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Zhaohui Li

Publications and source records attributed to Zhaohui Li.

At least 19 recordsLinked to original sources
arXiv2026

Unconstrained support points can leave a convex support

Support points provide a practical and effective way to approximate a probability distribution by a small set of equally weighted representatives through energy-distance minimization. In many applications, the variables satisfy convex constraints that the representatives should also respect. We investigate whether unconstrained support points automatically satisfy such constraints and show that this is not guaranteed. We construct counterexamples using three planar observations, a density with full triangular support, and positive-definite matrix data admitting an indefinite optimal representative under the Frobenius norm. We also identify cases in which containment holds, establish stability under distributional perturbations, and quantify the objective cost of imposing a convex-hull constraint. For a fixed number of representatives and a positive population constraint gap, empirical configurations with vanishing global objective error eventually leave the sample convex hull almost surely.

math.ST↗
arXiv2026

Certified computation of Buehler confidence limits for system reliability

Component tests can support a lower confidence limit for a system that has not been tested as a whole, but numerical optimization can compromise its coverage. A feasible point in the minimization defining a Buehler limit gives an upper bound on that limit; reporting it can therefore overstate reliability. We develop a computation that maintains lower and upper bounds on the ideal endpoint for a prescribed ordering. Bounds over entire parameter regions justify the reported lower endpoint at every completed checkpoint, including under adaptive stopping and selection among runs. Separate refinement conditions establish convergence, and an explicit error allocation accounts for simulated tail probabilities and confidence-region localization. The theory retains nuisance and dependence parameters in the observation model. Monotone corner bounds give the implemented specialization for scalar and two-component binomial systems; Bernstein and probability-distance bounds provide further theoretical constructions. Four benchmark problems reach a certified width of $10^{-5}$ under each of four search strategies. Additional stochastic candidates increase cost without reducing subdivisions, whereas complete sample-space enumeration verifies the implemented coverage at three parameter vectors. For the all-success example, the certified lower limit is $0.68128$, compared with $0.41738$ from Bonferroni-adjusted component limits.

stat.AP↗
arXiv2026

chATLAS: A Retrieval-Augmented Conversational Assistant for the ATLAS Experiment

Large scientific collaborations produce extensive internal documentation spread across heterogeneous systems, making it difficult for collaborators to locate and reuse existing knowledge. We present chATLAS, a deployed Retrieval-Augmented Generation (RAG) assistant developed for the ATLAS experiment at CERN. The system provides conversational access to more than 30,000 documents from several collaboration resources, material largely unavailable to general-purpose language models, using hybrid dense--sparse retrieval deployed on CERN infrastructure. A domain-specific benchmark built from automatically generated question--answer pairs is used to evaluate retrieval and generation performance and to guide design choices. We study the selection and domain-specific fine-tuning of an embedding model, contextual retrieval, ranking strategies, and generator choice. Fine-tuning combined with contextual retrieval more than doubles how often the correct document is returned as the top result on the most technically challenging documentation source. The deployed system also includes intent routing, safety handling, Mattermost integration, and an MCP server for future agentic workflows. chATLAS demonstrates that domain-adapted RAG can provide source-attributed, domain-specific access to collaboration knowledge, and offers a practical foundation for scientific collaborations and other organisations that rely on accurate, up-to-date retrieval from proprietary documentation.

cs.IR↗
arXiv2026

High-dimensional Supermode Photonics Enabled by Hierarchical Supersymmetric Transformation

