arXiv subjects

Lei Huang

Publications and source records attributed to Lei Huang.

At least 55 records · Page 3Linked to original sources

arXiv2026

Overview of Ground-based Wide-Angle Cameras array

As one of the key ground-based facilities of the Chinese-French SVOM mission, the main scientific objectives of the Ground-based Wide Angle Camera array (GWAC) are to detect prompt optical emission of gamma-ray bursts or other short duration astronomical transients on a second-scale temporal resolution. GWAC is located at Xinglong observatory, China, and consists of 10 mounts and 40 cameras, providing a joint field of view of about 3600 square degrees.The detection ability is 16 magnitude in 10 seconds of exposure time in the visual band under the condition of the new moon phase. Here, we give an overview of GWAC and introduce the science motivation of the project, as well as the performance of the hardware and the software. The observation strategies and the data processing are briefly presented. The early sciences in the last 5 years since the first light are summarized.

astro-ph.IM↗

arXiv2026

Alert Chain and Observation Planning for Ground Wide Angle Camera Network

The Ground Wide Angle Camera Network (GWAC-N) is a robotic telescope network. It consists of ten wide-field core telescopes (GWAC-A) and two 60cm narrow-field rapid follow-up telescopes (GWAC-F60A/B). The primary scientific goal of GWAC-N is to detect optical counterparts of gamma-ray bursts (GRBs) discovered by the SVOM satellite. This is achieved through synchronized monitoring with the GWAC-A array. Upon receiving a GRB trigger alert, the network conducts rapid, multi-band follow-up observations using the GWAC-F60A/B telescopes. The two-stage observation process involves many telescopes, making manual control impractical. Automated operations are therefore essential. They are realized through an integrated alert processing chain and an automated observation scheduling and dispatching mechanism. To enable this, we employ the SVOM Follow-up Observation Coordinating Service (FOCS) and GWAC-N's Automatic Observation Management (AOM) system. This paper presents the integrated alert processing workflow. It also describes the formulation of observation strategies, and the scheduling and execution of observations enabled by FOCS and AOM.

astro-ph.IM↗

arXiv2026

SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution

Social intelligence, the ability to navigate complex interpersonal interactions, presents a fundamental challenge for language agents. Training such agents via reinforcement learning requires solving the credit assignment problem: determining how individual utterances contribute to multi-turn dialogue outcomes. Existing approaches directly employ language models to distribute episode-level rewards, yielding attributions that are retrospective and lack theoretical grounding. We propose SAVOIR (ShApley Value fOr SocIal RL), a novel principled framework grounded in cooperative game theory. Our approach combines two complementary principles: expected utility shifts evaluation from retrospective attribution to prospective valuation, capturing an utterance's strategic potential for enabling favorable future trajectories; Shapley values ensure fair credit distribution with axiomatic guarantees of efficiency, symmetry, and marginality. Experiments on the SOTOPIA benchmark demonstrate that SAVOIR achieves new state-of-the-art performance across all evaluation settings, with our 7B model matching or exceeding proprietary models including GPT-4o and Claude-3.5-Sonnet. Notably, even large reasoning models consistently underperform, suggesting social intelligence requires qualitatively different capabilities than analytical reasoning.

cs.AI↗

arXiv2026

Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play

Games offer a compelling paradigm for developing general reasoning capabilities in language models, as they naturally demand strategic planning, probabilistic inference, and adaptive decision-making. However, existing self-play approaches rely solely on terminal game outcomes, providing no mechanism to distinguish transferable reasoning patterns from game-specific heuristics. We present STRATAGEM, which addresses two fundamental barriers to reasoning transfer: domain specificity, where learned patterns remain anchored in game semantics, and contextual stasis, where static game contexts fail to cultivate progressive reasoning. STRATAGEM selectively reinforces trajectories exhibiting abstract, domain-agnostic reasoning through a Reasoning Transferability Coefficient, while incentivizing adaptive reasoning development via a Reasoning Evolution Reward. Experiments across mathematical reasoning, general reasoning, and code generation benchmarks demonstrate substantial improvements, with particularly strong gains on competition-level mathematics where multi-step reasoning is critical. Ablation studies and human evaluation confirm that both components contribute to transferable reasoning.

