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Researcher profile

Zhen Guo

Zhen Guo contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.SRComputation and Languageeess.SPastro-ph.EPastro-ph.GAcond-mat.quant-gasCryptography and Securityeess.IVeess.SYMachine Learningmath.DGphysics.atom-phSoftware EngineeringSystems and Control

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Published work

9 published item(s)

preprint2026arXiv

CL-bench Life: Can Language Models Learn from Real-Life Context?

Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move beyond professional settings into everyday life, the nature of the contexts they must handle also shifts. Real-life contexts are often messy, fragmented, and deeply tied to personal and social experience, such as multi-party conversations, personal archives, and behavioral traces. Yet it remains unclear whether current frontier language models can reliably learn from such contexts and solve tasks grounded in them. To this end, we introduce CL-bench Life, a fully human-curated benchmark comprising 405 context-task pairs and 5,348 verification rubrics, covering common real-life scenarios. Solving tasks in CL-bench Life requires models to reason over complex, messy real-life contexts, calling for strong real-life context learning abilities that go far beyond those evaluated in existing benchmarks. We evaluate ten frontier LMs and find that real-life context learning remains highly challenging: even the best-performing model achieves only 19.3% task solving rate, while the average performance across models is only 13.8%. Models still struggle to reason over contexts such as messy group chat histories and fragmented behavioral records from everyday life. CL-bench Life provides a crucial testbed for advancing real-life context learning, and progress on it can enable more intelligent and reliable AI assistants in everyday life.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Towards Understanding and Characterizing Vulnerabilities in Intelligent Connected Vehicles through Real-World Exploits

Intelligent Connected Vehicles (ICVs) are a core component of modern transportation systems, and their security is crucial as it directly relates to user safety. Despite prior research, most existing studies focus only on specific sub-components of ICVs due to their inherent complexity. As a result, there is a lack of systematic understanding of ICV vulnerabilities. Moreover, much of the current literature relies on human subjective analysis, such as surveys and interviews, which tends to be high-level and unvalidated, leaving a significant gap between theoretical findings and real-world attacks. To address this issue, we conducted the first large-scale empirical study on ICV vulnerabilities. We began by analyzing existing ICV security literature and summarizing the prevailing taxonomies in terms of vulnerability locations and types. To evaluate their real-world relevance, we collected a total of 649 exploitable vulnerabilities, including 592 from eight ICV vulnerability discovery competitions, Anonymous Cup, between January 2023 and April 2024, covering 48 different vehicles. The remaining 57 vulnerabilities were submitted daily by researchers. Based on this dataset, we assessed the coverage of existing taxonomies and identified several gaps, discovering one new vulnerability location and 13 new vulnerability types. We further categorized these vulnerabilities into 6 threat types (e.g., privacy data breach) and 4 risk levels (ranging from low to critical) and analyzed participants' skills and the types of ICVs involved in the competitions. This study provides a comprehensive and data-driven analysis of ICV vulnerabilities, offering actionable insights for researchers, industry practitioners, and policymakers. To support future research, we have made our vulnerability dataset publicly available.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Large amplitude periodic outbursts and long period variables in the VVV VIRAC2-$β$ database

The VISTA Variables in the Via Lactea (VVV) survey obtained near-infrared photometry toward the Galactic bulge and the southern disc plane for a decade (2010 - 2019). We designed a modified Lomb-Scargle method to search for large-amplitude ($Δ$Ks > 1.5 mag) mid to long-term periodic variables (P > 10 d) in the 2nd version of VVV Infrared Astrometric Catalogue (VIRAC2-$β$). In total, 1520 periodic sources were discovered, including 59 candidate periodic outbursting young stellar objects (YSOs), based on the unique morphology of the phase-folded light curves, proximity to Galactic HII regions and mid-infrared colours. Five sources are spectroscopically confirmed as accreting YSOs. Both fast-rise/slow-decay and slow-rise/fast-decay periodic outbursts were found, but fast-rise/slow-decay outbursts predominate at the highest amplitudes. The multi-wavelength colour variations are consistent with a variable mass accretion process, as opposed to variable extinction. The cycles are likely to be caused by dynamical perturbations from stellar or planetary companions within the circumstellar disc. An additional search for periodic variability amongst YSO candidates in published Spitzer-based catalogues yielded a further 71 candidate periodic accretors, mostly with lower amplitudes. These resemble cases of pulsed accretion but with unusually long periods and greater regularity. The majority of other long-period variables are pulsating dusty Miras with smooth and symmetric light curves. We find that some Miras have redder $W3 - W4$ colours than previously thought, most likely due to their surface chemical compositions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Minimal Norm Tensors Principle and its Applications

In this paper we study the minimal norm tensors for general third covariant tensors and fourth covariant tensors, using this we can give a new explanation of Weyl tensor and Cotten tensor: Weyl tensor is the minimal norm tensor of Riemannian curvature tensor and Cotten tensor is the minimal norm tensor of divergence of Riemannian curvature tensor, and we also get some useful inequalities by computation the norm of minimal norm tensors.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Physics-assisted Generative Adversarial Network for X-Ray Tomography

