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Lei Wang

Publications and source records attributed to Lei Wang.

At least 127 records · Page 7Linked to original sources

On the regularity of irreducible subgroups of finite classical groups

Let $G$ be a finite group and let $τ= (H_1, \ldots, H_t)$ be a $t$-tuple of core-free subgroups of $G$. We say that $τ$ is regular if $G$ contains elements $g_1, \ldots, g_t$ such that $\bigcap_i H_i^{g_i} = 1$, which is equivalent to the existence of a regular $G$-orbit on the Cartesian product $G/H_1 \times \cdots \times G/H_t$. Regular tuples were first investigated by Anagnostopoulou-Merkouri and Burness in a paper from 2024, partly motivated by the aim of seeking a natural generalisation of the classical and widely studied concept of a base for a transitive permutation group, which aligns with the special case where the $H_i$ are pairwise conjugate subgroups. In this paper, we focus on the case where $G$ is a finite almost simple classical group and each $H_i$ is a maximal subgroup contained in Aschbacher's collection $\mathcal{S}$ of irreducibly embedded subgroups. Our main theorem determines all the non-regular $t$-tuples of this form with $t \geqslant 2$, which extends earlier work by Burness, Guralnick and Saxl in the base size setting. In particular, we deduce that every pair of maximal subgroups in $\mathcal{S}$ is regular if $n \geqslant 15$, where $n$ is the dimension of the natural module for the socle of $G$, and this lower bound is best possible.

math.GR↗

Measurement of solar $pp$ neutrino flux with the new PandaX-4T data

We report a new measurement of the solar proton--proton ($pp$) neutrino flux via neutrino--electron elastic scattering using the PandaX-4T Run 2 data set collected between 2024 and 2026, corresponding to an exposure of 1.9 tonne$\cdot$yr. Before Run 2 data taking, the detector underwent a series of upgrades to improve its response and background conditions. Time variations of radioactive noble-gas impurities are constrained using the physics data themselves, complemented by measurements from the gas-assay system. The analysis introduced improvements in the data processing chain, detector response characterization, and background models. A blind spectral analysis was then performed on the electronic-recoil data across a wide energy range from 20 to 1000 keV. In combination with the Run 0 data published earlier, the fitted $pp$ flux is $(8.5 \pm 3.5)\times 10^{10}$ $\mathrm{cm^{-2}s^{-1}}$, consistent with the prediction of the Standard Solar Model. With a statistical significance of $2.2σ$ above background, this marks the first positive indication of solar $pp$ neutrino--electron scattering below an electronic-recoil energy of 165 keV.

hep-ex↗

Performance of FBK VUV-HD3 and HPK VUV4 SiPMs in the Light-only Liquid Xenon (LoLX) Detector

Silicon Photomultipliers (SiPMs) are a critical technology for the next generation of rare-event search experiments using liquid xenon (LXe). LXe scintillation is emitted primarily in the vacuum-ultraviolet (VUV), requiring photosensors with high VUV detection efficiency under cryogenic operating conditions. Direct characterization of VUV-sensitive SiPMs in LXe is therefore important for informing detector design and comparing device response. This work presents a direct comparison of Fondazione Bruno Kessler (FBK) VUV-HD3 and Hamamatsu (HPK) VUV4 SiPMs operated simultaneously within the Light-only Liquid Xenon (LoLX) detector. Using data collected with gamma sources placed outside the detector, we characterized the relative performance of these photosensors. Our analysis reveals that under these operating conditions, the HPK SiPMs observe 33-38% less light than the FBK devices, a larger difference than predicted by standard PDE models in vacuum measurements. We show that this discrepancy is resolved by an angular and wavelength dependent PDE model incorporating surface shadowing effects into our optical simulation, which then accurately reproduces the experimental data. This finding has significant implications for the selection and implementation of photosensors in future large-scale LXe detectors.

physics.ins-det↗

RefAlign: Representation Alignment for Reference-to-Video Generation

Reference-to-video (R2V) generation is a controllable video synthesis paradigm that constrains the generation process using both text prompts and reference images, enabling applications such as personalized advertising and virtual try-on. In practice, existing R2V methods typically introduce additional high-level semantic or cross-modal features alongside the VAE latent representation of the reference image and jointly feed them into the diffusion Transformer (DiT). These auxiliary representations provide semantic guidance and act as implicit alignment signals, which can partially alleviate pixel-level information leakage in the VAE latent space. However, they may still struggle to address copy--paste artifacts and multi-subject confusion caused by modality mismatch across heterogeneous encoder features. In this paper, we propose RefAlign, a representation alignment framework that explicitly aligns DiT reference-branch features to the semantic space of a visual foundation model (VFM). The core of RefAlign is a reference alignment loss that pulls the reference features and VFM features of the same subject closer to improve identity consistency, while pushing apart the corresponding features of different subjects to enhance semantic discriminability. This simple yet effective strategy is applied only during training, incurring no inference-time overhead, and achieves a better balance between text controllability and reference fidelity. Extensive experiments on the OpenS2V-Eval benchmark demonstrate that RefAlign outperforms current state-of-the-art methods in TotalScore, validating the effectiveness of explicit reference alignment for R2V tasks.