Modes provide a fundamental degree of freedom for photonic information processing, yet conventional multimode waveguides exhibit non-equidistant effective-index distributions, making closely spaced modes vulnerable to intermodal crosstalk. Supermode photonics can overcome this limitation by geometrically engineering coupled waveguide arrays to realize large and equidistant effective-index spacing, but precise supermode excitation and detection remain challenging at the subwavelength scale. Here, we report a hierarchical second-order discrete supersymmetric (DSUSY) transformation method that enables high-purity excitation and extraction of arbitrary target supermodes in a compact and scalable architecture. We experimentally demonstrate six-supermode multiplexing systems on silicon-on-insulator and silicon nitride platforms. Benefiting from the large supermode index spacing and the isospectrality of DSUSY transformations, the fabricated devices exhibit low insertion losses (<2.6 dB) and intermodal crosstalk (<-11.1 dB) for all channels over a 100-nm wavelength range. A high-speed transmission experiment on the silicon device achieves an aggregate data rate of 1.2 Tbit/s, with all channel bit error rates below the 7% hard-decision forward-error-correction threshold. The method can further support polarization-insensitive architectures, enabling compact polarization-supermode hybrid multiplexing. This work provides a scalable route toward high-dimensional supermode photonics for high-capacity optical interconnects, highly parallel AI optical computing, and high-dimensional quantum information processing.

physics.optics↗
arXiv2026

Electrical manipulation and detection of perpendicular altermagnetic order via proximitized Dirac semimetal

Altermagnets, which combine antiferromagnetic-like magnetic compensation with ferromagnetic-like broken time-reversal symmetry, hold great promise for high-density and ultrafast spintronic applications. However, the detection and switching of perpendicular altermagnetic order are fundamentally constrained by magnetic symmetry, restricting both fundamental studies and practical implementation. We realize robust electrical reading and deterministic switching of perpendicular altermagnetic order by designing a Dirac semimetal/altermagnet heterostructure of PtTe2/CrSb. This engineered interface enables anomalous Hall readout via altermagnetic proximity effect and delivers efficient spin-orbit torque for manipulating the epitaxial perpendicular Neel vector in CrSb. These findings significantly broaden the functional scope of altermagnetic heterostructures and pave the way for highly scalable altermagnetic memory.

cond-mat.mtrl-sci↗
arXiv2026

SciEval: A Benchmark for Automatic Evaluation of K-12 Science Instructional Materials

The need to evaluate instructional materials for K-12 science education has become increasingly important, as more educators use generative AI to create instructional materials. However, the review of instructional materials is time-consuming, expertise-intensive, and difficult to scale, motivating interest in automated evaluation approaches. While large language models (LLMs) have shown strong performance on general evaluation tasks, their performance and reliability on instructional materials remain unclear. To address this gap, we formulate Automatic Instructional Materials Evaluation (AIME) as a generative AI task that predicts scores and evidence using the rubric designed by the educator. We create a benchmark dataset and develop baseline models for AIME. First, we curate the first AIME dataset, SciEval, consisting of instructional materials annotated with pedagogy-aligned evaluation scores and evidence-based rationales. Expert annotations achieve high inter-rater reliability, resulting in a dataset of 273 lesson-level instructional materials evaluated across 13 criteria (N=3549) using the EQuIP rubric. Second, we test mainstream LLMs (GPT, Gemini, Llama, and Qwen) on SciEval and find that none achieve strong performance. Then we fine-tune Qwen3 on SciEval. Results on a held-out test set show that domain-aligned fine-tuning can achieve up to 11 percent performance gains, highlighting the importance of domain-specific fine-tuning for AIME and facilitating the use of LLMs in other educational tasks.

