arXiv subjects

Hui Jiang

Publications and source records attributed to Hui Jiang.

At least 19 recordsLinked to original sources

arXiv2026

Fast distance computation of multivariate distributions via nonparanormal transport

With the increasing availability of data objects in the form of probability distributions, there is a growing need for statistical methods tailored to distributional data. Distance measures, especially the pairwise distance matrix between data objects, provide the foundation for a wide range of modern data analysis methods, such as clustering, multidimensional scaling, and distance-based regression, among others. The Wasserstein distance is commonly used with distributional data due to its compelling optimal transport property. However, while the Wasserstein distance can be efficiently computed for univariate distributions, its application to multivariate distributions is limited due to high computational costs. To address these scalability issues, we introduce the Nonparanormal Transport (NPT) metric, a closed-form distance based on the flexible nonparanormal distribution family for modeling skewed and non-Gaussian multivariate data. Simulation studies demonstrate that NPT maintains a high level of agreement with the Wasserstein distance, while being at least 1000 times faster than its efficient variants when computing a 100-distribution pairwise distance matrix in both 2 and 5 dimensions. We illustrate the utility of NPT through a multidimensional scaling analysis of bivariate oxygen desaturation distributions of 723 individuals with sleep apnea in the Sleep Heart Health Study.

stat.ME↗

arXiv2026

One Click to Leak: Characterizing the Real-World Usage and Threat Impact of MNO-based Single Sign-On Websites

Mobile Network Operator (MNO)-based Single Sign-On (MSSO) is a password-free authentication framework relying on mobile data sessions. Unlike traditional SSO, it shifts the Identity Provider (IdP) to the MNO and the authentication anchor to the Service Provider (SP). MSSO is increasingly deployed and has expanded from mobile apps to websites, yet its web ecosystem and security risks remain largely unexplored. We analyze mainstream MSSO deployments and identify a 3-phase workflow with three trust defects enabling trust hijacking. We further demonstrate One-Click-to-Leak (OCL) attacks, where a single webpage visit can leak sensitive identity information (e.g., phone numbers). With a leading security company, we conduct the first large-scale, longitudinal study of web-based MSSO. We design a hierarchical detection framework using passive DNS correlations and URL reconstruction from search data to identify MSSO-enabled websites. Over one year, we identified 116,852 website URLs across 729 apex domains. Of these URLs, 73.6% exhibit at least one trust defect: 69.4% expose developer credentials, and 27.1% issue high-privilege tokens before user consent, indicating widespread OCL-enabling trust defects. Among the 729 apex domains, 31.8% rely on Resellers, obscuring the downstream SP from the MNO in the analyzed flows. Script analysis identifies 101 websites strongly associated with OCL attack behavior. With our partner, we trace a representative upstream platform subsequently seized by law enforcement and uncover a monetized underground ecosystem. Sanitized backend data shows that it collected 14,100 users' phone numbers within three days and linked them to sensitive information such as browsing activity. Our work provides a comprehensive study of web-based MSSO deployment and security implications. Through responsible disclosure, our work helps secure the mobile authentication ecosystem.

cs.CR↗

arXiv2026

Beyond Natural Images: Rethinking AI-Generated Image Detection in Documents

AI-generated image detection has attracted increasing attention, but existing evaluations mainly focus on natural images, leaving AI-generated document images largely underexplored. This omission is concerning because documents often appear in sensitive real-world scenarios, such as invoices, expense reports, certificates, and medical records. In this paper, we first construct a controlled diagnostic benchmark, AIGDoc-Pilot, and reveal that existing detectors suffer substantial performance degradation on AI-generated document images, with the mean AUC dropping by more than 7%. Based on this, we further reveal two document-specific properties behind this gap: generation artifacts exhibit strong spatial inconsistency across local regions, and text density significantly affects real-synthetic separability, where text-dense regions offer stronger discriminative evidence. Motivated by these findings, we construct AIGDoc, a larger document-centric dataset containing diverse real-world documents and AI-generated counterparts produced by multiple advanced generation and editing models. Extensive experiments on AIGDoc demonstrate that existing detectors still struggle to reliably identify AI-generated documents, while document-based training partially narrows the gap. Together, these results offer valuable insights for developing dependable and generalizable detectors in document-centric scenarios. The code and datasets will be made publicly available upon acceptance of the paper.

