arXiv subjects

Jiarui Zhao

Publications and source records attributed to Jiarui Zhao.

At least 19 recordsLinked to original sources

arXiv2026

Impact of Residual Angular Chirp in a Petawatt-class Laser System on Laser-driven Proton Acceleration

Laser-driven proton acceleration has attracted considerable interest owing to its appealing potential in versatile applications including cancer therapy. Proton energies depend critically on the on-target intensities, yet the detrimental impact of focal spot degradation induced by spatiotemporal couplings on the acceleration remains insufficiently elucidated. In this study, we demonstrate that residual angular chirp (AC), stemming from minor misalignments of the grating compressor in a Petawatt-class laser system, acts as a critical bottleneck for proton acceleration. Experimental results reveal that even around 100 microradians of grating misalignment induces substantial focal-spot elongation and a pronounced reduction in peak intensity. By implementing an in situ spectral-blocking diagnostic, we effectively eliminated the residual AC and restored a near-diffraction-limited focus. This optimization led to a significant recovery of the on-target intensity, resulting in a twofold increase in the proton cutoff energy. Our work presents a successful demonstration of diagnosing and eliminating residual AC. This provides a practical reference for generating high-energy proton beams and supporting their diverse applications in a PW-class laser.

physics.plasm-ph↗

arXiv2026

GPU-Accelerated Matrix-Based Hough Transform for Online Track Reconstruction in the STCF MDC

The Super Tau-Charm Facility (STCF) is a proposed next-generation high-luminosity electron-positron collider operating at center-of-mass energies of 2-7 GeV for precision studies of tau-charm physics. Its high event rate, detector occupancy, and background level impose stringent requirements on real-time track reconstruction in MDC, particularly for low-transverse-momentum particles with strongly curved or multi-turn trajectories. To address this challenge, we develop a GPU-accelerated matrix-based Hough transform method for online track reconstruction in the STCF MDC. Following an algorithm-architecture co-design paradigm, the data representation and computational workflow of the conformal Hough transform are reformulated for GPU execution. The original irregular parameter-space computations are organized into regular matrix-based operations, and the core computations are adapted to CUDA thread organization and the GPU memory hierarchy to exploit the inherent parallelism of the Hough transform and reduce computational and data-transfer overhead. Tests on five representative simulated physics channels with nominal background overlay show an average signal retention ratio of 93.04%, while reducing the retained hit volume to 34.92% of the original level. The GPU implementation processes 1,000 events in approximately 0.14s, achieving a speedup of 151.57 x compared with the CPU baseline. These results demonstrate that the proposed method substantially improves track reconstruction throughput while preserving track-associated hits, providing a new methodological perspective for real-time track reconstruction in future high-luminosity particle-collider experiments.

hep-ex↗

arXiv2026

Neural-Network-Assisted Binary Template Construction for Matrix-Based Pattern Matching in the STCF MDC

The Super Tau-Charm Facility, operating at high luminosity, will produce high event rates and high data throughput, imposing stringent requirements on fast track finding and data reduction and compression algorithms in the High-Level Trigger. Local track segment finding in the Main Drift Chamber underpins subsequent segment combination and full track reconstruction, yet high background rates and limited detection efficiency can significantly increase the risk of false triggers and signal loss in pattern matching algorithms. This paper presents a neural-network-assisted framework for constructing binary template libraries used in matrix-based pattern matching for MDC local track segment finding. The framework formulates template construction as a differentiable multi-objective optimization problem, employing a neural network to jointly learn template parameters under multiple constraints. After training, only binary template pairs are exported and deployed into the existing bitwise pattern matching routine, requiring no neural network inference at runtime and thus preserving the deterministic, fast, and parallelizable nature of the online algorithm. Experimental results based on simulation samples demonstrate that, under limited detection efficiency, the resulting template library maintains relatively high signal retention across different transverse momentum ranges and background levels, and can be flexibly tailored to adjust the coverage range according to practical requirements. The proposed approach decouples the physics performance from the computational speed by combining the improved physics performance brought by offline neural-network-based optimization with the determinism and high speed of a conventional online algorithm, suggesting a new research direction for artificial-intelligence-enhanced online data processing in high-luminosity particle collider experiments.

hep-ex↗

arXiv2026

LoHoSearch: Benchmarking Long-Horizon Search Agents Beyond the Human Difficulty Ceiling