cs.CV↗

Neural Canonical Transformation for the Spectra of Fluxional Molecule CH5+

Protonated methane, CH5+, is a highly fluxional molecule with large spatial motions of the hydrogen atoms. The molecule's anharmonic effects and the delocalized wavefunction of the hydrogen atoms significantly affect the excitation spectrum of the molecule. The neural canonical transformation (NCT) approach, which we previously developed to solve the vibrational spectra of molecules and solids, is a powerful method that effectively treats nuclear quantum effects and anharmonicities. Using NCT with wavefunctions in atomic coordinates rather than normal coordinates, we successfully calculate the ground and excited states of CH5+. We found that the wavefunctions for the ground state, as well as for low- and high-energy excited states, show preferences for the three stationary points on the potential energy surface. This work extends the applicability of the NCT approach for calculating excited states to fluxional molecules without fixed geometry.

physics.chem-ph↗

AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems

Recommendation algorithm iteration is moving from an artisanal, engineer-bound process toward an industrialized research loop, but this transition remains blocked by a structural execution bottleneck: the idea-to-launch cycle still depends on human engineers to generate hypotheses, modify production code, launch A/B experiments, and attribute online results. Innovation therefore scales linearly with headcount rather than compounding with evidence, compute, and accumulated experimental knowledge. We present AgentX, a production-deployed multi-agent system that fundamentally restructures this production function. AgentX operates as a self-evolving development engine: it autonomously generates, implements, evaluates, and learns from recommendation experiments at a scale and pace that no manual workflow can sustain. The system orchestrates four tightly coupled stages in a closed loop. A Brainstorm Agent synthesizes evidence from historical experiments, system architecture, data analysis, and external research into ranked, executable proposals. A Developing Agent translates each proposal into production-ready code through repository-grounded generation and multi-dimensional reliability verification. An Evaluation Agent conducts safe online rollout with guardrail-vetoed A/B judgment, converting both successes and failures into structured knowledge assets. A Harness Evolution layer (SGPO) then distills execution trajectories into semantic-gradient updates that continuously sharpen the agents themselves -- making the system not merely automated, but self-improving.

cs.AI↗

Symmetry-Selective Strain Control of Spin-Momentum Locking and Spin Transport in Two-Dimensional Pentagonal Altermagnets

Altermagnets are compensated magnets featuring momentum-dependent nonrelativistic spin splitting generated by nontrivial operations connecting opposite-spin sublattices. A direct symmetry-based route to control this spin splitting is to modify the real-space operations that define the altermagnetic spin-momentum locking (SML). Here, we develop a strain-resolved symmetry framework for two-dimensional pentagonal altermagnets, classifying whether uniaxial and shear strain tensors preserve, reconstruct, or eliminate the SML. Using the above criterion combined with first-principles screening, we identify 94 stable altermagnetic candidates from 3330 materials. These candidates cover all type-III spin Laue groups of orthorhombic lattices and are classified into three strain-response types: Type-I preserves the SML; Type-II reconstructs the SML through partial symmetry breaking while retaining essential altermagnetic features; and Type-III destroys the altermagnetic SML. Representative materials further demonstrate this classification: ferroelastic $α$-CoS$_2$ exhibits ferroelastically switchable SML and reverses the sign of the off-diagonal spin conductivity; shear-strained \(α\)-CoP\(_2\) undergoes a \(g\)- to \(d\)-wave reconstruction of the SML, activating off-diagonal spin conductivity; and uniaxially strained FeSSe realizes strain-selected spin-valley transport. This work provides theoretical and material guidance for strain-controlled transport in two-dimensional orthorhombic altermagnets.

