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Xiaoming Zhang

Publications and source records attributed to Xiaoming Zhang.

At least 19 recordsLinked to original sources
arXiv2026

Reasoning through Evolution: Automatic Meta-path Discovery for LLM-based Fake News Detection

Propagation structures provide crucial evidence for fake news detection, yet existing approaches primarily rely on supervised GNN-based models, which require substantial labeled data and exhibit limited generalization. Although large language models (LLMs) exhibit strong reasoning capabilities, directly feeding them raw propagation graphs creates a significant modality mismatch and severe information overload, making structure-aware reasoning unreliable in zero-shot and few-shot settings. To bridge this gap, we propose MAGER, a multi-agent genetic evolution framework that automatically discovers meta-paths optimized for LLM reasoning. By compressing complex propagation graphs into informative subgraphs, the evolved meta-paths alleviate both information overload and modality mismatch, enabling frozen LLMs to perform structure-aware veracity reasoning. We further introduce a graph in-context learning strategy that retrieves semantically and structurally similar demonstrations to strengthen classification and reasoning. Extensive experiments show that MAGER substantially improves frozen LLMs as standalone fake news detectors in data-efficient settings. Our code is available at https://github.com/SenticNet/MAGER.

cs.AI↗
arXiv2026

Ensemble learning of pathology foundation models for precision oncology

Histopathology is essential for cancer diagnosis and treatment selection, and pathology foundation models learn visual representations from whole-slide images (WSIs). However, existing foundation models are trained on disparate datasets using varying strategies, leading to inconsistent performance and limited generalizability. Here, we introduce ELF (Ensemble Learning of Foundation models), which integrates five pretrained pathology foundation models into unified slide-level representations. Trained on 53,699 WSIs spanning 20 anatomical sites, ELF leverages ensemble learning to capture complementary information across models. ELF's slide-level architecture is designed for data-efficient downstream evaluation, including settings with limited data such as therapeutic response prediction. We evaluate ELF for disease classification, biomarker detection, as well as anticancer and immunotherapy response prediction across multiple cancer types. ELF achieves higher performance than the evaluated constituent and slide-level foundation models across the tested tasks, supporting further evaluation of ensemble learning for pathology applications in oncology.

cs.CV↗
arXiv2026

A Recentered-Domain Yau-Yau Filter for Target Tracking

The Yau-Yau filter reformulates nonlinear state estimation as probability-density propagation governed by the Forward Kolmogorov equation (FKE). Applying it to target tracking, however, requires efficient FKE approximation on a finite computational domain. This paper proposes a Recentered-Domain Yau-Yau Filter (RD-YYF), which solves the FKE within a fixed-size local window centered at the latest state estimate. This design concentrates numerical resolution near the dominant posterior density. Offline, physics-informed neural networks (PINNs) generate FKE solution snapshots, while principal component analysis constructs a low-dimensional representation of density evolution. A lightweight residual surrogate maps the initial-condition coefficients and domain center to the terminal-solution coefficients. Online, the pretrained surrogate predicts density evolution within the recentered window, followed by observation update and state estimation. Experiments on two geometrically constrained target-tracking examples show that RD-YYF achieves lower tracking errors than the extended Kalman filter (EKF) and particle filter (PF), while retaining efficient per-timestep inference. Ablation results indicate that domain recentering improves density approximation in high-probability regions and accelerates offline PINN convergence. These results demonstrate the potential of RD-YYF for efficient nonlinear target tracking

math.OC↗
arXiv2026

Benign Alone, Harmful Together: Exploiting Experience Composition in Self-Evolving LLM Agents

Self-evolving large language model agents improve their capabilities by distilling interaction trajectories into persistent experiences. Yet this mechanism introduces a new safety risk: experiences that are benign in isolation may jointly weaken an agent's safety boundary when accumulated and reused across sessions. Existing memory attacks typically require direct memory access or induce explicitly malicious records, limiting their stealthiness and applicability. We propose EvoBreak, an experience-conditioned sequential attack that operates through individually benign attack-stage tasks and induced experiences. EvoBreak repeatedly observes the experiences distilled by the victim, identifies uncovered target-relevant requirements, and adaptively acquires complementary experiences before reformulating the final query to activate them jointly. To support training, we introduce BreakGym, a structure-first synthesis pipeline that generates decomposable safety-sensitive targets with diverse dependency structures. EvoBreak is optimized using rejection-sampling supervised fine-tuning and Hint-guided GRPO. Experiments across self-evolving frameworks, victim backbones, pre-evolution domains, and safety benchmarks demonstrate that EvoBreak consistently outperforms existing attacks while maintaining high benignness. These results reveal benign experience composition as a persistent attack surface in self-evolving agents.