cs.AI↗

arXiv2026

x1: Learning to Think Adaptively Across Languages and Cultures

Languages encode distinct abstractions and inductive priors, yet most large language models (LLMs) overlook this diversity by reasoning in a single dominant language. In this work, we introduce x1, a family of reasoning models that can adaptively reason in an advantageous language on a per-instance basis. To isolate the effect of reasoning-language choice, x1 is constructed without expanding the model's knowledge boundaries and is trained by contrasting linguistically distinct reasoning trajectories for the same input. Our extensive experiments demonstrate the benefits of adaptive multilingual reasoning across multilingual mathematical reasoning and culturally grounded tasks. Moreover, our results challenge a simplistic view of scaling laws: while scaling reduces cross-lingual disparities in procedural domains such as math reasoning, it does not eliminate the advantages of culture-associated languages in culturally grounded tasks, as we empirically show that such reasoning enables more efficient and accurate cultural knowledge recall. Overall, our findings establish language choice as a functional component of reasoning, with implications for building more generalist and globally competent reasoning models.

cs.CL↗

arXiv2026

Mutual Coupling-Aware Beamforming in Multi-User Continuous Aperture Array Systems

A mutual coupling-aware beamforming design for continuous aperture array (CAPA)-aided multi-user systems is investigated. First, a transmit coupling kernel is characterized to explicitly capture the mutual coupling effects inherent in CAPAs, based on which a mutual coupling-aware sum-rate maximization functional optimization problem is formulated. To address this problem, a kernel approximation (KA)-based weighted minimum mean-squared error (WMMSE) algorithm is developed. The optimal beamforming condition is derived within the WMMSE framework using the calculus of variations, while KA is employed to obtain a closed-form beamforming solution via wavenumber-domain Fourier transforms and Gauss-Legendre quadrature. Furthermore, the proposed framework is extended to CAPA-to-CAPA multiple-input multiple-output (MIMO) systems. Finally, numerical results demonstrate that: 1) the proposed algorithm achieves improved performance compared to benchmark schemes; 2) the modeled coupling effects are physically rational, where the performance of spatially discrete arrays converges to that of CAPAs; and 3) CAPA-to-CAPA MIMO systems can achieve higher degrees of freedom when the transceivers are placed in close proximity.

eess.SP↗

arXiv2026

Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges

Current large language models (LLMs) often exhibit imbalances in multilingual capabilities and cultural adaptability, largely attributed to their English-centric pre-training data. In this paper, we introduce and investigate cross-lingual latent transplantation (XTransplant), a probing framework which aims to further exploit the model's internalized multilingual knowledge during inference and examine its effects on the multilingual capability and cultural adaptability of LLMs. XTransplant framework enables models to harness the complementary strengths of both English and non-English resources by transplanting latent activations across languages. Through extensive analysis, we empirically demonstrate that XTransplant, a form of cross-lingual interaction, has mutually beneficial effects on the multilingual capability and cultural adaptability of LLMs, particularly for low-resource languages and cultures. We further reveal that attention modules play a pivotal role in supporting multilingual understanding, while feed-forward modules are more adept at capturing culture-specific knowledge. In addition, we conduct in-depth analysis of XTransplant's stability, effectiveness, and generalizability. By probing the upper bound performance of XTransplant, we expose the considerable underutilization of current LLMs' multilingual potential-a challenge that remains open. We hope our analysis offers a new lens for advancing cross-lingual interactions and better leveraging models' internalized multilingual knowledge.

cs.CL↗

arXiv2026

SFFNet: Synergistic Feature Fusion Network With Dual-Domain Edge Enhancement for UAV Image Object Detection

