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Shuai Zhang

Publications and source records attributed to Shuai Zhang.

At least 55 records · Page 3Linked to original sources
arXiv2026

Layer-Hybridized Wigner Crystals in MoSe2/WS2 Moiré Superlattice

Transition metal dichalcogenide moiré heterobilayers with type-II band alignment provide a versatile platform for layer-polarized generalized Wigner crystals, in which strong Coulomb interactions drive charge ordering at fractional lattice fillings. With a finite interlayer band offset, an out-of-plane electric field can tune layer-resolved moiré bands through resonance and enable controllable interlayer hybridization. Although hybridized Mott insulators have been previously demonstrated, whether fractional charge-ordered states can survive such hybridization remains elusive. Here we drive an H-stacked MoSe2/WS2 moiré heterobilayer through a type-I-to-type-II band-alignment transition and realize layer-hybridized Mott insulator and generalized Wigner crystals. For fillings below one electron per moiré cell, tunneling delocalizes electrons and modifies Wigner crystallization. However, above one electron per cell, Coulomb repulsion overcomes tunneling and favors layer-separated occupation, stabilizing stronger charge-ordered states. These results establish electrically tunable hybridized moiré heterobilayers as a powerful platform for engineering correlated charge order and exploring fractional Chern phases and emergent magnetism.

cond-mat.mes-hall↗
arXiv2026

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation

Domain-specific finetuning is essential for dense retrievers, yet not all data pairs contribute equally to the learning process. We introduce OPERA, a data pruning framework that exploits this heterogeneity to improve both the effectiveness and efficiency of retrieval model adaptation. We first investigate static pruning (SP), which retains only high-similarity query-document pairs, revealing an intrinsic quality-coverage tradeoff: ranking (NDCG) improves while retrieval (Recall) can degrade due to reduced query diversity. To resolve this tradeoff, we propose a two-stage dynamic pruning (DP) strategy that adaptively modulates sampling probabilities at both query and document levels throughout training, prioritizing high-quality examples while maintaining access to the full training set. Evaluations across eight datasets spanning six domains demonstrate the effectiveness of both approaches: SP improves ranking over standard finetuning (NDCG@10 +0.2 points), while DP achieves the strongest performance on both ranking (NDCG@10 +1.0 points) and retrieval (Recall@20 +0.4 points), with an average rank of 1.38 across all methods. These findings scale to Qwen3-Embedding, an LLM-based dense retriever, confirming architecture-agnostic benefits. Notably, DP reaches comparable performance in less than 50\% of the training time required by standard finetuning.

cs.IR↗
arXiv2026

Post-Newtonian Roche-Lobe-Overflow Prescription for Compact Binary Mass Transfer and the Corresponding Gravitational Waveforms

Mass transfer in binary systems is central to many astrophysical phenomena, including the evolution of compact interacting binaries. Starting from the first post-Newtonian hydrodynamic equations in the corotating frame, we derive the first post-Newtonian Roche potential and construct the corresponding post-Newtonian form of the Roche lobe overflow mass transfer prescription. We then include the time dependence of the component masses in the binary dynamics and compute the associated corrections to the equations of motion, gravitational-wave energy and angular-momentum fluxes, and far-zone polarization waveforms. Finally, we apply the model to representative ultracompact binary systems. We find that mass transfer can play an important role in the dynamical evolution of compact binaries. For gravitational-wave observations, its main effect appears as a secular phase drift accumulated over long observation times.

gr-qc↗
arXiv2026

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge

The IEEE Low-Power Computer Vision Challenge (LPCVC) aims to promote the development of efficient vision models for edge devices, balancing accuracy with constraints such as latency, memory capacity, and energy use. The 2025 challenge featured three tracks: (1) Image classification under various lighting conditions and styles, (2) Open-Vocabulary Segmentation with Text Prompt, and (3) Monocular Depth Estimation. This paper presents the design of LPCVC 2025, including its competition structure and evaluation framework, which integrates the Qualcomm AI Hub for consistent and reproducible benchmarking. The paper also introduces the top-performing solutions from each track and outlines key trends and observations. The paper concludes with suggestions for future computer vision competitions.

cs.CV↗
arXiv2026

Identifying and Determining Atmospheric Parameters of BHB Stars Based on LAMOST DR11