cs.AI↗
arXiv2026

Integrated Supermode Photonics Enabled by Supersymmetric Transformation

We report a systematic methodology to obtain supermodes with equidistant effective index distribution and to excite arbitrary target supermodes with high precision. By employing a multi-well optical potential realized by a judiciously designed waveguide array, the supported supermodes achieve maximal spacing and an equidistant distribution in effective index. More importantly, we develop a 2nd-order discrete supersymmetric (DSUSY) transformation method that enables the excitation and detection of two supermodes at the same time and can be extended to any number of supermodes via simple cascading. Together, these findings overcome the long-standing bottlenecks in integrated supermode photonics and provide an intrinsically scalable route towards harnessing supermodes as a new degree of freedom for encoding, transmitting, and processing information. We experimentally demonstrate the feasibility and universality of this method by realizing two- and four-supermode multiplexing systems. Benefitting from the large effective index spacing between supermodes and the isospectral nature of the DSUSY transformation, the fabricated devices show low insertion losses (< 2.48 dB at 1550 nm) and intermodal crosstalk (< -18 dB at 1550 nm) for all mode channels over a 100-nm wavelength range (1500-1600 nm). The high-speed data transmission experiment performed on the four-channel system achieves an aggregate data rate of 1.024 Tb/s while maintaining considerably low bit error rates, underscoring the potential of supermode photonics for high-capacity on-chip optical communications. This work lays the foundation for integrated supermode photonics, which uses supermodes as a new degree of freedom for light manipulation and opens new avenues for supermode-based applications including but not limited to on-chip optical communications, intelligent optical computing and quantum information technologies.

physics.optics↗
arXiv2026

Judging the Judges: Human Validation of Multi-LLM Evaluation for High-Quality K--12 Science Instructional Materials

Designing high-quality, standards-aligned instructional materials for K--12 science is time-consuming and expertise-intensive. This study examines what human experts notice when reviewing AI-generated evaluations of such materials, aiming to translate their insights into design principles for a future GenAI-based instructional material design agent. We intentionally selected 12 high-quality curriculum units across life, physical, and earth sciences from validated programs such as OpenSciEd and Multiple Literacies in Project-based Learning. Using the EQuIP rubric with 9 evaluation items, we prompted GPT-4o, Claude, and Gemini to produce numerical ratings and written rationales for each unit, generating 648 evaluation outputs. Two science education experts independently reviewed all outputs, marking agreement (1) or disagreement (0) for both scores and rationales, and offering qualitative reflections on AI reasoning. This process surfaces patterns in where LLM judgments align with or diverge from expert perspectives, revealing reasoning strengths, gaps, and contextual nuances. These insights will directly inform the development of a domain-specific GenAI agent to support the design of high-quality instructional materials in K--12 science education.

cs.CY↗
arXiv2026

Observation of magnon torques mediated by orbital hybridization at the light metal/antiferromagnetic insulator interface

Magnon torques, which can operate without involving moving electrons, could circumvent the Joule heating issue. In conventional magnon torque systems, the spin source layer with strong spin-orbit coupling is utilized to inject magnons, and the efficiency is limited by the inherent spin Hall conductivity of the spin source layer. In this work, we observe magnon torques in the Cr/NiO/ferromagnet heterostructure with the effective spin Hall conductivity of 2.45x10^5 hbar/(2eΩm), twice that of the best conventional magnon torque system. We demonstrate the magnon-torque-driven switching of a perpendicularly magnetized CoFeB layer at room temperature, with a switching power consumption density of 0.136 mW/μm^2. We find that the magnon torque originates from the orbital hybridization and interfacial inversion symmetry breaking at the Cr/NiO interface. Our findings not only significantly enhance the efficiency of magnon torques, but also provide key insights into the fundamental mechanisms of magnon injections.

cond-mat.mtrl-sci↗
arXiv2026

Quaternion optical computing chip for parallel high-dimensional data processing

Optical computing chips have emerged as a transformative computing technology due to their high computational density, low energy consumption, and compact footprint. While real- and complex-valued computing chips have been well developed, their fundamental limitations in representing high-dimensional data significantly constrain their applicability in modern signal processing. Quaternions enable direct operations on three- and four-dimensional data, powering high-dimensional processing in data analytics and artificial intelligence. Here we demonstrate a quaternion optical computing chip (QOCC) for the first time and benchmark its performance in several typical application scenarios: three-dimensional point cloud processing, RGB chromatic transformation, and quaternion convolutional neural network for color image recognition. The QOCC harnesses high parallelism of light by wavelength-division multiplexing, processing high-dimensional data simultaneously through multiple optical wavelength channels. Compared to the electronic computing counterpart, our QOCC achieves higher computational fidelity (root mean square error < 0.035) and substantially reduced computational load (2/3 lower). It paves the way towards next-generation optical computing, overcoming the limitations of traditional computing systems in high-dimensional data processing.