cs.CR↗
arXiv2026

IG-GAN: A Generative Adversarial Network for Aerodynamic Data Generation Based on Intrinsic Geometry

Existing generative models learn data distributions in flat Euclidean space. However, most data in our real world are manifolds embedded in high dimensional Euclidean space. Therefore, we propose an intrinsic-geometry-based generative adversarial network (IG-GAN) for data generation in the field of aerodynamics. The generator of the IG-GAN represents aerodynamic data as a piecewise smooth manifold constructed by Bézier surfaces, and the generator tries to learn the coefficients of each Bézier surface to further combine multiple Bézier surfaces into a smooth manifold automatically. The discriminator in the IG-GAN is a radial-basis-function based discriminator (RBF-D). Experimental results show that IG-GAN achieves lower predicted Mean Squared Errors (MSEs) than those of three baselines. Specifically, on the Burgers' equation dataset, IG-GAN reduces the predicted MSE of velocity u by 97.41% compared with state of the art SSL-Transformer. Additionally, on the ONERA M6 aircraft dataset, IG-GAN reduces the overall MSE of nine aerodynamic coefficients by 82.95% compared with SSL-Transformer.

cs.LG↗
arXiv2026

Topological Interstitial-Electron Conductor

Electron transport in solids arises primarily from two mechanisms: freely moving bulk electrons in metals, and gapless boundary states in topological insulators. Here, we report a new mechanism discovered in electrides. The topological interstitial-electron conductors (TIECs) proposed here are insulating electrides, but host interstitial electrons (IEs) distributed within crystal voids that traverse the entire unit cell. Without being tightly bound to real ions, the IEs generally experience low periodic potential barrier along the void channels. As a consequence, by applying a weak electric field sufficient to overcome the IE barriers but far below the system's dielectric breakdown threshold, one can expect that the TIECs would generate a persistent current contributed by the IEs and propagating along the void channels. We identify a family of realistic altermagnetic electrides, $A_5X_3$ ($A$ = Ca, Sr, Ba, Yb; $X$ = As, Sb), as TIECs. Remarkably, for $A_5X_3$ materials, the periodic potential barrier of the IEs along the void channels are ultralow, ranging from 13.43 to 67.96 meV per formula unit. This renders our proposal readily accessible to experimental verification. We further demonstrate that when the IEs of $A_5X_3$ undergo periodic motion along the channels, topological surface states will emerge at the boundary perpendicular to the channel direction, and continuously move across the bulk band gap. This pumping-like behaviour not only corroborates the topological nature of TIECs, but also rationalizes the finite-electric-field induced electronic transport within the band theory. Our findings expand the classification of electronic conductors, uncover unexplored transport properties of electrides, and establish a new material platform for low-power electronic devices.

cond-mat.mtrl-sci↗
arXiv2026

Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems

Large language model-based multi-agent systems have recently gained significant attention due to their potential for complex, collaborative, and intelligent problem-solving capabilities. Existing surveys typically categorize LLM-based multi-agent systems (LLM-MAS) according to their application domains or architectures, overlooking the central role of communication in coordinating agent behaviors and interactions. To address this gap, this paper presents a comprehensive survey of LLM-MAS from a communication-centric perspective. Specifically, we propose a structured framework that integrates system-level communication (architecture, goals, and protocols) with system internal communication (strategies, paradigms, objects, and content), enabling a detailed exploration of how agents interact, negotiate, and achieve collective intelligence. Through an extensive analysis of recent literature, we identify key components in multiple dimensions and summarize their strengths and limitations. In addition, we highlight current challenges, including communication efficiency, security vulnerabilities, inadequate benchmarking, and scalability issues, and outline promising future research directions. This review aims to help researchers and practitioners gain a clear understanding of the communication mechanisms in LLM-MAS, thereby facilitating the design and deployment of robust, scalable, and secure multi-agent systems.

cs.MA↗
arXiv2026

Evo-Attacker: Memory-Augmented Reinforcement Learning for Long-Horizon Tool Attacks on LLM-MAS