cond-mat.mtrl-sci↗

Inference of Component Effect on System Performance

In a computer system, multiple components--such as the CPU, memory, and others--work together as a system whose performance can be directly measured. However, the effect of a component under investigation (CUI), e.g., CPU, on system performance cannot be directly measured and can only be inferred. Accurately inferring CUI effect on system performance is a critical issue. Our experiments reveal that the general-purpose rigorous methodologies, like Design of Experiments (DoE), Randomized Controlled Trials (RCTs), and a single-purpose empirical methodology, like SPEC CPU2017, can not address this issue effectively and efficiently. We propose a rigorous methodology to address this issue: First, we identify a self-contained system (SCS) under the context of which we can completely understand how CUI and other essential components affect the system performance, and then we use a structural causal model methodology to represent and infer the causal effect of CUI on the system performance. We utilize this methodology and verify its correctness in the context of CPU design and evaluation. Through theoretical analysis and pioneering controlled experiments, we systematically compare our methodology against three established methodologies: SPEC CPU2017, DoE, and RCTs. The results show that our methodology can achieve its goal effectively and efficiently, whereas others exhibit inherent limitations.

cs.PF↗

Irreversibility Enhances Quantum-Enhanced Markov-Chain Monte Carlo

Detailed balance underlies conventional Markov-chain Monte Carlo (MCMC) algorithms. Yet in classical systems, breaking detailed balance generates irreversible probability currents and can accelerate sampling. Whether irreversibility can similarly enhance quantum MCMC remains an intriguing question. Here we show that irreversibility provides a new route to improving the recent quantum-enhanced MCMC (QEMC), which combines quantum proposals with classical acceptance. By introducing state-dependent proposals that break detailed balance while preserving the target stationary distribution, we develop an irreversible quantum-enhanced Monte Carlo (IQEMC). Guided by Landau-Zener transitions, IQEMC promotes large energy descents from high-energy states while maintaining stable transitions near low-energy states. On spin-glass benchmarks, IQEMC outperforms QEMC without increasing computational complexity and, unlike the annealing baseline, exhibits a spectral gap that increases with system size and annealing speed. These results establish irreversibility as a physically grounded mechanism for enhancing quantum MCMC.

cond-mat.stat-mech↗

Anomalous charge density wave in a two-dimensional superatomic superconductor

The spatial modulation of electron density into a wave-like pattern, known as charge density wave (CDW), represents a fundamental quantum state that often coexists with superconductivity, quantum Hall states, axion insulating phases and etc. Conventional CDWs are mediated by longitudinal acoustic phonons, exhibit picometer-scale lattice distortions ($10^{-12}$--$10^{-11}$ m), and typically vanish approaching the atomic limit. Here, we report a series of anomalous CDW behaviors in the 2D superatomic superconductor Au$_6$Te$_{12}$Se$_8$. Remarkably, its CDW is governed by transverse phonons, accompanied by an extraordinarily high real-space displacement of $\sim 4$ Ångström. Furthermore, we observe an exotic dimensional response persisting up to micrometer-scale thickness, a regime where other materials are already considered as bulk. Through liquid helium-temperature transmission electron microscopy, ultrafast pump-probe spectroscopy and transport measurements, we demonstrate a dramatic enhancement of the CDW transition temperature ($T_{\text{CDW}}$) from $<2$ K in the bulk to 110 K in approaching the ``superatomic limit''. Our findings not only reveal novel facets of both CDW and superatomic materials, but the competition between this anomalous CDW and superconductivity opens avenues for exploring unconventional electron-phonon interactions.

cond-mat.supr-con↗

LaST-HD: Learning Latent Physical Reasoning from Scalable Human Data for Robot Manipulation

Human-hand demonstrations provide a direct and scalable source of physical interaction data for robot learning. While manual retargeting is indispensable for establishing kinematic action correspondence across different morphologies, robust transfer requires going beyond geometry to address the underlying alignment of physical dynamics between human and robot manipulation. To address this, we introduce LaST-HD, a novel human-to-robot action learning paradigm that extends reasoning-before-acting VLA by aligning human-hand and robot demonstrations in a shared latent reasoning space. Rather than mimicking human kinematics, LaST-HD trains an auxiliary action-conditioned world model on unpaired human-hand and robot trajectories to synthesize unified latent targets. After aligning cross-embodiment representations in this shared forward-dynamics space, these targets supervise LaST-HD's latent reasoning process, enabling it to internalize shared physical dynamics and drive efficient human-hand action learning. Moreover, we develop Out-of-Lab (OOL) Glove, a low-cost motion-capture glove tailored to LaST-HD for human-hand data collection. The captured human data provide precise keypoints and serve as universal action supervision across grippers and dexterous hands. Armed with the aligned latent space and high-fidelity human-hand data, we develop a progressive mixed-to-human training recipe comprising mixed human-robot co-training and human-hand online correction post-training. Through mixed co-training, LaST-HD improves generalization to novel objects, scenes, and positions using only human-hand demonstrations. With online correction, LaST-HD further adapts to novel environments and achieves over 90\% accuracy using only 20 minutes of OOL glove data.