Large catalogs of blue horizontal-branch (BHB) stars are essential for studying substructures and kinematics of the Galactic halo. And accurate determination of atmospheric parameters for BHB stars provides insight into stellar evolution. In this work, we perform a systematic search for BHB stars based on LAMOST DR11, and identify $13\,988$ BHB spectra, corresponding to $10\,236$ unique BHB stars. We estimate an identification rate of $\sim80\%-90\%$, and a contamination rate of $\lesssim10\%$ for our sample. Atmospheric parameters for these BHB stars are estimated via the data-driven method named the Stellar LAbel Machine (SLAM). We demonstrate the necessity of including color indices in the spectral labeling to effectively break the degeneracy between effective temperature and surface gravity. We note a bump in the distribution of [Fe/H], and most of these metal-rich BHB stars belong to the disk population. We also provide a list of 4282 blue straggler (BS) stars with determined atmospheric parameters.

astro-ph.SR↗
arXiv2026

Topological-Charge-Enabled Photonic Doping in ENZ Media

Conventional photonic doping schemes predominantly employ circular or rectangular dielectric dopants with zero topological charge, where the effective permeability can only be tuned through material selection and geometric scaling, resulting in limited design flexibility. In this work, topological structures are introduced into dielectric dopants by embedding internal holes to generate nonzero topological charge. Based on this concept, a theoretical model is established to describe the effective permeability of photonic doping systems with nonzero topological charge, and the underlying mechanisms governing topological-charge-dependent transmission are systematically elucidated. The results demonstrate that engineering nonzero topological charge through the number, shape, size and position of internal holes within dielectric dopants enables flexible manipulation of the internal magnetic field distributions, thereby providing precisely control over the effective permeability, as well as the resonance frequency and spectral linewidth of the transmission spectrum. The proposed multi-dimensional photonic doping strategy, integrating topological-charge engineering with geometric design, substantially enriches the available degrees of freedom for dispersion engineering and provides a versatile platform for advanced functional photonic devices.

physics.optics↗
arXiv2026

VideoSEMA: a scalable and efficient Mamba-like attention for video understanding

We present for video understanding (classification) a split space-time attention model, VideoSEMA, consisting of a scalable and efficient Mamba-like attention (SEMA) block in space and a softmax temporal attention in time. In each frame, SEMA attention applies a local window attention in parallel with a global averaging in a Mamba macro-architecture, which is called Mamba-like. Under certain rank conditions, we prove that the computationally cheaper split space-time attention is equivalent to full space-time attention. On benchmark K400 data sets, VideoSEMA out-performs heavier vision transformer and Mamba models. On benchmark SSv2 data, VideoSEMA leads in top-1 accuracy among models of similar parameter sizes. As image resolution scales up from standard $224^2$ to $1024^2$ on K400 and without fine-tuning, VideoSEMA degrades much more gracefully than VideoMamba in accuracy. It is promising to extend VideoSEMA to longer videos with a dilated/sparse temporal attention.

cs.CV↗
arXiv2026

SEMA: a Scalable and Efficient Mamba like Attention via Token Localization and Averaging

Attention is the critical component of a transformer. Yet the quadratic computational complexity of vanilla full attention in the input size and the inability of its linear attention variant to focus have been challenges for computer vision tasks. We provide a mathematical definition of generalized attention and formulate both vanilla softmax attention and linear attention within the general framework. We prove that generalized attention disperses, that is, as the number of keys tends to infinity, the query assigns equal weights to all keys. Motivated by the dispersion property and recent development of Mamba form of attention, we design Scalable and Efficient Mamba like Attention (SEMA) which utilizes token localization to avoid dispersion and maintain focusing, complemented by theoretically consistent arithmetic averaging to capture global aspect of attention. We support our approach on Imagenet-1k where classification results show that SEMA is a scalable and effective alternative beyond linear attention, outperforming recent vision Mamba models on increasingly larger scales of images at similar model parameter sizes.