X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory results. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known a priori, deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step algorithm for tomographic reconstruction. In contrast to previous efforts, our PGAN utilizes maximum-likelihood estimates derived from the measurements to regularize the reconstruction with both known physics and the learned prior. Compared with methods with less physics assisting in training, PGAN can reduce the photon requirement with limited projection angles to achieve a given error rate. The advantages of using a physics-assisted learned prior in X-ray tomography may further enable low-photon nanoscale imaging.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The ODYSSEUS Survey. Motivation and First Results: Accretion, Ejection, and Disk Irradiation of CVSO 109

The Hubble UV Legacy Library of Young Stars as Essential Standards (ULLYSES) Director's Discretionary Program of low-mass pre-main-sequence stars, coupled with forthcoming data from ALMA and JWST, will provide the foundation to revolutionize our understanding of the relationship between young stars and their protoplanetary disks. A comprehensive evaluation of the physics of disk evolution and planet formation requires understanding the intricate relationships between mass accretion, mass outflow, and disk structure. Here we describe the Outflows and Disks around Young Stars: Synergies for the Exploration of ULLYSES Spectra (ODYSSEUS) Survey and present initial results of the classical T Tauri Star CVSO 109 in Orion OB1b as a demonstration of the science that will result from the survey. ODYSSEUS will analyze the ULLYSES spectral database, ensuring a uniform and systematic approach in order to (1) measure how the accretion flow depends on the accretion rate and magnetic structures, (2) determine where winds and jets are launched and how mass-loss rates compare with accretion, and (3) establish the influence of FUV radiation on the chemistry of the warm inner regions of planet-forming disks. ODYSSEUS will also acquire and provide contemporaneous observations at X-ray, optical, NIR, and millimeter wavelengths to enhance the impact of the ULLYSES data. Our goal is to provide a consistent framework to accurately measure the level and evolution of mass accretion in protoplanetary disks, the properties and magnitudes of inner-disk mass loss, and the influence of UV radiation fields that determine ionization levels and drive disk chemistry.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards Building an Open-Domain Dialogue System Incorporated with Internet Memes

In recent years, Internet memes have been widely used in online chatting. Compared with text-based communication, conversations become more expressive and attractive when Internet memes are incorporated. This paper presents our solutions for the Meme incorporated Open-domain Dialogue (MOD) Challenge of DSTC10, where three tasks are involved: text response modeling, meme retrieval, and meme emotion classification. Firstly, we leverage a large-scale pre-trained dialogue model for coherent and informative response generation. Secondly, based on interaction-based text-matching, our approach can retrieve appropriate memes with good generalization ability. Thirdly, we propose to model the emotion flow (EF) in conversations and introduce an auxiliary task of emotion description prediction (EDP) to boost the performance of meme emotion classification. Experimental results on the MOD dataset demonstrate that our methods can incorporate Internet memes into dialogue systems effectively.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Double Degenerate Bose-Fermi Mixture of Strontium and Lithium

We report on the attainment of a degenerate Fermi gas of $\rm^{6}Li$ in contact with a Bose-Einstein condensate (BEC) of $^{84}$Sr. A degeneracy of $T/T_F=0.33(3)$ is observed with $1.6\times10^5$ $^{6}$Li atoms in the two lowest energy hyperfine states together with an almost pure BEC of $3.1\times10^5$ $^{84}$Sr atoms. The elastic s-wave scattering length between $^6$Li and $^{84}$Sr is estimated to be $|a_{\rm^{6}Li-\rm^{84}Sr}|=(7.1_{-1.7}^{+2.6})a_0$ ($a_0$ being the Bohr radius) from measured interspecies thermalization rates in an optical dipole trap.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

OTHR multitarget tracking with a GMRF model of ionospheric parameters

The ionosphere is the propagation medium for radio waves transmitted by an over-the-horizon radar (OTHR). Ionospheric parameters, typically, virtual ionospheric heights (VIHs), are required to perform coordinate registration for OTHR multitarget tracking and localization. The inaccuracy of ionospheric parameters has a significant deleterious effect on the target localization of OTHR. Therefore, to improve the localization accuracy of OTHR, it is important to develop accurate models and estimation methods of ionospheric parameters and the corresponding target tracking algorithms. In this paper, we consider the variation of the ionosphere with location and the spatial correlation of the ionosphere in OTHR target tracking. We use a Gaussian Markov random field (GMRF) to model the VIHs, providing a more accurate representation of the VIHs for OTHR target tracking. Based on expectation-conditional maximization and GMRF modeling of the VIHs, we propose a novel joint optimization solution, called ECM-GMRF, to perform target state estimation, multipath data association and VIHs estimation simultaneously. In ECM-GMRF, the measurements from both ionosondes and OTHR are exploited to estimate the VIHs, leading to a better estimation of the VIHs which improves the accuracy of data association and target state estimation, and vice versa. The simulation indicates the effectiveness of the proposed algorithm.

0 citations0 reviewsNo code linkedOpen access