cs.RO↗

Vibe Calibration: Autonomous Bring-up of a 112-Qubit Superconducting Quantum Processor by a Skill-Orchestrating Language Agent

Superconducting quantum computing is one of the most mature solid-state platforms for quantum computation, with processors exceeding one hundred qubits. Yet further scaling toward fault-tolerant quantum computing is increasingly constrained by calibration complexity. Conventional scripts are brittle to anomalous signals, and expert judgment is bounded by cognitive bandwidth and serial operation time, failing to keep pace with system scale. Here we report Vibe Calibration, an autonomous calibration system orchestrated by large language model agents, which distills expert tacit knowledge into reusable Skills. Each Skill is organized as a decision tree that packages parameterized measurement commands, quantitative acceptance criteria, and audit records, enabling autonomous execution and self-healing. We capture this knowledge through a three-phase human-in-the-loop distillation process and fine-tune a large language model on validated trajectories. On a 112-qubit processor with frequency-tunable transmons, the system autonomously completes calibration of 108 out of 112 qubits in 4.7 hours, achieving a 4--5$\times$ speedup over manual calibration of the full 112 qubits. A cross-validated comparison with expert manual calibration on a 16-qubit subset shows agreement on 14 out of 16 qubits. More importantly, the model demonstrates transferable calibration workflows across devices. While low-level control scripts require minor interface adaptation for different hardware platforms, the core decision logic and task orchestration generalize to new processors, demonstrating a reusable laboratory interface rather than a memorized script.This work demonstrates, for the first time, fully autonomous calibration of a hundred-qubit superconducting processor through reusable and auditable Skills, removing a critical barrier to scalable quantum hardware operation.

quant-ph↗

Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers

Parameter-efficient fine-tuning (PEFT) has become a practical solution for adapting large pretrained vision transformers (ViTs) to downstream tasks while updating only a small subset of parameters. However, existing adapter-based methods perform adaptation independently for each token, implicitly assuming that token refinements should be learned in isolation. This token-wise formulation overlooks the structured relationships among tokens that naturally arise in visual scenes, potentially leading to redundant updates and spatially inconsistent feature refinement. In this work, we revisit the design of parameter-efficient adapters and propose to perform adaptation in hyperedge space rather than token space. We introduce HyperAdapter, a hypergraph-based adapter architecture that enables structured, group-aware adaptation through soft token routing. HyperAdapter constructs a soft hypergraph over ViT tokens using prototype-based assignments, aggregates token features into latent hyperedge representations, applies lightweight bottleneck adaptation at the hyperedge level, and diffuses the resulting updates back to tokens via the hypergraph incidence structure. This design injects an explicit structural inductive bias into PEFT while preserving the modularity and efficiency of standard adapters. Extensive experiments across diverse visual benchmarks demonstrate that structured hyperedge adaptation consistently outperforms strong PEFT baselines under comparable parameter budgets, with particularly pronounced gains on tasks requiring structured reasoning. Our results suggest that the choice of adaptation space is a critical yet underexplored dimension in parameter-efficient transfer for ViTs.

cs.CV↗

Trip+: Benchmarking Agents in Personalized Interactive Travel Planning

Interactive travel planning has become a popular use case for language models. Agents are deployed to manage evolving preferences and unexpected disruptions over multiple turns. Such settings require models to make complex, profile-conditioned planning decisions. However, existing benchmarks often evaluate feasibility, personalization, or interaction in relatively isolated settings. We therefore introduce Trip+ to measure the ability of agents to plan travel holistically. In Trip+, given traveler profiles and dynamic interactions, agents must generate and revise minute-level itineraries. End-to-end traveler experiences are evaluated via an LLM-based simulator, enabling the assessment of subjective metrics like fatigue. Our scenarios range from simple request resolutions to complex environment-driven replanning. We evaluate 18 LMs and find a consistent gap in experiential quality. Models favor technically feasible but exhausting itineraries that diverge sharply from profiled traveler preferences.

cs.AI↗

Terahertz frequency upconversion by coherently driving charge dynamics in the InSb/CdTe heterostructure