cs.CV↗

arXiv2026

Agent Harness Distillation: Inference-Time Harness Extraction and Exploitation in Autonomous Multi-Agent Systems

Autonomous multi-agent systems (AMAS) built on large language models (LLMs), such as Hermes, increasingly rely on inference-time harnesses to coordinate reasoning and action. Constructing these harnesses requires substantial engineering effort and computational resources, as they are iteratively optimized over a combinatorial search space while co-evolving with the underlying LLM. Inference-time harnesses therefore constitute valuable intellectual property (IP). Although prior work has investigated IP leakage in static multi-agent systems with pre-configured architectures, it remains unclear whether similar risks arise in AMAS, where harness behavior emerges dynamically during inference. To address this gap, we introduce Agent Harness Distillation (AHD), a framework for studying the security risks arising from inference-time harness extraction in AMAS. We formalize harness extraction as a new security problem and develop an evaluation framework for quantifying such risks. AHD extracts inference-time harness capabilities from a target agent through black-box interactions and consists of two stages. In the pre-distillation stage, AHD infers inference-time harness behaviors from the responses of the target agent and constructs an initial harness. In the post-distillation stage, AHD iteratively refines the initial harness to align with the behavioral patterns of the target agent. Experiments on real-world AMAS across multiple backbone LLMs demonstrate the effectiveness of AHD and reveal substantial IP leakage risks. We further propose a deception-based defense that reduces harness extraction effectiveness while preserving the utility of the protected agent. Our findings uncover a previously underexplored security threat to AMAS.

cs.CR↗

arXiv2026

Localized inhomogeneity and position-dependent stability of migratory bird formations

We investigate how localized inhomogeneity affects the geometry and stability of migratory bird formations. We use a lifting-line model with a horseshoe-vortex representation to describe the longitudinal dynamics of aerodynamic interactions. As a reference case, we first analyze homogeneous formations and show that their steady states exhibit a U-shaped geometry with hierarchical streamwise spacing, in which adjacent birds become progressively closer toward the leader. We then introduce localized inhomogeneity by modifying the wingspan of a single bird, with its physical properties determined by scaling relations. We determine the range of wingspan variation that preserves a stable formation. The stability range depends strongly on the position of the modified bird, being narrower near the outer wing and broader near the leader. These findings provide a minimal dynamical framework for understanding how local aerodynamic interactions and localized individual differences affect collective flight structures.

nlin.PS↗

arXiv2026

UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory

Self-evolving memory serves as the trainable parameters for Large Language Models (LLMs)-based agents, where extraction (distilling insights from experience) and management (updating the memory bank) must be tightly coordinated. Existing methods predominately optimize memory management while treating memory extraction as a static process, resulting in poor generalization, where agents accumulate instance-specific noise rather than robust memories. To address this, we propose Unified Memory Extraction and Management (UMEM), a self-evolving agent framework that jointly optimizes a Large Language Model to simultaneous extract and manage memories. To mitigate overfitting to specific instances, we introduce Semantic Neighborhood Modeling and optimize the model with a neighborhood-level marginal utility reward via GRPO. This approach ensures memory generalizability by evaluating memory utility across clusters of semantically related queries. Extensive experiments across five benchmarks demonstrate that UMEM significantly outperforms highly competitive baselines, achieving up to a 10.67% improvement in multi-turn interactive tasks. Futhermore, UMEM maintains a monotonic growth curve during continuous evolution. Codes and models will be publicly released.

cs.CL↗

arXiv2025

MUSE: Multi-Scale Dense Self-Distillation for Nucleus Detection and Classification