cs.CV↗
arXiv2026

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

Large language models are increasingly trained as interactive agents for long-horizon tasks involving multi-turn interaction, tool use, and environment feedback. Outcome-based reinforcement learning (RL) provides a practical optimization paradigm, but its sparse trajectory-level rewards offer limited guidance on intermediate decisions, leaving a supervision gap between episode-level outcomes and token-level policy learning. We propose SEED (SElf-Evolving On-Policy Distillation), a self-evolving framework that converts completed on-policy trajectories into training-time hindsight skills and distills their behavioral effect back into the policy model. SEED first fine-tunes the policy to analyze completed trajectories and generate natural-language skills that capture reusable workflows, decisive observations, or failure-avoidance rules. During RL, the current policy both collects trajectories and serves as the analyzer that extracts hindsight skills from them. Policy updates therefore improve subsequent decision making and skill analysis together, allowing hindsight supervision to evolve with the policy. SEED then re-scores the sampled actions under ordinary and skill-augmented contexts, converting the skill-induced probability shift into a dense token-level on-policy distillation signal. This signal is jointly optimized with outcome-based RL, keeping the auxiliary supervision aligned with the current trajectory distribution. Extensive experiments on text-based and vision-based agentic tasks show that SEED consistently improves performance and sample efficiency, exhibiting robust generalization to unseen scenarios. Our code is available at https://github.com/jinyangwu/SEED.

cs.CL↗
arXiv2026

The Sample Pre-selection and Characterization Station at the SECUF: Instrumentation, Capabilities, and Representative Scientific Achievements

The Synergetic Extreme Condition User Facility (SECUF) is a comprehensive, state-of-the-art user facility designed to provide integrated extreme physical conditions-including ultrahigh pressure, ultralow temperature, strong magnetic fields, and ultrafast optical fields-for frontier research in condensed matter physics and materials science. Within SECUF, the F2 Sample Pre-selection and Characterization Station plays a pivotal supporting role. Its mission is to provide comprehensive sample synthesis, processing, pre-screening, and characterization services to prepare high-quality specimens for subsequent experiments under extreme conditions. This paper details the specifications and performance of ten core instrument systems within these units. Furthermore, we highlight several breakthrough scientific achievements enabled by the F2 Station, encompassing the discovery of novel quantum spin supersolid states, pressure-induced high-temperature superconductivity in nickelates, giant anomalous Hall angles, and molecular water in lunar soil. We also outline ongoing technical developments that expand the station's capabilities, such as integrated high-pressure cells and self-built ancillary measurement systems.

cond-mat.str-el↗
arXiv2026

Expressibility and trainability of a two-dimensional pairwise quantum-circuit ansatz

Parameterized quantum circuits~(PQCs) constitute a central building block of variational quantum algorithms~(VQAs) and quantum machine learning~(QML) methods. Existing ansatz designs often adopt hardware-agnostic or simplified 1D chain/ring entanglement patterns. However, as quantum hardware continues to develop, native 2D connectivity patterns, such as planar superconducting-qubit architectures, are becoming increasingly important. Inspired by this hardware structure, we construct a native 2D pairwise ansatz and compare its expressibility and trainability with representative 1D ansatze at identical layer depths, despite their different circuit depths. For the fixed 16-qubit system, the 2D ansatz has the smallest KL divergence at $L=1$ and $2$, and its second-order frame potential approaches the theoretical lower bound more rapidly at shallow layer counts than the frame potentials of the three 1D ansatze. We also evaluate the gradient variance of the Pauli-$Z$-string expectation value $\langle Z_0\otimes\cdots\otimes Z_{15}\rangle$ with respect to the first $R_y$ angle. For this Pauli-$Z$ string and fixed parameter, the gradient variance is smaller for the 2D circuit at $L=1$--$4$. The differences narrow at $L=5$, and the four ansatze yield statistically compatible variances at $L=6$.

quant-ph↗
arXiv2026

Boosting Infrared Small Target Detection via Logit-Domain Contrast and Adaptive Shape Refinement

Infrared small target detection (IRSTD) remains challenging due to tiny target size, low signal-to-noise ratio, severe foreground-background imbalance, and blurred boundaries in complex scenes. Existing methods usually rely on post-activation probability-domain supervision for discrimination, where weak targets and strong clutter may produce saturated and close probabilities, limiting weak-target discrimination. Meanwhile, blurred boundaries and halo-like predictions mainly stem from thermal diffusion, tiny target scale, boundary uncertainty, and insufficient explicit contour constraints. To address these issues, we propose Adaptive-Contrastive SLSIoU (AC-SLSIoU), a plug-and-play discriminative and shape-aware loss for IRSTD. Specifically, a Logit-Domain Margin Constraint (LDMC) is introduced to enlarge the response gap between targets and informative hard negatives in the logit space, thereby enhancing weak-target discrimination. Adaptive Boundary Suppression (ABS) applies scale-aware annular penalties to refine target contours and suppress halo-like overflow responses. In addition, False-Alarm Focal Loss assigns larger weights to high-probability negative samples, further penalizing persistent high-confidence false alarms. Without introducing extra inference overhead, the proposed method can be seamlessly integrated into existing detectors and consistently improves both detection accuracy and shape quality. Extensive experiments and cross-backbone evaluations demonstrate the effectiveness, robustness, and generalization ability of the proposed method for infrared small target detection.