Object detection in unmanned aerial vehicle (UAV) images remains a highly challenging task, primarily caused by the complexity of background noise and the imbalance of target scales. Traditional methods easily struggle to effectively separate objects from intricate backgrounds and fail to fully leverage the rich multi-scale information contained within images. To address these issues, we have developed a synergistic feature fusion network (SFFNet) with dual-domain edge enhancement specifically tailored for object detection in UAV images. Firstly, the multi-scale dynamic dual-domain coupling (MDDC) module is designed. This component introduces a dual-driven edge extraction architecture that operates in both the frequency and spatial domains, enabling effective decoupling of multi-scale object edges from background noise. Secondly, to further enhance the representation capability of the model's neck in terms of both geometric and semantic information, a synergistic feature pyramid network (SFPN) is proposed. SFPN leverages linear deformable convolutions to adaptively capture irregular object shapes and establishes long-range contextual associations around targets through the designed wide-area perception module (WPM). Moreover, to adapt to the various applications or resource-constrained scenarios, six detectors of different scales (N/S/M/B/L/X) are designed. Experiments on two challenging aerial datasets (VisDrone and UAVDT) demonstrate the outstanding performance of SFFNet-X, achieving 36.8 AP and 20.6 AP, respectively. The lightweight models (N/S) also maintain a balance between detection accuracy and parameter efficiency. The code will be available at https://github.com/CQNU-ZhangLab/SFFNet.

cs.CV↗

arXiv2026

Ring Asymmetry and Spin in M87*

Event Horizon Telescope (EHT) images of the supermassive black hole M87* depict an asymmetric ring of emission. General relativistic magnetohydrodynamic (GRMHD) models of M87* and its accretion disk predict that the amplitude and location of the ring's peak brightness asymmetry should fluctuate due to turbulence in the source plasma. We compare the observed distribution of brightness asymmetry amplitudes to the simulated distribution in GRMHD models, across varying black hole spin $a_{*}$. We show that, for strongly magnetized (MAD) models, three epochs of EHT data marginally disfavor $|a_{*}| \lesssim 0.2$. This is consistent with the Blandford-Znajek model for M87's jet, which predicts that M87* should have nonzero spin. We show quantitatively how future observations could improve spin constraints, and discuss how improved spin constraints could distinguish between differing jet-launching mechanisms and black hole growth scenarios.

astro-ph.HE↗

arXiv2026

GenoBERT: A Language Model for Accurate Genotype Imputation

Genotype imputation enables dense variant coverage for genome-wide association and risk-prediction studies, yet conventional reference-panel methods remain limited by ancestry bias and reduced rare-variant accuracy. We present Genotype Bidirectional Encoder Representations from Transformers (GenoBERT), a transformer-based, reference-free framework that tokenizes phased genotypes and uses a self-attention mechanism to capture both short- and long-range linkage disequilibrium (LD) dependencies. Benchmarking on two independent datasets including the Louisiana Osteoporosis Study (LOS) and the 1000 Genomes Project (1KGP) across ancestry groups and multiple genotype missingness levels (5-50%) shows that GenoBERT achieves the highest overall accuracy compared to four baseline methods (Beagle5.4, SCDA, BiU-Net, and STICI). At practical sparsity levels (up to 25% missing), GenoBERT attains high overall imputation accuracy ($r^2 approx 0.98$) across datasets, and maintains robust performance ($r^2 > 0.90$) even at 50% missingness. Experimental results across different ancestries confirm consistent gains across datasets, with resilience to small sample sizes and weak LD. A 128-SNP (single-nucleotide polymorphism) context window (approximately 100 Kb) is validated through LD-decay analyses as sufficient to capture local correlation structures. By eliminating reference-panel dependence while preserving high accuracy, GenoBERT provides a scalable and robust solution for genotype imputation and a foundation for downstream genomic modeling.

q-bio.GN↗

arXiv2026

Can Large Language Models Simulate Human Cognition Beyond Behavioral Imitation?

An essential problem in artificial intelligence is whether LLMs can simulate human cognition or merely imitate surface-level behaviors, while existing datasets suffer from either synthetic reasoning traces or population-level aggregation, failing to capture authentic individual cognitive patterns. We introduce a benchmark grounded in the longitudinal research trajectories of 217 researchers across diverse domains of artificial intelligence, where each author's scientific publications serve as an externalized representation of their cognitive processes. To distinguish whether LLMs transfer cognitive patterns or merely imitate behaviors, our benchmark deliberately employs a cross-domain, temporal-shift generalization setting. A multidimensional cognitive alignment metric is further proposed to assess individual-level cognitive consistency. Through systematic evaluation of state-of-the-art LLMs and various enhancement techniques, we provide a first-stage empirical study on the questions: (1) How well do current LLMs simulate human cognition? and (2) How far can existing techniques enhance these capabilities?

