the points. We exploit this in order to form an algorithm which approximates the convex hull.

Instrumental variable methods have been widely used to identify causal effects in the presence of unmeasured confounding. A key identification condition known as the exclusion restriction states that the instrument cannot have a direct effect on the outcome which is not mediated by the exposure in view. In the health and social sciences, such an assumption is often not credible. To address this concern, we consider identification conditions of the population average treatment effect with an invalid instrumental variable which does not satisfy the exclusion restriction, and derive the efficient influence function targeting the identifying functional under a nonparametric observed data model. We propose a novel multiply robust locally efficient estimator of the average treatment effect that is consistent in the union of multiple parametric nuisance models, as well as a multiply debiased machine learning estimator for which the nuisance parameters are estimated using generic machine learning methods, that effectively exploit various forms of linear or nonlinear structured sparsity in the nuisance parameter space. When one cannot be confident that any of these machine learners is consistent at sufficiently fast rates to ensure n-consistency for the average treatment effect, we introduce a new criteria for selective machine learning which leverages the multiple robustness property in order to ensure small bias. The proposed methods are illustrated through extensive simulations and a data analysis evaluating the causal effect of 401(k) participation on savings.

The collision system and multiplicity dependence of chemical freeze-out temperature (T_{rm ch}) and strangeness saturation factor (γ_{s}) are obtained by studying the particle ratios at the Large Hadron Collider (LHC) energies. Here, we consider the new results in pp at 13 TeV, p+Pb at s_{rm NN} = 5.02 TeV, Xe+Xe at s_{rm NN} = 5.44 TeV and Pb+Pb at s_{rm NN} = 5.02 TeV along with the earlier results in pp at s = 7 TeV and Pb+Pb at s_{rm NN} = 2.76 TeV. A statistical thermal model is used to extract the chemical freeze-out parameters in different multiplicity classes. To understand the particle production from small to large collision systems two ensembles namely, canonical and grand canonical have been considered in this study. A clear observation of multiplicity dependence of T_{rm ch} and γ_{s} is observed. The values obtained in high-multiplicity pp collisions are found to be similar to the peripheral Pb+Pb collisions. A final state midrapidity charged particle multiplicity density of around 20-30 appears to be a threshold below which, the chemical freeze-out temperature is lower than the kinetic freeze-out temperature.

We interpret the recent excess in a rare decay of the Higgs boson, Hto Zgamma, using a light axion-like particle (ALP) in the massrange 0.05 - 0.1 GeV.The dominant decay of such a light ALP is into a pair of collimated photons, whose decay is required to happen before reaching the ECAL detector, such that it mimics a single photon in the detector. It can explain the excess with a coupling C^{rm eff}_{aZH} / Lambda sim 4 times 10^{-5};{rm GeV}^{-1}, while the decay of the ALP before reaching the ECAL requires the diphoton coupling C^{rm eff}_{gammagamma}/ Lambda ge 0.35 ,{rm TeV}^{-1} (0.1,{rm eV}/m_a)^2. A potential test would be the rare decay of the Z boson Z to a H^* to a (b bar b) at the Tera-Z option of the future FCC and CEPC. However, it has a branching ratio of only O(10^{-12}), and thus barely testable. The production cross section for pp to Z^* to a H via the same coupling C^{rm eff}_{aZH} / Lambda at the LHC is too small for detection.

The superconducting materials that make up an MKID have a significant effect on its performance. The T_c and normal state resistivity ρ_N of the film determine the penetration depth λ and therefore how much kinetic inductance it has. The ratio of kinetic inductance to total inductance (α), the volume of the inductor, and Q_m determines the magnitude of the response to incoming energy. The quasiparticle lifetime τ_qp is the characteristic time during which the MKID's surface impedance is modified by the incoming energy. Many materials have been explored for use in superconducting resonators and MKIDs, but that information is often not published or scattered around the literature. This chapter contains information and references on the work that has been done with thin film lithographed circuits for MKIDs over the last two decades. Note that measured material properties such as the internal loss quality factor Q_i and quasiparticle lifetime τ_qp vary significantly depending on how the MKID superconducting thin film is made and the system they are measured in, so it is best to interpret all stated values as typical but not definitive. Values are omitted in cases when there aren't enough measurements or there is too much disagreement in the literature to estimate a typical value. In order to be as complete as possible some unpublished results from the author's lab are included and can be identified by the lack of a reference. Unless noted all films are polycrystalline or amorphous.