We investigate terahertz (THz) harmonic generation in the InSb/CdTe heterostructure, demonstrating, for the first time, efficient in-plane magnetic field-induced second-harmonic generation (SHG). We also achieve significant third-harmonic generation (THG), rivalling Dirac materials such as graphene and Cd3As2. Our theoretical analysis identifies the primary SHG mechanism as the orbital-Zeeman correction to Drude conductivity, while the dominant THG contribution also shows Drude-like behavior. The results provide a general route to efficient THz harmonic generation in high mobility materials.

cond-mat.mtrl-sci↗

EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments

Large language model (LLM) agents have achieved strong performance on a wide range of benchmarks, yet most evaluations assume static environments. In contrast, real-world deployment is inherently dynamic, requiring agents to continually align their knowledge, skills, and behavior with changing environments and updated task conditions. To address this gap, we introduce EvoArena, a benchmark suite that models environment changes as sequences of progressive updates across terminal, software, and social domains. We further propose EvoMem, a patch-based memory paradigm that records memory evolution as structured update histories, enabling agents to reason about environmental evolution through changes in their memory. Experiments show that current agents struggle on EvoArena, achieving an average accuracy of 39.6% across evolving terminal, software, and social-preference domains. EvoMem consistently improves performance, yielding an average gain of 1.5% on EvoArena and also improving standard benchmarks such as GAIA and LoCoMo by 6.1% and 4.8%. Beyond individual tasks, EvoMem further improves chain-level accuracy by 3.7% on EvoArena, where success requires completing a consecutive sequence of related evolutionary subtasks. Mechanistic analysis shows that EvoMem improves evidence capture in the memory, indicating better preservation of complete evolving environment states. Our results highlight the importance of modeling evolution in both evaluation and memory for reliable agent deployment.

cs.CL↗

ARC-Fi: Exploiting Antenna Spatial Diversity for Label-Efficient Domain Generalization in Wi-Fi Sensing

Wi-Fi sensing systems are severely hindered by domain shifts when deployed in unseen real-world environments. While existing methods attempt to tackle this through Unsupervised Domain Adaptation (UDA) or Domain Generalization (DG), they critically rely on either inaccessible target data or prohibitively expensive, massive labeled source datasets. In practice, collecting abundant unlabeled Channel State Information (CSI) is feasible, whereas manual labeling is severely constrained. This realistic dilemma necessitates Semi-Supervised Domain Generalization (SSDG). To this end, we propose ARC-Fi, the first dedicated SSDG framework for Wi-Fi sensing. Directly applying conventional contrastive learning to CSI data inevitably triggers paradigm-specific "shortcut learning," causing models to memorize environmental backgrounds rather than gesture dynamics. To overcome this, ARC-Fi introduces a physics-informed data augmentation strategy: the Antenna Response Consistency (ARC) module. ARC exploits the intrinsic spatial diversity of multi-antenna systems, treating signals from co-located antennas as naturally semantics-preserving augmented views to explicitly block environmental shortcuts. Furthermore, we introduce a unified Semi-Supervised Contrastive Objective that leverages scarce labels and reliable pseudo-labels to align cross-domain features, effectively preventing the blind repulsion of same-class instances. Extensive experiments on the Widar and CSIDA datasets demonstrate that ARC-Fi establishes a new state-of-the-art, significantly outperforming existing UDA, DG, and SSDG methods. Ultimately, this work provides a physics-grounded, label-efficient solution, advancing the scalable deployment of robust real-world Wi-Fi sensing systems. Code is available at: https://github.com/KaoruMiyazono/UniCrossFi.

cs.LG↗

Collective relaxation eigenmodes and anisotropic magnon thermal transport in $α$-MnTe

The intrinsic magnon thermal transport in the altermagnet $α$-MnTe is studied by solving the three-dimensional linearized Boltzmann transport equation with the four-magnon collision matrix. Diagonalizing the collision matrix gives direct access to the relaxation eigenmodes beyond the relaxation-time approximation. We show that Umklapp scattering lifts the momentum-related zero modes of the Normal-only collision operator and substantially modifies the low-lying relaxation spectrum. Using the same collision matrix, we compute the magnon thermal conductivity tensor. The full linearized Boltzmann transport equation result exceeds the relaxation-time approximation by more than an order of magnitude at low temperature and reveals a strong transport anisotropy, with the out-of-plane thermal conductivity remaining larger than the in-plane component over the studied temperature range. A mode-resolved analysis shows that the dominant heat-carrying modes retain momentum-like character inherited from the Normal-only zero modes, and that the larger out-of-plane conductivity mainly originates from the stronger out-of-plane group-velocity contribution, rather than from a large difference in relaxation lifetimes.

cond-mat.mtrl-sci↗