While Large Language Model-based Multi-Agent Systems (LLM-MAS) demonstrate remarkable capabilities in solving complex tasks by orchestrating specialized agents and external tools, the implicit trust in tool outputs creates a critical attack surface. Existing tool attacks are limited by domain specificity or fixed and static templates. To address these challenges, we propose Evo-Attacker, which formulates the tool attack as a self-evolving, memory-augmented reinforcement learning process. Evo-Attacker constructs a dynamic attack memory and employs deliberative reasoning to retrieve adversarial patterns and strategize modifying interventions at critical moments. Furthermore, we introduce Attack-Flow GRPO to optimize intermediate reasoning steps via terminal outcomes, addressing the long-horizon credit assignment challenge. Comprehensive experiments demonstrate that Evo-Attacker consistently outperforms baselines, highlighting its generalization and evolutionary capabilities and the urgent need for defensive tool safeguards.

cs.CR↗
arXiv2026

PropGuard: Safeguarding LLM-MAS via Propagation-Aware Exploration and Remediation

LLM-based multi-agent systems (LLM-MAS) have become a promising paradigm for solving complex tasks through role specialization, tool use, memory, and collaborative reasoning. However, these interactions create new security risks that malicious instructions injected through messages, tools, or memories can propagate across agents and rounds, causing system-level compromise. Existing defenses largely rely on local filtering or graph-based anomaly detection, but they often fail to trace fine-grained propagation paths or remediate contaminated states without disrupting benign collaboration. We propose PropGuard, a propagation-aware framework for safeguarding LLM-MAS. PropGuard constructs a dual-view spatio-temporal graph that combines response-centric risk estimation with full-state evidence preservation. Guided by these risk priors, a GE-GRPO trained inspector sequentially explores the full-state graph to recover compact suspicious propagation subgraphs. PropGuard then verifies harmful propagation through subgraph-aware diagnosis and applies source-guided remediation to correct upstream contamination and replay affected downstream interactions. Experiments across four communication architectures and five attack settings demonstrate that PropGuard consistently lowers attack success while maintaining high task-level defense success, achieving a favorable effectiveness--efficiency trade-off.

cs.LG↗
arXiv2026

Diffusion Reconstruction towards Generalizable Audio Deepfake Detection

Achieving robust generalization against unseen attacks remains a challenge in Audio Deepfake Detection (ADD), driven by the rapid evolution of generative models. To address this, we propose a framework centered on hard sample classification. The core idea is that a model capable of distinguishing challenging hard samples is inherently equipped to handle simpler cases effectively. We investigate multiple reconstruction paradigms, identifying the diffusion-based method as optimal for generating hard samples. Furthermore, we leverage multi-layer feature aggregation and introduce a Regularization-Assisted Contrastive Learning (RACL) objective to enhance generalizability. Experiments demonstrate the superior generalization of our approach, with our best model achieving a significant reduction in the average Equal Error Rate (EER) compared to the baseline.

cs.SD↗
arXiv2026

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning

Conventional Graph Contrastive Learning (GCL) on Text-Attributed Graphs (TAGs) relies on blind stochastic augmentations, inadvertently entangling task-relevant signals with noise. We propose SDM-SCR, a robust framework anchored in Approximate Orthogonal Decomposition. First, the Semantic Decoupling Module (SDM) leverages the instruction-following capability of Large Language Models (LLMs) to actively parse raw attributes into asymmetric, task-oriented signal and noise views. This shifts the paradigm from random perturbation to semantic-aware disentanglement. Subsequently, Semantic Consistency Regularization (SCR) exploits the spectral observation that semantic signals are topologically smooth while residual noise is high-frequency. SCR functions as a selective spectral filter, enforcing consistency only on the signal subspace to eliminate LLM hallucinations without over-smoothing. This ``Disentangle-then-Refine'' mechanism ensures rigorous signal purification. Extensive experiments demonstrate that SDM-SCR achieves SOTA performance in accuracy and efficiency.

cs.AI↗
arXiv2026

A Multimodal Foundation Model of Spatial Transcriptomics and Histology for Biological Discovery and Clinical Prediction