Nucleus detection and classification (NDC) in histopathology analysis is a fundamental task that underpins a wide range of high-level pathology applications. However, existing methods heavily rely on labor-intensive nucleus-level annotations and struggle to fully exploit large-scale unlabeled data for learning discriminative nucleus representations. In this work, we propose MUSE (MUlti-scale denSE self-distillation), a novel self-supervised learning method tailored for NDC. At its core is NuLo (Nucleus-based Local self-distillation), a coordinate-guided mechanism that enables flexible local self-distillation based on predicted nucleus positions. By removing the need for strict spatial alignment between augmented views, NuLo allows critical cross-scale alignment, thus unlocking the capacity of models for fine-grained nucleus-level representation. To support MUSE, we design a simple yet effective encoder-decoder architecture and a large field-of-view semi-supervised fine-tuning strategy that together maximize the value of unlabeled pathology images. Extensive experiments on three widely used benchmarks demonstrate that MUSE effectively addresses the core challenges of histopathological NDC. The resulting models not only surpass state-of-the-art supervised baselines but also outperform generic pathology foundation models.

cs.CV↗

arXiv2025

Stochasticity-induced non-Hermitian skin criticality

Typically, scaling up the size of a system does not change the shape of its energy spectrum, other than making it denser. Exceptions, however, occur in the new phenomenon of non-Hermitian skin criticality, where closely competing generalized Brillouin zone (GBZ) solutions for non-Hermitian state accumulation give rise to anomalously scaling complex spectra. In this work, we discover that such non-Hermitian criticality can generically emerge from stochasticity in the lattice bond orientation, a surprising phenomenon only possible in 2D or beyond. Marked by system size-dependent amplification rate, it can be physically traced to the proliferation of feedback loops arising from excess local non-Hermitian skin effect (NHSE) accumulation induced by structural disorder. While weak disorder weakens the amplification as intuitively anticipated, stronger disorder enigmatically strengthens the amplification almost universally, scaling distinctly from conventional critical system. By representing cascades of local excess NHSE as ensembles of effectively coupled chains, we analytically derived a critical GBZ that predicts how state amplification scales with the system size and disorder strength, highly consistent with empirical observations. Our new mechanism for disordered-facilitated amplification applies generically to structurally perturbed non-Hermitian lattices with broken reciprocity, and would likely find applications in non-Hermitian sensing through various experimentally mature meta-material platforms.

cond-mat.dis-nn↗

arXiv2025

HSCodeComp: A Realistic and Expert-level Benchmark for Deep Search Agents in Hierarchical Rule Application

Effective deep search agents must not only access open-domain and domain-specific knowledge but also apply complex rules-such as legal clauses, medical manuals and tariff rules. These rules often feature vague boundaries and implicit logic relationships, making precise application challenging for agents. However, this critical capability is largely overlooked by current agent benchmarks. To fill this gap, we introduce HSCodeComp, the first realistic, expert-level e-commerce benchmark designed to evaluate deep search agents in hierarchical rule application. In this task, the deep reasoning process of agents is guided by these rules to predict 10-digit Harmonized System Code (HSCode) of products with noisy but realistic descriptions. These codes, established by the World Customs Organization, are vital for global supply chain efficiency. Built from real-world data collected from large-scale e-commerce platforms, our proposed HSCodeComp comprises 632 product entries spanning diverse product categories, with these HSCodes annotated by several human experts. Extensive experimental results on several state-of-the-art LLMs, open-source, and closed-source agents reveal a huge performance gap: best agent achieves only 46.8% 10-digit accuracy, far below human experts at 95.0%. Besides, detailed analysis demonstrates the challenges of hierarchical rule application, and test-time scaling fails to improve performance further.

cs.AI↗

arXiv2025

Central limit theorem and Cramér-type moderate deviations for Milstein scheme

In this paper, we investigate the Milstein numerical scheme with step size $η$ for a stochastic differential equation driven by multiplicative Brownian motion. Under some appropriate coefficient conditions, the continuous-time system and its discrete Milstein scheme approximation each possess unique invariant measures, which we denote by $π$ and $π_η$ respectively. We first establish a central limit theorem for the empirical measure $Π_η$, a statistical consistent estimator of $π_η$. Subsequently, we derive both normalized and self-normalized Cramér-type moderate deviations.

math.PR↗

arXiv2025

HAMLET-FFD: Hierarchical Adaptive Multi-modal Learning Embeddings Transformation for Face Forgery Detection