Search agent benchmarks exemplified by BrowseComp have rapidly saturated over the past year, with the strongest models surpassing 90% accuracy. Since these benchmarks are predominantly human-authored, annotators lack a global perspective on entity statistics and cannot systematically maximize search space size and structural complexity. This creates a difficulty ceiling that is hard to break. To address this, we introduce LoHoSearch (Long-Horizon Search Agents), a challenging benchmark comprising 544 human-verified questions across 11 domains. LoHoSearch is constructed via an automated pipeline built upon a knowledge graph covering over 7 million Wikipedia entities, which selects relations with large search spaces and assembles them into structurally complex questions with KG-verified unique answers. Our evaluation demonstrates that even the strongest model achieves only 34.74% accuracy, and existing context management strategies (best +6.8%) yield far smaller gains than on prior benchmarks. LoHoSearch provides a more demanding standard for evaluating long-horizon reasoning and context management in search agents.

cs.CL↗

arXiv2026

SpikeHash: Learning Binary Codes with Spiking Neural Networks for Cross-Modal Hashing Retrieval

Cross-modal hashing retrieval encodes heterogeneous data into compact binary codes for efficient Hamming-space search. Existing methods usually learn cross-modal semantics in continuous feature spaces and generate binary codes through a final sign operation, which weakly couples training optimization with discrete hash retrieval. We propose SpikeHash, a unified spiking framework that formulates cross-modal hashing as spike-state evolution, directional spike interaction, and competitive spike readout. Specifically, SpikeHash converts image and text features into multi-timestep spike sequences. In a shared Hamming space, the two spike sequences jointly drive the temporal evolution of a shared hash state. Cross-modal interaction is further performed through directional spike modulation, enabling each modality to influence the firing dynamics of the other. Crucially, SpikeHash replaces the conventional continuous hash head with a positive-negative spiking hash readout, where each hash bit is produced by temporal competition between paired spike channels. Experimental results show that SpikeHash achieves competitive retrieval accuracy on three benchmark datasets while reducing the parameter size, operation count, and estimated energy of the hash learning stage, suggesting a compact spiking alternative to conventional continuous hash mapping. The project page is available at https://shuqiao-111.github.io/.

cs.IR↗

arXiv2026

HotLoop Optimization of Petawatt Laser Focal Spot via a Twin-Focus Scheme

Achieving diffraction-limited focusing of high-power laser pulses to generate ultra-high intensities is crucial for developing compact laser-driven particle accelerators and exploring strong-field quantum electrodynamics. However, accurately diagnosing and optimizing the focal spots of petawatt (PW) laser pulses remains a significant challenge. In this work, we present an experimental methodology utilizing a twin-focus scheme to precisely characterize the intensity distribution and wavefront of focused PW femtosecond laser pulses, and employ it to elucidate their power-dependent evolution. Furthermore, we optimized the focal spots at full power via our in-situ wavefront correction method termed ``HotLoop', achieving a Strehl ratio of 0.80 for 1 PW laser pulses. Consequently, the cutoff proton energies in laser proton acceleration experiments were significantly enhanced. The success of this approach underscores the necessity of in-situ high-energy wavefront correction for ultra-high intensity laser-matter interactions.

physics.optics↗

arXiv2026

A Spatial-Resolved Proton Energy Spectrometer Based on a Scintillation-Fiber Cube

Advanced particle acceleration methods have produced high-peak-current ion beams with broad energy spread and complex spatial distribution. There is an urgent need to develop online spatial-resolved energy spectrometers for high-energy pulsed ions. This paper introduces a novel spectrometer based on a scintillation-fiber cube for online diagnosis of proton beams with broadband energy spread and complex spatial distribution. We present its working principles, experimental setup, and comprehensive calibration using monoenergetic and spatially uniform proton beams generated by a synchrotron accelerator. Calibration results confirm an energy measurement range of 6-93 MeV, a relative energy uncertainty of 0.6% at 80 MeV, and a pixel size of 0.5 mm for beam profile reconstruction. By exploiting a custom-designed energy degrader, we generated a complex proton beam and measured it with the scintillation-fiber cube spectrometer (SFICS). The results demonstrate the spectrometer's potential for online measurement of the energy spectrum and spatial distribution of complex proton beams.

physics.acc-ph↗

arXiv2026

Unconventional Quantum Criticality in Long-Range Spin-1 Chains: Insights from Entanglement Entropy and Bipartite Fluctuations