Spatial transcriptomics (ST) enables gene expression mapping within anatomical context but remains costly and low-throughput. Hematoxylin and eosin (H\&E) staining offers rich morphology yet lacks molecular resolution. We present \textbf{\ours} (\textbf{S}patial \textbf{T}ranscriptomics and hist\textbf{O}logy \textbf{R}epresentation \textbf{M}odel), a foundation model trained on 1.2 million spatially resolved transcriptomic profiles with matched histology across 18 organs. Using a hierarchical architecture integrating morphological features, gene expression, and spatial context, STORM bridges imaging and omics through robust molecular--morphological representations. STORM enhances spatial domain discovery, producing biologically coherent tissue maps, and outperforms existing methods in predicting spatial gene expression from H\&E images across 11 tumor types. The model is platform-agnostic, performing consistently across Visium, Xenium, Visium HD, and CosMx. Applied to 23 independent cohorts comprising 7,245 patients, STORM significantly improves immunotherapy response prediction and prognostication over established biomarkers, providing a scalable framework for spatially informed discovery and clinical precision medicine.

cs.AI↗
arXiv2026

BloClaw: An Omniscient, Multi-Modal Agentic Workspace for Next-Generation Scientific Discovery

The integration of Large Language Models (LLMs) into life sciences has catalyzed the development of "AI Scientists." However, translating these theoretical capabilities into deployment-ready research environments exposes profound infrastructural vulnerabilities. Current frameworks are bottlenecked by fragile JSON-based tool-calling protocols, easily disrupted execution sandboxes that lose graphical outputs, and rigid conversational interfaces inherently ill-suited for high-dimensional scientific data.We introduce BloClaw, a unified, multi-modal operating system designed for Artificial Intelligence for Science (AI4S). BloClaw reconstructs the Agent-Computer Interaction (ACI) paradigm through three architectural innovations: (1) An XML-Regex Dual-Track Routing Protocol that statistically eliminates serialization failures (0.2% error rate vs. 17.6% in JSON); (2) A Runtime State Interception Sandbox that utilizes Python monkey-patching to autonomously capture and compile dynamic data visualizations (Plotly/Matplotlib), circumventing browser CORS policies; and (3) A State-Driven Dynamic Viewport UI that morphs seamlessly between a minimalist command deck and an interactive spatial rendering engine. We comprehensively benchmark BloClaw across cheminformatics (RDKit), de novo 3D protein folding via ESMFold, molecular docking, and autonomous Retrieval-Augmented Generation (RAG), establishing a highly robust, self-evolving paradigm for computational research assistants. The open-source repository is available at https://github.com/qinheming/BloClaw.

cs.AI↗
arXiv2026

TrinityDNA: A Bio-Inspired Foundational Model for Efficient Long-Sequence DNA Modeling

The modeling of genomic sequences presents unique challenges due to their length and structural complexity. Traditional sequence models struggle to capture long-range dependencies and biological features inherent in DNA. In this work, we propose TrinityDNA, a novel DNA foundational model designed to address these challenges. The model integrates biologically informed components, including Groove Fusion for capturing DNA's structural features and Gated Reverse Complement (GRC) to handle the inherent symmetry of DNA sequences. Additionally, we introduce a multi-scale attention mechanism that allows the model to attend to varying levels of sequence dependencies, and an evolutionary training strategy that progressively adapts the model to both prokaryotic and eukaryotic genomes. TrinityDNA provides a more accurate and efficient approach to genomic sequence modeling, offering significant improvements in gene function prediction, regulatory mechanism discovery, and other genomics applications. Our model bridges the gap between machine learning techniques and biological insights, paving the way for more effective analysis of genomic data. Additionally, we introduced a new DNA long-sequence CDS annotation benchmark to make evaluations more comprehensive and oriented toward practical applications.

cs.CE↗
arXiv2026

Geodesic Gradient Descent: A Generic and Learning-rate-free Optimizer on Objective Function-induced Manifolds

Euclidean gradient descent algorithms barely capture the geometry of objective function-induced hypersurfaces and risk driving update trajectories off the hypersurfaces. Riemannian gradient descent algorithms address these issues but fail to represent complex hypersurfaces via a single classic manifold. We propose geodesic gradient descent (GGD), a generic and learning-rate-free Riemannian gradient descent algorithm. At each iteration, GGD uses an n-dimensional sphere to approximate a local neighborhood on the objective function-induced hypersurface, adapting to arbitrarily complex geometries. A tangent vector derived from the Euclidean gradient is projected onto the sphere to form a geodesic, ensuring the update trajectory stays on the hypersurface. Parameter updates are performed using the endpoint of the geodesic. The maximum step size of the gradient in GGD is equal to a quarter of the arc length on the n-dimensional sphere, thus eliminating the need for a learning rate. Experimental results show that compared with the classic Adam algorithm, GGD achieves test MSE reductions ranging from 35.79% to 48.76% for fully connected networks on the Burgers' dataset, and cross-entropy loss reductions ranging from 3.14% to 11.59% for convolutional neural networks on the MNIST dataset.