The rapid evolution of face manipulation techniques poses a critical challenge for face forgery detection: cross-domain generalization. Conventional methods, which rely on simple classification objectives, often fail to learn domain-invariant representations. We propose HAMLET-FFD, a cognitively inspired Hierarchical Adaptive Multi-modal Learning framework that tackles this challenge via bidirectional cross-modal reasoning. Building on contrastive vision-language models such as CLIP, HAMLET-FFD introduces a knowledge refinement loop that iteratively assesses authenticity by integrating visual evidence with conceptual cues, emulating expert forensic analysis. A key innovation is a bidirectional fusion mechanism in which textual authenticity embeddings guide the aggregation of hierarchical visual features, while modulated visual features refine text embeddings to generate image-adaptive prompts. This closed-loop process progressively aligns visual observations with semantic priors to enhance authenticity assessment. By design, HAMLET-FFD freezes all pretrained parameters, serving as an external plugin that preserves CLIP's original capabilities. Extensive experiments demonstrate its superior generalization to unseen manipulations across multiple benchmarks, and visual analyses reveal a division of labor among embeddings, with distinct representations specializing in fine-grained artifact recognition.

cs.CV↗

arXiv2025

Super-resolution femtosecond electron diffraction reveals electronic and nuclear dynamics at conical intersections

Conical intersections play a pivotal role in excited-state quantum dynamics. Capturing transient molecular structures near conical intersections remains challenging due to the rapid timescales and subtle structural changes involved. We overcome this by combining the enhanced temporal resolution of mega-electron-volt ultrafast electron diffraction with a super-resolution real-space inversion algorithm, enabling visualization of nuclear and electronic motions at conical intersections with sub-angstrom resolution, surpassing the diffraction limit. We apply this technique to the textbook example of the ring-opening reaction of 1,3-cyclohexadiene, which proceeds through two conical intersections within 100 femtoseconds. The super-resolved transient structures near conical intersections reveal a C-C bond length difference of less than 0.4 angstrom and an approximately 30-femtosecond traversal time of the nuclear wave packet between them. These findings establish super-resolution ultrafast scattering as a transformative tool for uncovering quantum dynamics in molecules and open new avenues for studying light-matter interactions at the most fundamental level.

physics.chem-ph↗

arXiv2025

Non-Hermitian entanglement dip from scaling-induced exceptional criticality

It is well established that the entanglement entropy of a critical system generally scales logarithmically with system size. Yet, in this work, we report a new class of non-Hermitian critical transitions that exhibit dramatic divergent dips in their entanglement entropy scaling, strongly violating conventional logarithmic behavior. Dubbed scaling-induced exceptional criticality (SIEC), it transcends existing non-Hermitian mechanisms such as exceptional bound states and non-Hermitian skin effect (NHSE)-induced gap closures, which are nevertheless still governed by logarithmic entanglement scaling. Key to SIEC is its strongly scale-dependent spectrum, where eigenbands exhibit an exceptional crossing only at a particular system size. As such, the critical behavior is dominated by how the generalized Brillouin zone (GBZ) sweeps through the exceptional crossing with increasing system size, and not just by the gap closure per se. We provide a general approach for constructing SIEC systems based on the non-local competition between heterogeneous NHSE pumping directions, and show how a scale-dependent GBZ can be analytically derived to excellent accuracy. Beyond 1D free fermions, SIEC is expected to occur more prevalently in higher-dimensional or even interacting systems, where antagonistic NHSE channels generically proliferate. SIEC-induced entanglement dips generalize straightforwardly to kinks in other entanglement measures such as Renyi entropy, and serve as spectacular demonstrations of how algebraic and geometric singularities in complex band structures manifest in quantum information.

quant-ph↗

arXiv2025

Probing valence electron and hydrogen dynamics using charge-pair imaging with ultrafast electron diffraction

A key challenge in ultrafast science has been to directly track the coupled motions of electrons and nuclei in real-space and real-time. This study presents a significant step towards this goal by demonstrating the feasibility of time-resolved real-space tracking of valence electron and hydrogen dynamics during the photodissociation of ammonia (NH3) using MeV ultrafast electron diffraction. It is demonstrated that the enhanced temporal resolution, in conjunction with the analysis of the charge-pair distribution function, enables the disentanglement of the correlated motion of valence electrons and hydrogens in photoexcited ammonia molecule. The methodology employed in this study, which utilizes the charge-pair distribution function from ultrafast electron scattering to retrieve intertwined electron and nucleus dynamics, may open up new opportunities in the study of quantum dynamics for a wide range of molecules.