cs.CV↗
arXiv2026

ForeSea: AI Forensic Search with Multi-modal Queries for Video Surveillance

Despite decades of work, surveillance still struggles in searching and reasoning about specific targets across long, multi-camera videos. Existing methods - tracking, retrieval, and video LLMs require heavy manual filtering, capture only shallow attributes, and fail at temporal understanding. Prior benchmarks are also limited to basic retrieval and question answering, without addressing real world challenges that often involve multimodal queries and temporal grounding (e.g., "When did this person join the fight?" with the person's image). To address this gap, we introduce ForeSeaQA, a new benchmark specifically designed for video QA with image-and-text queries and timestamped annotations of key events. The dataset consists of long-horizon surveillance footage paired with diverse multimodal questions, enabling systematic evaluation of retrieval, temporal grounding, and multimodal reasoning in realistic forensic conditions. Not limited to this benchmark, we propose ForeSea, an AI forensic search system with a 3-stage, plug-and-play pipeline. (1) A tracking module filters irrelevant footage; (2) a multimodal embedding module indexes the remaining clips; and (3) during inference, the system retrieves top-K candidate clips for a video LLM to answer queries and localize events. On ForeSeaQA benchmark, ForeSea improves accuracy by 3.1 points and temporal IoU by 10.1 points over prior retrieval-augmented baselines. To our knowledge, ForeSeaQA is the first benchmark to support complex multimodal queries with precise temporal grounding, and ForeSea is the first VideoRAG system built to excel in this setting.

cs.CV↗
arXiv2026

Entropy-Driven Structural Phase Transition in Nb$_3$Cl$_8$ via Density Functional Theory and an Effective Model

As a prototypical flat-band cluster Mott insulator on an effective triangular lattice, Nb$_3$Cl$_8$ is a potential candidate for hosting a quantum spin liquid (QSL) state. Nevertheless, a first-order structural phase transition around 90K transforms the high-temperature paramagnetic $α$ phase into the low-temperature nonmagnetic $β$ phase, suppressing the candidate QSL regime of the $α$ phase. To clarify the microscopic origin of this transition, we combine first-principles calculations with an extended Hubbard model to construct a unified free-energy framework. This framework reveals that the transition is jointly driven by phonon and spin entropy: the $α$ phase is stabilized by softer phonons and larger paramagnetic spin entropy, whereas the $β$ phase is favored by interlayer dimerization, which hardens the phonons and quenches the spin entropy through singlet formation. Furthermore, by evaluating the pressure-dependent generalized enthalpy, we provide a thermodynamic explanation for the suppression of the transition under c-axis uniaxial pressure, where stabilizing the $α$ phase may allow the candidate QSL regime of the $α$ phase to be explored at low temperatures.

cond-mat.mtrl-sci↗
arXiv2026

OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning

Outcome-based reinforcement learning provides a stable optimization backbone for language agents, but its sparse trajectory-level rewards provide little guidance on which intermediate decisions should be reinforced or suppressed. On-policy self-distillation offers dense token-level supervision, yet existing skill-conditioned variants often rely on external skill memories or retrieved privileged context, which are costly to maintain and can be mismatched with the state distribution induced by the current policy in multi-turn interaction. We propose \textbf{OPID} (\textbf{O}n-\textbf{P}olicy Sk\textbf{i}ll \textbf{D}istillation), a framework that extracts skill supervision directly from completed on-policy trajectories. OPID represents trajectory hindsight as hierarchical skills: episode-level skills capture global workflows or failure-avoidance rules, while step-level skills capture local decision knowledge at critical timesteps. A critical-first routing mechanism uses step-level skills when critical decisions are identified and falls back to episode-level skills as default guidance otherwise. The selected skill is injected into the interaction history, allowing the old policy to re-score the same sampled response under both original and skill-augmented contexts. The resulting log-probability shift yields a token-level self-distillation advantage, which is combined with the outcome advantage for policy optimization. OPID thus preserves RL as the primary training objective while introducing dense, distribution-matched hindsight supervision. Experiments on ALFWorld, WebShop and Search-based QA demonstrate that OPID generally improves agent performance, sample efficiency, and robustness over outcome-only RL and existing skill-distillation baselines. Our code is available at https://github.com/jinyangwu/OPID/tree/main.