We study the ground-state phase diagram of a spin-1 Heisenberg chain with staggered long-range (LR) interactions decaying as $\propto r^{-\alpha}$ using a quantum Monte Carlo approach based on the split-spin representation. This formulation enables efficient large-scale simulations by mapping the spin-1 model onto spin-$1/2$ degrees of freedom with local projection constraints. We resolve the continuous quantum phase transition between the gapped Haldane phase at large $\alpha$ (short-range regime) and a gapless antiferromagnetically ordered N\'eel phase at small $\alpha$ (LR regime), where the continuous SU(2) symmetry is broken. From finite-size scaling and crossing point analyses, we determine the critical point to be at $\alpha_c = 2.49(1)$ and extract the associated critical exponents, which indicate unconventional criticality. In particular, the transition is found to be nonconformal, characterized by a dynamical exponent $z \neq 1$. We further analyze the scaling of entanglement entropy and bipartite fluctuations across the transition, and determine the corresponding universal scalings in both phases and at criticality.

cond-mat.str-el↗

arXiv2026

Detecting Symmetry-Resolved Entanglement: A Quantum Monte Carlo Approach

Symmetry and entanglement are two fundamental concepts in quantum many-body physics. Their interplay is captured by symmetry-resolved entanglement, which decomposes the total entanglement into contributions from different symmetry sectors. Computing symmetry-resolved entanglement in strongly interacting higher-dimensional quantum systems remains challenging. Here, we formulate and implement an estimator-based quantum Monte Carlo (QMC) framework for computing symmetry-resolved R\'enyi entropies (SRRE) in sign-problem-free interacting lattice systems by measuring disorder (symmetry-twisted) operators in ordinary and replica ensembles and reconstructing SRRE from the corresponding charged moments. We validate the framework in two controlled one-dimensional settings: the transverse-field Ising model (TFIM), for which exact conformal-field-theory predictions are available, and the interacting Heisenberg chain, which tests the $U(1)$ symmetry-sector reconstruction and its finite-size behavior. We then apply the method to the two-dimensional TFIM. Within the accessible system sizes and a phenomenological finite-size extrapolation, our data provide numerical evidence consistent with entanglement equipartition at the $(2+1)$D Ising critical point. Our work establishes a practical numerical route to symmetry-resolved entanglement in interacting lattice models and provides a framework for future studies beyond one dimension.

cond-mat.str-el↗

arXiv2026

Teacher-Guided Student Self-Knowledge Distillation Using Diffusion Model

Existing Knowledge Distillation (KD) methods often align feature information between teacher and student by exploring meaningful feature processing and loss functions. However, due to the difference in feature distributions between the teacher and student, the student model may learn incompatible information from the teacher. To address this problem, we propose teacher-guided student Diffusion Self-KD, dubbed as DSKD. Instead of the direct teacher-student alignment, we leverage the teacher classifier to guide the sampling process of denoising student features through a light-weight diffusion model. We then propose a novel locality-sensitive hashing (LSH)-guided feature distillation method between the original and denoised student features. The denoised student features encapsulate teacher knowledge and could be regarded as a teacher role. In this way, our DSKD method could eliminate discrepancies in mapping manners and feature distributions between the teacher and student, while learning meaningful knowledge from the teacher. Experiments on visual recognition tasks demonstrate that DSKD significantly outperforms existing KD methods across various models and datasets. Our code is attached in supplementary material.

cs.CV↗

arXiv2026

LongCat-Flash-Thinking-2601 Technical Report

We introduce LongCat-Flash-Thinking-2601, a 560-billion-parameter open-source Mixture-of-Experts (MoE) reasoning model with superior agentic reasoning capability. LongCat-Flash-Thinking-2601 achieves state-of-the-art performance among open-source models on a wide range of agentic benchmarks, including agentic search, agentic tool use, and tool-integrated reasoning. Beyond benchmark performance, the model demonstrates strong generalization to complex tool interactions and robust behavior under noisy real-world environments. Its advanced capability stems from a unified training framework that combines domain-parallel expert training with subsequent fusion, together with an end-to-end co-design of data construction, environments, algorithms, and infrastructure spanning from pre-training to post-training. In particular, the model's strong generalization capability in complex tool-use are driven by our in-depth exploration of environment scaling and principled task construction. To optimize long-tailed, skewed generation and multi-turn agentic interactions, and to enable stable training across over 10,000 environments spanning more than 20 domains, we systematically extend our asynchronous reinforcement learning framework, DORA, for stable and efficient large-scale multi-environment training. Furthermore, recognizing that real-world tasks are inherently noisy, we conduct a systematic analysis and decomposition of real-world noise patterns, and design targeted training procedures to explicitly incorporate such imperfections into the training process, resulting in improved robustness for real-world applications. To further enhance performance on complex reasoning tasks, we introduce a Heavy Thinking mode that enables effective test-time scaling by jointly expanding reasoning depth and width through intensive parallel thinking.