cs.LG↗
arXiv2026

Studies of beauty hadron and non-prompt charm hadron production in pp collisions at $\sqrt{s}$=13 TeV within a transport model approach

In high-energy proton proton ($pp$) collisions at the LHC, non-prompt charm hadrons, originating from beauty hadron decays, provide a valuable probe for beauty quark dynamics, particularly at low transverse momentum where direct beauty measurements are challenging. We employ A Multi-Phase Transport Model (AMPT) of string melting version coupled with PYTHIA8 initial conditions to study the beauty hadron and non-prompt charm hadron productions in $pp$ collisions at $\sqrt{s} = 13$ TeV. In this work, the beauty quark mass during the generation stage has been increased to reproduce the measured $b\bar{b}$ cross section, and a beauty flavor specific coalescence parameter $r_{BM}^b$ is introduced to match LHCb measurements of beauty baryon to meson ratios. With these refinements, AMPT achieves a reasonable agreement with experimental data on beauty hadron yields and non-prompt charm hadron production from ALICE and LHCb. We present the transverse momentum and multiplicity dependence of non-prompt to prompt charm hadron ratios, providing new insights into the interplay between beauty quark production and hadronization process. We emphasize that the multiplicity dependence of the non-prompt to prompt charm hadron productions can be useful to constrain the flavor dependences of the coalescence dynamics. This work establishes a unified framework for future studies of heavy quark transport and collective flow behavior in small collision systems.

hep-ph↗
arXiv2026

Mixture of Disentangled Experts with Missing Modalities for Robust Multimodal Sentiment Analysis

Multimodal Sentiment Analysis (MSA) integrates multiple modalities to infer human sentiment, but real-world noise often leads to missing or corrupted data. However, existing feature-disentangled methods struggle to handle the internal variations of heterogeneous information under uncertain missingness, making it difficult to learn effective multimodal representations from degraded modalities. To address this issue, we propose DERL, a Disentangled Expert Representation Learning framework for robust MSA. Specifically, DERL employs hybrid experts to adaptively disentangle multimodal inputs into orthogonal private and shared representation spaces. A multi-level reconstruction strategy is further developed to provide collaborative supervision, enhancing both the expressiveness and robustness of the learned representations. Finally, the disentangled features act as modality experts with distinct roles to generate importance-aware fusion results. Extensive experiments on two MSA benchmarks demonstrate that DERL outperforms state-of-the-art methods under various missing-modality conditions. For instance, our method achieves improvements of 2.47% in Acc-2 and 2.25% in MAE on MOSI under intra-modal missingness.

cs.MM↗
arXiv2026

The R2Pub Telescopes for Surveying: An Overview and Performance Evaluation of the System

The R2Pub telescope, built by the Beijing Planetarium, is a 60 cm equatorial binocular telescope located at the Daocheng site of Yunnan Observatories in China, at an altitude of about 4700 m. This paper presents an overview of the R2Pub telescope system, including its design, instrumentation, and survey capabilities, and reports an initial evaluation of its system performance. R2Pub is a prime-focus binocular system, with each optical tube covering a field of view of approximately 18 square degrees. It is designed to detect a wide range of transient and variable sources in the local universe, such as variable stars, eclipsing binaries, supernovae, gamma-ray burst afterglows, tidal disruption events, active galactic nuclei, and other unknown transients. The observatory infrastructure, including the dome, equatorial mount, optical tubes, and associated subsystems, has been fully constructed and installed, and the system has entered the commissioning phase. Benefiting from the high-altitude location, good seeing conditions, and dark sky background at the Daocheng site, performance tests during commissioning show that the R2Pub system can reach a 5-sigma limiting magnitude of about 18.7 mag in the Pan-STARRS r' band with a 60 s exposure. Ongoing observations with R2Pub are expected to contribute to studies of variable and transient phenomena and to enhance public outreach in astronomy. The binocular design enables simultaneous dual-band observations, providing instantaneous color information for transient sources and improving the classification and physical characterization of their properties and evolution.

astro-ph.IM↗