cs.CL↗
arXiv2026

Beyond a Single Light: A Large-Scale Aerial Dataset for Urban Scene Reconstruction Under Varying Illumination

Recent advances in Neural Radiance Fields and 3D Gaussian Splatting have demonstrated strong potential for large-scale UAV-based 3D reconstruction tasks by fitting the appearance of images. However, real-world large-scale captures are often based on multi-temporal data capture, where illumination inconsistencies across different times of day can significantly lead to color artifacts, geometric inaccuracies, and inconsistent appearance. Due to the lack of UAV datasets that systematically capture the same areas under varying illumination conditions, this challenge remains largely underexplored. To fill this gap, we introduceSkyLume, a large-scale, real-world UAV dataset specifically designed for studying illumination robust 3D reconstruction in urban scene modeling: (1) We collect data from 10 urban regions data comprising more than 100k high resolution UAV images (four oblique views and nadir), where each region is captured at three periods of the day to systematically isolate illumination changes. (2) To support precise evaluation of geometry and appearance, we provide per-scene LiDAR scans and accurate 3D ground-truth for assessing depth, surface normals, and reconstruction quality under varying illumination. (3) For the inverse rendering task, we introduce the Temporal Consistency Coefficient (TCC), a metric that measuress cross-time albedo stability and directly evaluates the robustness of the disentanglement of light and material. We aim for this resource to serve as a foundation that advances research and real-world evaluation in large-scale inverse rendering, geometry reconstruction, and novel view synthesis.

cs.CV↗
arXiv2026

Latent Block-Diffusion Temporal Point Processes: A Semi-Autoregressive Framework for Asynchronous Event Sequence Generation

Modeling and sampling from the underlying distribution of asynchronous event sequences are crucial in various real-world applications, including social networks, medical diagnosis, and financial transactions. Existing autoregressive methods suffer from error accumulation during multi-step generation, while non-autoregressive diffusion methods are typically limited to fixed-length output sequences. In this paper, we propose Latent Block-Diffusion Temporal Point Processes (LBDTPP), a novel semi-autoregressive TPP framework that introduces a latent block diffusion mechanism for high-quality and variable-length event sequence generation. The core idea is to define an autoregressive probability distribution over event blocks in latent space and perform Gaussian diffusion within each block. By sequentially generating blocks while simultaneously sampling events in each block, LBDTPP preserves the length flexibility of autoregressive TPPs and inherits the parallel high-quality generation capability of diffusion models. Theoretically, we derive Wasserstein error bounds showing that, under suitable local approximation and prefix-stability assumptions, block-wise generation can reduce error accumulation compared with event-wise autoregressive generation. Extensive experiments on six real-world benchmark datasets demonstrate that LBDTPP outperforms state-of-the-art TPP baselines in both unconditional and conditional generation tasks. Further empirical analyses verify the benefits of latent-space diffusion and block-wise generation, and reveal the trade-off between generation quality and block size. Our code is available at https://github.com/Zh-Shuai/LBDTPP.

cs.LG↗
arXiv2026

Spark: Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning

Reinforcement learning has empowered large language models to act as intelligent agents, yet training them for long-horizon tasks remains challenging due to the scarcity of high-quality trajectories, especially under limited resources. Existing methods typically scale up rollout sizes and indiscriminately allocate computational resources among intermediate steps. Such attempts inherently waste substantial computation budget on trivial steps while failing to guarantee sample quality. To address this, we propose \textbf{Spark} (\textbf{S}trategic \textbf{P}olicy-\textbf{A}ware explo\textbf{R}ation via \textbf{K}ey-state dynamic branching), a novel framework that selectively branches at critical decision states for resource-efficient exploration. Our key insight is to activate adaptive branching exploration at critical decision points to probe promising trajectories, thereby achieving precise resource allocation that prioritizes sampling quality over blind coverage. This design leverages the agent's intrinsic decision-making signals to reduce dependence on human priors, enabling the agent to autonomously expand exploration and achieve stronger generalization. Experiments across diverse tasks (e.g., embodied planning), demonstrate that \textsc{Spark} achieves superior success rates with significantly fewer training samples, exhibiting robust generalization even in unseen scenarios. Our code and checkpoints are available at https://github.com/jinyangwu/SPARK.

cs.LG↗