physics.chem-ph↗

arXiv2025

Imitater: An Efficient Shared Mempool Protocol with Application to Byzantine Fault Tolerance

Byzantine Fault Tolerant (BFT) consensus, a cornerstone of blockchain technology, has seen significant advancements. While existing BFT protocols ensure security guarantees, they often suffer from efficiency challenges, particularly under conditions of network instability or malicious exploitation of system mechanisms. We propose a novel Shared Mempool (SMP) protocol, named Imitater, which can be seamlessly integrated into BFT protocols. By chaining microblocks and applying coding techniques, Imitater efficiently achieves \emph{totality} and \emph{availability}. Furthermore, a BFT protocol augmented with Imitater ensures \emph{order preservation} of client transactions while mitigating the risks of \emph{over-distribution} and \emph{unbalanced workload}. In the experiment, we integrate Imitater into the HotStuff protocol, resulting in Imitater-HS. The performance of Imitater-HS is validated in a system with up to 256 nodes. Experimental results demonstrate the efficiency of our approach: Imitater-HS achieves higher throughput and lower latency in the presence of faulty nodes compared to Stratus-HS, the state-of-the-art protocol. Notably, the throughput improvement increases with the number of faulty nodes.

cs.DC↗

arXiv2025

Development of portable cosmic-ray muon detector array for muography

As the multidisciplinary applications of cosmic-ray muons expand to large-scale and wide-area scenarios, the construction of cosmic-ray muon detector arrays has become a key solution to overcome the hardware limitations of individual detector. For muography, the array-based detector design enables fast-scanning of large target objects, allowing for rapid identification of density variation regions, which can improve the efficiency of tomography. This paper integrates scintillator detector technology with Internet of things (IoT) technology, proposing a novel array networking model for nationwide deployment. The model enables long-distance data collection and distribution, laying the foundation for future multidisciplinary applications such as muography and other fields.

physics.ins-det↗

arXiv2025

Design of a CsI(Tl) Calorimeter for Muonium-to-Antimuonium Conversion Experiment

The Muonium-to-Antimuonium Conversion Experiment (MACE) is proposed to search for charged lepton flavor violation and increase the sensitivity by more than two orders of magnitude compared to the MACS experiment at PSI in 1999. A clear signature of this conversion is the positron produced from antimuonium decay. This paper presents a near-$4π$-coverage calorimeter designed for MACE, which can provide an energy resolution of 10.8% at 511 keV, and a signal efficiency of 78.3% for annihilation $γ$-ray events. Detailed Monte-Carlo simulations using MACE offline software based on Geant4 are performed for geometry optimization, coincidence system design, background estimation, and benchmark detector validation.

physics.ins-det↗

arXiv2025

Imaging the photochemical dynamics of cyclobutanone with MeV ultrafast electron diffraction

We study the photoinduced chemical dynamics of cyclobutanone upon excitation at 200 nm to the 3s Rydberg state using MeV ultrafast electron diffraction (UED). We observe both the elastic scattering signal, which contains information about the structural dynamics, and the inelastic scattering signal, which encodes information about the electronic state. Our results suggest a sub-picosecond timescale for the photodissociation dynamics, and an excited state lifetime of about 230 femtoseconds. The dissociation is found to be dominated by the C3 channel where cyclopropane and CO are produced. The branching ratio of the C3 channel to the C2 channel where ethene and ketene are produced, is estimated to be approximately 5:3. Our data suggest that the C3 and C2 channels account for approximately 80% of the photoproducts, with the remaining 20% exhibiting ring-opened structures. It is found that the timescale associated with the dissociation process in the C2 channel is shorter compared to that in the C3 channel. Leveraging the enhanced temporal resolution of MeV UED, our results provide a real-time mapping of the nuclear wavepacket dynamics, capturing the complete photochemical dynamics from S2 minimum through the S1/S0 conical intersection, and finally to the dissociation. Our experimental results provide new insights into the Norrish Type I reaction and can be used to benchmark non-adiabatic dynamics simulations.

physics.chem-ph↗