cs.CL↗

arXiv2026

Controlling entanglement by phase engineering in giant-atom waveguide

We investigate the entanglement dynamics of two giant atoms coupled to a common waveguide. By introducing additional phase modulation at each coupling point, every photon propagation path is jointly controlled by two distinct coupling phases, enabling precise and flexible manipulation of the entanglement evolution. This phase engineering induces destructive interference among different paths, leading to entanglement dynamics in nested giant atoms that become equivalent to those of small atoms, as well as dynamical equivalence between separated and braided configurations. Furthermore, the proposed scheme significantly enhances the robustness of entanglement against variations in the phase shift, offering a practical route to generate stable entanglement and enabling quantum devices with programmable propagation and controllable memory effects.

quant-ph↗

arXiv2026

SAM Molecular Stacking with Heterogeneous Orientationfor High-Performance Perovskite Photovoltaics

This study demonstrates that thermal-evaporated SAM (eSAM) films, particularly in a thick configuration, spontaneously adopt a heterogeneous molecular orientation, forming a vertical-to-horizontal gradient in molecular packing. This unique architecture establishes a graded energy barrier, which is shown to facilitate more efficient hole transport compared with the single energy barrier presented by conventional thin SAMs. In conclusion, while solution-processed SAMs present formidable scalability challenges, the thermal evaporation of SAMs offers a viable pathway toward industrial-scale fabrication. The strategy of employing thick eSAM films with gradient molecular packing not only circumvents the uniformity issues of solution methods but also introduces a superior structure for charge transport, positioning it as a promising enabler for the commercialization of high-efficiency perovskite photovoltaics. The inability to achieve uniform hole transport with solution-processed self-assembled monolayers (SAMs) constitutes a fundamental bottleneck for scaling perovskite photovoltaics. Herein, we demonstrate that thermal-evaporated SAMs (eSAMs) overcome this limitation by enabling precise thickness control. Crucially, a thickened eSAM spontaneously forms a vertical-to-horizontal gradient in molecular orientation, which creates a descending energy barrier that directionally facilitates hole transport. This tailored interface also ensures excellent surface coverage and directs the growth of high-quality perovskite films. Consequently, the resultant photovoltaic devices set new benchmarks, delivering impressive power conversion efficiencies (PCEs) of 21.46% (small-area, 0.108 cm2) and 19.38% (large-area module, 15.52 cm2) for fully vacuum-evaporated devices, while also setting an impressive PCE of 23.67% for eSAM-based devices with solution-processed perovskites.

cond-mat.mtrl-sci↗

arXiv2026

Bootstrapping Exploration with Group-Level Natural Language Feedback in Reinforcement Learning

Large language models (LLMs) typically receive diverse natural language (NL) feedback through interaction with the environment. However, current reinforcement learning (RL) algorithms rely solely on scalar rewards, leaving the rich information in NL feedback underutilized and leading to inefficient exploration. In this work, we propose GOLF, an RL framework that explicitly exploits group-level language feedback to guide targeted exploration through actionable refinements. GOLF aggregates two complementary feedback sources: (i) external critiques that pinpoint errors or propose targeted fixes, and (ii) intra-group attempts that supply alternative partial ideas and diverse failure patterns. These group-level feedbacks are aggregated to produce high-quality refinements, which are adaptively injected into training as off-policy scaffolds to provide targeted guidance in sparse-reward regions. Meanwhile, GOLF jointly optimizes generation and refinement within a unified RL loop, creating a virtuous cycle that continuously improves both capabilities. Experiments on both verifiable and non-verifiable benchmarks show that GOLF achieves superior performance and exploration efficiency, achieving 2.2$\times$ improvements in sample efficiency compared to RL methods trained solely on scalar rewards. Code is available at https://github.com/LuckyyySTA/GOLF.

cs.CL↗

arXiv2026

QiMeng-CRUX: Narrowing the Gap Between Natural Language and Verilog via Core Refined Understanding eXpression for Circuit Design