cs.AI↗

arXiv2025

SpectralTrain: A Universal Framework for Hyperspectral Image Classification

Hyperspectral image (HSI) classification typically involves large-scale data and computationally intensive training, which limits the practical deployment of deep learning models in real-world remote sensing tasks. This study introduces SpectralTrain, a universal, architecture-agnostic training framework that enhances learning efficiency by integrating curriculum learning (CL) with principal component analysis (PCA)-based spectral downsampling. By gradually introducing spectral complexity while preserving essential information, SpectralTrain enables efficient learning of spectral -- spatial patterns at significantly reduced computational costs. The framework is independent of specific architectures, optimizers, or loss functions and is compatible with both classical and state-of-the-art (SOTA) models. Extensive experiments on three benchmark datasets -- Indian Pines, Salinas-A, and the newly introduced CloudPatch-7 -- demonstrate strong generalization across spatial scales, spectral characteristics, and application domains. The results indicate consistent reductions in training time by 2-7x speedups with small-to-moderate accuracy deltas depending on backbone. Its application to cloud classification further reveals potential in climate-related remote sensing, emphasizing training strategy optimization as an effective complement to architectural design in HSI models. Code is available at https://github.com/mh-zhou/SpectralTrain.

cs.CV↗

arXiv2025

DCL-SE: Dynamic Curriculum Learning for Spatiotemporal Encoding of Brain Imaging

High-dimensional neuroimaging analyses for clinical diagnosis are often constrained by compromises in spatiotemporal fidelity and by the limited adaptability of large-scale, general-purpose models. To address these challenges, we introduce Dynamic Curriculum Learning for Spatiotemporal Encoding (DCL-SE), an end-to-end framework centered on data-driven spatiotemporal encoding (DaSE). We leverage Approximate Rank Pooling (ARP) to efficiently encode three-dimensional volumetric brain data into information-rich, two-dimensional dynamic representations, and then employ a dynamic curriculum learning strategy, guided by a Dynamic Group Mechanism (DGM), to progressively train the decoder, refining feature extraction from global anatomical structures to fine pathological details. Evaluated across six publicly available datasets, including Alzheimer's disease and brain tumor classification, cerebral artery segmentation, and brain age prediction, DCL-SE consistently outperforms existing methods in accuracy, robustness, and interpretability. These findings underscore the critical importance of compact, task-specific architectures in the era of large-scale pretrained networks.

cs.CV↗

arXiv2025

EPIPTrack: Rethinking Prompt Modeling with Explicit and Implicit Prompts for Multi-Object Tracking

Multimodal semantic cues, such as textual descriptions, have shown strong potential in enhancing target perception for tracking. However, existing methods rely on static textual descriptions from large language models, which lack adaptability to real-time target state changes and prone to hallucinations. To address these challenges, we propose a unified multimodal vision-language tracking framework, named EPIPTrack, which leverages explicit and implicit prompts for dynamic target modeling and semantic alignment. Specifically, explicit prompts transform spatial motion information into natural language descriptions to provide spatiotemporal guidance. Implicit prompts combine pseudo-words with learnable descriptors to construct individualized knowledge representations capturing appearance attributes. Both prompts undergo dynamic adjustment via the CLIP text encoder to respond to changes in target state. Furthermore, we design a Discriminative Feature Augmentor to enhance visual and cross-modal representations. Extensive experiments on MOT17, MOT20, and DanceTrack demonstrate that EPIPTrack outperforms existing trackers in diverse scenarios, exhibiting robust adaptability and superior performance.

cs.CV↗

arXiv2024

Scaling of Disorder Operator and Entanglement Entropy at Easy-Plane Deconfined Quantum Criticalities