physics.optics↗
arXiv2025

Electrically Reconfigurable Arbitrary Splitting-Ratio Optical Splitter Based on Low-Loss Sb2Se3

Reconfigurable beam splitters capable of being arbitrarily programmed for the power splitting ratios are vital for the adaptive optical networks and photonic computing. Conventional mechanisms such as thermo-optic, free-carrier, or mechanical tuning are usually volatile and require continuous power, limiting their suitability for low-frequency and low power-consumption programmable operations. Here, we experimentally demonstrate an electrically reconfigurable beam splitter based on the low-loss phase-change material Sb2Se3, enabling multi-level and arbitrary splitting-ratio (SR) control. By locally triggering phase transitions in the coupling region with integrated micro-electrodes, we exploit the high refractive-index contrast between different phases and negligible absorption in the near-infrared wavelength of Sb2Se3 to precisely tune the coupling strength with non-volatile retention. 8-level of power splitting states is achieved within a compact footprint of ~14.5-μm in the experiments, with insertion loss is ~1 dB across 1515-1550 nm and near-zero static power. Combining the advantages of compactness, broad bandwidth, low loss, non-volatility, and multi-level control experimentally, this device provides a universal building block for scalable, energy-efficient reconfigurable photonic circuits, with great prospects in optical computing and intelligent communication systems.

physics.optics↗
arXiv2025

Transform-Resampled Double Bootstrap Percentile with Applications in System Reliability Assessment

System reliability assessment(SRA) is a challenging task due to the limited experimental data and the complex nature of the system structures. Despite a long history dating back to \cite{buehler1957confidence}, exact methods have only been applied to SRA for simple systems. High-order asymptotic methods, such as the Cornish-Fisher expansion, have become popular for balancing computational efficiency with improved accuracy when data are limited, but frequently encounter the "bend-back" problem in high-reliability scenarios and require complex analytical computations. To overcome these limitations, we propose a novel method for SRA by modifying the double bootstrap framework, termed the double bootstrap percentile with transformed resamples. In particular, we design a nested resampling process for log-location-scale lifetime models, eliminating the computational burden caused by the iterative resampling process involved in the conventional double bootstrap. We prove that the proposed method maintains the high-order convergence property, thus providing a highly accurate yet computationally efficient confidence limit for system reliability. Moreover, the proposed procedure is straightforward to implement, involving only a simple resampling operation and efficient moment estimation steps. Numerical studies further demonstrate that our approach outperforms the state-of-the-art SRA methods and, at the same time, is much less susceptible to the bend-back issue.

stat.ME↗
arXiv2025

Optimized and regularly repeated lattice-based Latin hypercube designs for large-scale computer experiments

Computer simulations serve as powerful tools for scientists and engineers to gain insights into complex systems. Less costly than physical experiments, computer experiments sometimes involve large number of trials. Conventional design optimization and model fitting methods for computer experiments are inefficient for large-scale problems. In this paper, we propose new methods to optimize good lattice point sets, using less computation to construct designs with enhanced space-filling properties such as high separation distance, low discrepancy, and high separation distance on projections. These designs show promising performance in uncertainty quantification as well as physics-informed neural networks. We also propose a new type of space-filling design called regularly repeated lattice-based Latin hypercube designs, which contain lots of local space-filling Latin hypercube designs as subdesigns. Such designs facilitate rapid fitting of multiple local Gaussian process models in a moving window type of modeling approach and thus are useful for large-scale emulation problems.

