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A community of researchers appears to think that a machine can be surprised and have introduced various surprise measures, principally the Shannon surprise and the Bayesian surprise. The questions of what constitutes a surprise and how to react to one still elicit debates. In this work, we introduce mutual information surprise (MIS), a new framework that redefines surprise not as an anomaly measure, but as a signal of epistemic growth. Furthermore, we develop a statistical test sequence that could trigger a surprise reaction and propose a MIS-based reaction policy that dynamically governs system behavior through sampling adjustment and process forking. Empirical evaluations—on both synthetic domains and a dynamic pollution map estimation task—show that a system governed by the MIS-based reaction policy significantly outperforms those under classical surprise-based approaches in stability, responsiveness, and predictive accuracy. The important implication of our new proposal is that MIS quantifies the impact of new observations on mutual information, shifts surprise from reactive to reflective, enables reflection on learning progression, and thus offers a path toward self-aware and adaptive autonomous systems. We expect the new surprise measure to play a critical role in further advancing autonomous systems on their ability to learn and adapt in a complex and dynamic environment. History: Kwok Tsui served as the senior editor for this article. Funding: This work was supported in part by the National Science Foundation [Grant CNS-2328395]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijds.2026.0182 . 

This paper is concerned with the problem of how to speed up computation for Gaussian process models trained on autocorrelated data. The Gaussian process model is a powerful tool commonly used in nonlinear regression applications. Standard regression modeling assumes random samples and an independently, identically distributed noise. Various fast approximations that speed up Gaussian process regression work under this standard setting. But for autocorrelated data, failing to account for autocorrelation leads to a phenomenon known as temporal overfitting that deteriorates model performance on new test instances. To handle autocorrelated data, existing fast Gaussian process approximations have to be modified; one such approach is to segment the originally correlated data points into blocks in which the blocked data are de-correlated. This work explains how to make some of the existing Gaussian process approximations work with blocked data. Numerical experiments across diverse application data sets demonstrate that the proposed approaches can remarkably accelerate computation for Gaussian process regression on autocorrelated data without compromising model prediction performance. History: Bianca Colosimo served as the senior editor for this article. Funding: Y. Ding received partial support from the National Science Foundation (NSF) [Grant CNS–2328395]. M. Katzfuss received partial support from the NSF [Grant DMS–1953005] and by the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin–Madison with funding from the Wisconsin Alumni Research Foundation. A. Chokhachian’s research was sponsored by the Ocean Energy Safety Institute Consortium (OESIC) through a grant from the U.S. Department of the Interior, Bureau of Safety and Environmental Enforcement (BSEE), and the U.S. Department of Energy (DOE) and was accomplished under Agreement Number E21AC00000. 

This paper introduces SecRepoBench, a benchmark to evaluate code agents on secure code completion in real-world repositories. SecRepoBench has 318 code completion tasks in 27 C/C++ repositories, covering 15 CWEs. We evaluate 29 standalone LLMs and 15 code agents across 3 state-of-the-art agent frameworks using our benchmark. We find that state-of-the-art LLMs struggle with generating correct and secure code completions. However, code agents significantly outperform standalone LLMs. We show that SecRepoBench is more difficult than the prior state-of-the-art benchmark. Finally, our comprehensive analysis provides insights into potential directions for enhancing the ability of code agents to write correct and secure code in real-world repositories. 

SecRepoBench is a repository-level secure code completion benchmark. It contains 318 code completion tasks obtained from 27 popular GitHub C/C++ repositories covering 15 CWEs. Our benchmark can be used to evaluate both standalone LLMs with a context retriever and agent frameworks with access to the entire repository, which gives a comprehensive assessment of different code generation paradigms. 

A<sc>bstract</sc> We study the azimuthal angle dependence of the energy-energy correlators$$\langle \mathcal{E}\left({\widehat{n}}_{1}\right)\mathcal{E}\left({\widehat{n}}_{2}\right)\rangle $$in the back-to-back region fore⁺e⁻annihilation and deep inelastic scattering (DIS) processes with general polarization of the proton beam. We demonstrate that the polarization information of the beam and the underlying partons from the hard scattering is propagated into the azimuthal angle dependence of the energy-energy correlators. In the process, we define the Collins-type EEC jet functions and introduce a new EEC observable using the lab-frame angles in the DIS process. Furthermore, we extend our formalism to explore the two-point energy correlation between hadrons with different quantum numbers$${\mathbb{S}}_{i}$$in the back-to-back limit$$\langle {\mathcal{E}}_{{\mathbb{S}}_{1}}\left({\widehat{n}}_{1}\right){\mathcal{E}}_{{\mathbb{S}}_{2}}\left({\widehat{n}}_{2}\right)\rangle $$. We find that in the Operator Product Expansion (OPE) region the nonperturbative information is entirely encapsulated by a single number. Using our formalism, we present several phenomenological studies that showcase how energy correlators can be used to probe transverse momentum dependent structures. 

A<sc>bstract</sc> We apply the joint threshold and transverse momentum dependent (TMD) factorization theorem to introduce new threshold-TMD distribution functions, including threshold-TMD parton distribution functions (PDFs) and fragmentation functions (FFs). We apply Soft-Collinear Effective Theory and renormalization group methods to carry out QCD evolution for both threshold-TMD PDFs and FFs. We show the universality of threshold-TMD functions among three standard processes, i.e. the Drell-Yan production inppcollisions, semi-inclusive deep-inelastic scattering and back-to-back two hadron production ine⁺e⁻collisions. In the end, we present the numerical predictions for different threshold-TMD functions and also transverse momentum distributions atpp,ep, ande⁺e⁻collisions.