We systematically investigate the scaling behaviors of the disorder operator and the entanglement entropy (EE) of the easy-plane JQ (EPJQ) model at its transitions between the antiferromagnetic XY ordered phase (AFXY) and the valence bond solid (VBS) phase. We find there exists a tiny yet finite value of the order parameters at the AFXY-VBS phase transition points of the EPJQ model, and the finite order parameter is strengthened as anisotropy $\Delta$ varies from the Heisenberg limit ($\Delta=1$) to the easy-plane limit ($\Delta=0$). This observation provides evidence that the N\'eel-VBS transition in the JQ model setting evolves from weak to prominent first-order transition as the system becomes anisotropic. Furthermore, both EE and disorder operator with smooth boundary cut exhibit anomalous scaling behavior at the transition points, resembling the scaling inside the Goldstone mode (AFXY) phase, and the anomalous scaling becomes strengthened as the transition becomes more first order. In particular, for $\Delta \le 0.3$, the obtained log-coefficients converge to 0.5 which is the same as the contribution from one Goldstone mode in the N\'eel phase. For $\Delta > 0.3$, the log-coefficients are smaller and our findings might suffer from strong finite-size effects due to the fact that the remaining N\'eel order here is quite tiny.

cond-mat.str-el↗

arXiv2024

Unconventional Scalings of Quantum Entropies in Long-Range Heisenberg Chains

In this work, building on state-of-the-art quantum Monte Carlo simulations, we perform systematic finite-size scaling of both entanglement and participation entropies for long-range Heisenberg chain with unfrustrated power-law decaying interactions. We find distinctive scaling behaviors for both quantum entropies in the various regimes explored by tuning the decay exponent $\alpha$, thus capturing non-trivial features through logarithmic terms, beyond the case of linear Nambu-Goldstone modes. Our systematic analysis reveals that the quantum entanglement information, hidden in the scaling of the two studied entropies, can be obtained to the same level of order parameters and other usual finite-size observables of quantum many-body lattice models. The analysis and results obtained here can readily apply to more quantum criticalities in 1D and 2D systems.

cond-mat.str-el↗

arXiv2024

Extracting Universal Corner Entanglement Entropy during the Quantum Monte Carlo Simulation

The subleading corner logarithmic corrections in entanglement entropy (EE) are crucial for revealing universal characteristics of the quantum critical points (QCPs), but they are challenging to detect. Motivated by recent developments in the stable computation of EE in (2+1)D quantum many-body systems, we have developed a new method for directly measuring the corner contribution in EE with less computational cost. The cornerstone of our approach is to measure the subtracted corner entanglement entropy (SCEE) defined as the difference between the EEs of subregions with the same boundary length for smooth and cornered boundaries during the sign-problem free quantum Monte Carlo simulation. Our improved method inherently eliminates not only the area law term of EE but also the subleading log-corrections arising from Goldstone modes, leaving the universal corner contribution as the leading term of SCEE with greatly improved data quality. Utilizing this advanced approach, we calculate the SCEE of the bilayer Heisenberg model on both square and honeycomb lattices across their (2+1)D O(3) QCPs with different opening angles on entanglement boundary, and obtain the accurate values of the corresponding universal corner log-coefficients. These findings will encourage further theoretical investigations to access controlled universal information for interacting CFTs at (2+1)D.

cond-mat.str-el↗

arXiv2024

Electron acceleration and X-ray generation from near-critical-density carbon nanotube foams driven by moderately relativistic lasers

Direct laser acceleration of electrons in near-critical-density (NCD) carbon nanotube foams (CNFs) has its advantages in the high-efficiency generation of relativistic electrons and broadband X-rays. Here, we report the first simultaneous measurement on the spectra of laser-driven electrons and X-rays from CNFs at moderately relativistic intensities of around 5\times{10}^{19}\ W/cm^2.\ The density and thickness of the CNFs were scanned in the experiments, indicating the optimized electrons temperature of 5.5 MeV and X-ray critical energy of 5 keV. Two-dimensional (2D) particle-in-cell (PIC) simulations confirm that the electrons, with a temperature significantly higher than the pondermotive scale, are directly accelerated by the laser along the NCD plasma channel, while the bright X-rays are emitted by these electrons through betatron radiation or Thomson backscattering inside the channel. The simultaneously generated electrons and X-rays, automatically synchronized with the femtosecond laser driver, are suitable for applications such as bi-modal radiography.

physics.plasm-ph↗