stat.ME↗
arXiv2025

Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability

Recent advancements in cognitive science and multi-round reasoning techniques for Large Language Models (LLMs) suggest that iterative thinking processes improve problem-solving performance in complex tasks. Inspired by this, approaches like Chain-of-Thought, debating, and self-refinement have been applied to auto-regressive LLMs, achieving significant successes in tasks such as mathematical reasoning, commonsense reasoning, and multi-hop question answering. Despite these successes, the theoretical basis for how multi-round reasoning enhances problem-solving abilities remains underexplored. In this work, we investigate the approximation, learnability, and generalization properties of multi-round auto-regressive models. We show that Transformers with finite context windows are universal approximators for steps of Turing-computable functions and can approximate any Turing-computable sequence-to-sequence function through multi-round reasoning. We extend PAC learning to sequence generation and demonstrate that multi-round generation is learnable even when the sequence length exceeds the model's context window. Finally, we examine how generalization error propagates across rounds, and show how the aforementioned approaches can help constrain this error, ensuring outputs stay within an expectation boundary. This work sheds light on the systemic theoretical foundations of multi-round sequence learning and reasoning, emphasizing its role in inference complexity.

cs.AI↗
arXiv2024

O-MAGIC: Online Change-Point Detection for Dynamic Systems

The capture of changes in dynamic systems, especially ordinary differential equations (ODEs), is an important and challenging task, with multiple applications in biomedical research and other scientific areas. This article proposes a fast and mathematically rigorous online method, called ODE-informed MAnifold-constrained Gaussian process Inference for Change point detection(O-MAGIC), to detect changes of parameters in the ODE system using noisy and sparse observation data. O-MAGIC imposes a Gaussian process prior to the time series of system components with a latent manifold constraint, induced by restricting the derivative process to satisfy ODE conditions. To detect the parameter changes from the observation, we propose a procedure based on a two-sample generalized likelihood ratio (GLR) test that can detect multiple change points in the dynamic system automatically. O-MAGIC bypasses conventional numerical integration and achieves substantial savings in computation time. By incorporating the ODE structures through manifold constraints, O-MAGIC enjoys a significant advantage in detection delay, while following principled statistical construction under the Bayesian paradigm, which further enables it to handle systems with missing data or unobserved components. O-MAGIC can also be applied to general nonlinear systems. Simulation studies on three challenging examples: SEIRD model, Lotka-Volterra model and Lorenz model are provided to illustrate the robustness and efficiency of O-MAGIC, compared with numerical integration and other popular time-series-based change point detection benchmark methods.

stat.AP↗
arXiv2024

Joint Training for Selective Prediction

Classifier models are prevalent in natural language processing (NLP), often with high accuracy. Yet in real world settings, human-in-the-loop systems can foster trust in model outputs and even higher performance. Selective Prediction (SP) methods determine when to adopt a classifier's output versus defer to a human. Previous SP approaches have addressed how to improve softmax as a measure of model confidence, or have developed separate confidence estimators. One previous method involves learning a deferral model based on engineered features. We introduce a novel joint-training approach that simultaneously optimizes learned representations used by the classifier module and a learned deferral policy. Our results on four classification tasks demonstrate that joint training not only leads to better SP outcomes over two strong baselines, but also improves the performance of both modules.

cs.CL↗
arXiv2024

Novel Subsampling Strategies for Heavily Censored Reliability Data

Computational capability often falls short when confronted with massive data, posing a common challenge in establishing a statistical model or statistical inference method dealing with big data. While subsampling techniques have been extensively developed to downsize the data volume, there is a notable gap in addressing the unique challenge of handling extensive reliability data, in which a common situation is that a large proportion of data is censored. In this article, we propose an efficient subsampling method for reliability analysis in the presence of censoring data, intending to estimate the parameters of lifetime distribution. Moreover, a novel subsampling method for subsampling from severely censored data is proposed, i.e., only a tiny proportion of data is complete. The subsampling-based estimators are given, and their asymptotic properties are derived. The optimal subsampling probabilities are derived through the L-optimality criterion, which minimizes the trace of the product of the asymptotic covariance matrix and a constant matrix. Efficient algorithms are proposed to implement the proposed subsampling methods to address the challenge that optimal subsampling strategy depends on unknown parameter estimation from full data. Real-world hard drive dataset case and simulative empirical studies are employed to demonstrate the superior performance of the proposed methods.

stat.ME↗