arXiv subjects

Xingyu Yan

Publications and source records attributed to Xingyu Yan.

6 recordsLinked to original sources

arXiv2026

Ferroelectric switching of odd-parity magnon spin splitting

Magnetic symmetry can lift spin degeneracy in momentum space without producing net magnetization, generating spin textures classified by their parity under momentum reversal. Even-parity textures have well established in altermagnets. Odd-parity spin textures have recently emerged in electronic bands of compensated magnets, but their counterpart in collective bosonic excitations remains experimentally unresolved. Magnons provide a natural setting for this extension because they carry spin angular momentum through insulating magnets without accompanying charge flow. Beyond realizing this missing state, a broader challenge is to program spin splitting with voltage at room temperature. Here we report room-temperature transport evidence for ferroelectric switching odd-parity magnon spin splitting in multiferroic BiFeO3. Symmetry analysis and spin-wave calculations reveal that the cycloidal chirality splits opposite-spin magnon branches with an odd-in-momentum dependence and sets the sign. Experimentally, an injected in-plane-polarized spin current generates an out-of-plane magnon spin component during propagation. This component exhibits the crystalline angular dependence predicted by theory and reverses upon ferroelectric switching, providing a transport fingerprint of the spin-split magnon state. We use the programmed magnon spin to drive deterministic field-free switching of a perpendicular ferromagnet and demonstrate XNOR logic-in-memory. Our results extend odd-parity spin splitting from fermionic electronic states to bosonic collective modes and establish nonvolatile electrical control at room temperature.

cond-mat.mtrl-sci↗

arXiv2026

Zero-Knowledge Proofs of Quantumness

With the rapid development of quantum computers, proofs of quantumness have recently become an interesting research direction. However, in current schemes for proofs of quantumness, quantum provers face the risk of being maliciously exploited by classical verifiers. Through malicious strategies in interaction with quantum provers, classical verifiers could solve some instances of hard problems that arise from the specific scheme in use. This is due to the lack of formalization that prevents malicious verifiers from extracting useful information in proofs of quantumness. To address this issue, we formalize zero-knowledge proofs of quantumness. Intuitively, the zero-knowledge property necessitates that the information gained by the classical verifier from interactions with the quantum prover should not surpass what can be simulated using a simulated classical prover interacting with the same verifier. As a result, the new zero-knowledge notion can prevent a malicious verifier from exploiting quantum advantage. We find that the classical zero-knowledge proof is sufficient to compile some existing proofs of quantumness schemes into zero-knowledge proofs of quantumness schemes. It appears to be more general to require zero-knowledge proof on the verifier side instead of the prover side. This helps to regulate the verifier's behavior from malicious to be honest-but-curious. As a result, both parties will play not only one role in the proofs of quantumness but also the dual role in the classical zero-knowledge proof. Specifically, Shor's factoring-based scheme and the learning with errors-based scheme in [Brakerski et al., FOCS, 2018] can be transformed into zero-knowledge proofs of quantumness by requiring an extractable non-interactive zero-knowledge argument on the verifier side. Zero-knowledge proofs of quantumness can thus be viewed as an enhanced security notion for proofs of quantumness.

quant-ph↗

arXiv2026

Prediction-Powered Linear Regression: A Balance Between Interpretation and Prediction

Unlabeled data are increasingly prevalent in contemporary economic studies, yet their effective use for improving prediction remains challenging because the outcomes are often costly or even infeasible to observe. Machine learning methods can help label these data and achieve high predictive accuracy, but they often lack interpretability. In this paper, we propose a Prediction-powered Unified Model Averaging (PUMA) framework to combine linear regression and machine learning methods, achieving a balance between interpretation and prediction. Unlike existing studies on prediction-powered inference, our approach is the first to jointly address uncertainty arising from model misspecification, power tuning parameter selection, and the choice of machine learning algorithms by using model averaging. Theoretically, under mild conditions, we establish the in-sample and out-of-sample asymptotic prediction optimality, estimation consistency, and asymptotic distribution of the PUMA estimator. Extensive simulations and a real-world application further demonstrate the empirical advantages of the proposed method over existing state-of-the-art approaches.

stat.ME↗

arXiv2026

CTBench: Evaluating Troubleshooting Capabilities of AI Agents in Realistic Telecom Network Operations

Agents are increasingly considered for automating network operations and maintenance, where engineers must diagnose network faults, optimize configurations to enhance services, and reduce operational costs while acting under strict constraints. However, existing evaluations fail to accurately model real network characteristics or assess agents under partially observable telecom environments with diverse vendors, devices, protocols, and interfaces. In this paper, we introduce CTBench, a public benchmark for assessing whether an agent behaves like a competent telecom troubleshooting engineer. CTBench focuses on root cause analysis and path restoration. Each task is constructed by experts and annotated with rich task metadata, including golden evidence steps. CTBench uses expert-grounded metrics that evaluate both final answers and the diagnostic evidence. Experiments with representative harness-model combinations show that state-of-the-art agents perform very well at identifying endpoints in path-restoration tasks but, more generally, underperform in root cause analysis. In particular, agents struggle with interface state, link-layer, service-management, and other operational faults. Most importantly, even when agents produce plausible or correct final answers, they often fail to provide the evidence-grounded diagnoses required in operational practice. Our results further show that path restoration is generally more resource expensive, yet larger resource usage does not necessarily translate into better diagnosis.

cs.AI↗

arXiv2023

Frequentist Model Averaging for Global Fréchet Regression

To consider model uncertainty in global Fréchet regression and improve density response prediction, we propose a frequentist model averaging method. The weights are chosen by minimizing a cross-validation criterion based on Wasserstein distance. In the cases where all candidate models are misspecified, we prove that the corresponding model averaging estimator has asymptotic optimality, achieving the lowest possible Wasserstein distance. When there are correctly specified candidate models, we prove that our method asymptotically assigns all weights to the correctly specified models. Numerical results of extensive simulations and a real data analysis on intracerebral hemorrhage data strongly favour our method.

stat.ME↗

arXiv2023

Post-Quantum $κ$-to-1 Trapdoor Claw-free Functions from Extrapolated Dihedral Cosets

\emph{Noisy trapdoor claw-free function} (NTCF) as a powerful post-quantum cryptographic tool can efficiently constrain actions of untrusted quantum devices. However, the original NTCF is essentially \emph{2-to-1} one-way function (NTCF$^1_2$). In this work, we attempt to further extend the NTCF$^1_2$ to achieve \emph{many-to-one} trapdoor claw-free functions with polynomial bounded preimage size. Specifically, we focus on a significant extrapolation of NTCF$^1_2$ by drawing on extrapolated dihedral cosets, thereby giving a model of NTCF$^1_κ$ where $κ$ is a polynomial integer. Then, we present an efficient construction of NTCF$^1_κ$ assuming \emph{quantum hardness of the learning with errors (LWE)} problem. We point out that NTCF can be used to bridge the LWE and the dihedral coset problem (DCP). By leveraging NTCF$^1_2$ (resp. NTCF$^1_κ$), our work reveals a new quantum reduction path from the LWE problem to the DCP (resp. extrapolated DCP). Finally, we demonstrate the NTCF$^1_κ$ can naturally be reduced to the NTCF$^1_2$, thereby achieving the same application for proving the quantumness.

cs.CR↗