Large language models (LLMs) have shown promising capabilities in hardware description language (HDL) generation. However, existing approaches often rely on free-form natural language descriptions that are often ambiguous, redundant, and unstructured, which poses significant challenges for downstream Verilog code generation. We treat hardware code generation as a complex transformation from an open-ended natural language space to a domain-specific, highly constrained target space. To bridge this gap, we introduce Core Refined Understanding eXpression (CRUX), a structured intermediate space that captures the essential semantics of user intent while organizing the expression for precise Verilog code generation. We further design a two-stage training framework, comprising Joint Expression Modeling and Dual-Space Optimization, to enhance the quality of both CRUX and Verilog code. Experiments across multiple Verilog generation benchmarks demonstrate that our model, CRUX-V, achieves state-of-the-art performance among general models, particularly under challenging design tasks. Furthermore, the CRUX space proves transferable and beneficial when used as input prompts for other code models, highlighting its effectiveness in narrowing the gap between free-form natural language descriptions and precise Verilog generation.

cs.LG↗

arXiv2026

Rotation of a Solar Jet Driven by Plasma Flow along Helical Magnetic Fields in an Active Region

Solar jets, collimated plasma ejections driven by magnetic reconnection, play a vital role in energy transport and coronal heating. While rotational motions in jets are often attributed to magnetic field untwisting, alternative explanatory mechanisms remain possible. This study investigates a rotating jet in an active region observed on 2023 August 1 using multi-wavelength observations from Atmospheric Imaging Assembly (AIA), Chinese Ha Solar Explorer (CHASE), and Interface Region Imaging Spectrograph (IRIS), combined with a self-consistent time-dependent magnetofrictional (TMF) model and magnetohydrodynamic (MHD) simulation. Spectral diagnostics reveal coexisting red and blue shifts along the edges and central axis of the jet, indicating helical plasma motion within a twisted magnetic structure. Numerical simulations demonstrate that the jet's rotation arises from plasma propagating along helical open field lines, formed via reconnection between a pre-existing flux rope and overlying magnetic fields. Contrary to classical untwisting models, both linear and rotational velocities decrease with altitude during the jet propagation. These results highlight that the observed rotation results from plasma spiral motion along twisted fields rather than untwisting dynamics of the magnetic field itself, providing new insights into solar jet energetics and their connection to broader solar phenomena.

astro-ph.SR↗

arXiv2026

Hybrid Black Hole and Disk-Driven Jets: Steady Axisymmetric Ideal MHD Modeling

Improved observational precision in relativistic jets has underscored the need for tractable theoretical models. In this study, we construct a semi-analytical hybrid jet model that incorporates both black hole-driven and disk-driven components within the framework of steady, axisymmetric, ideal MHD. We derive a condition that determines the launching sites of cold outflows, introducing a new constraint on the magnetic field configuration threading the accretion disk. Using the Bernoulli equation and critical point analysis, we derive flow solutions along various magnetic field lines. Our hybrid jet model shows that discontinuities in field-line angular velocity lead to clear velocity shear and density jumps at the interface between the two jet components. These features are accompanied by localized enhancements in velocity and density, potentially explaining the observed limb-brightening.

astro-ph.HE↗

arXiv2026

Counting and Entropy Bounds for Structure-Avoiding Spatially-Coupled LDPC Constructions

Designing large coupling memory quasi-cyclic spatially-coupled LDPC (QC-SC-LDPC) codes with low error floors requires eliminating specific harmful substructures (e.g., short cycles) induced by edge spreading and lifting. Building on our work~\cite{r15} that introduced a Clique Lovász Local Lemma (CLLL)-based design principle and a Moser--Tardos (MT)-type constructive approach, this work quantifies the size and structure of the feasible design space. Using the quantitative CLLL, we derive explicit lower bounds on the number of feasible edge-spreading and lifting assignments satisfying a given family of structure-avoidance constraints, and further obtain bounds on the number of non-equivalent solutions under row/column permutations. Moreover, via Rényi entropy bounds for the MT distribution, we provide a computable lower bound on the number of distinct solutions that the MT algorithm can output, giving a concrete diversity guarantee for randomized constructions. Specializations for eliminating 4-cycles yield closed-form bounds as functions of system parameters, offering a principled way to select the memory and lifting degree and to estimate the remaining search space.

cs.IT↗