arXiv subjects

Ziyu Li

Publications and source records attributed to Ziyu Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

Asking like Socrates: Socrates helps VLMs understand remote sensing images

Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (RS) tasks, we observe widespread pseudo reasoning: models narrate the process of reasoning rather than genuinely reason toward the correct answer based on visual evidence. We attribute this to the Glance Effect, where a single, coarse perception of large-scale RS imagery results in incomplete understanding and reasoning based on linguistic self-consistency instead of visual evidence. To address this, we propose RS-EoT (Remote Sensing Evidence-of-Thought), a language-driven, iterative visual evidence-seeking paradigm. To instill this paradigm, we propose SocraticAgent, a self-play multi-agent system that synthesizes reasoning traces via alternating cycles of reasoning and visual inspection. To enhance and generalize these patterns, we propose a two-stage progressive RL strategy: first, RL on fine-grained Grounding tasks to enhance RS-EoT capabilities, followed by RL on RS VQA to generalize to broader understanding scenarios. Experiments show RS-EoT achieves state-of-the-art performance on multiple RS VQA and grounding benchmarks. Analyses reveal clear iterative cycles of reasoning and evidence seeking, confirming RS-EoT mitigates the Glance Effect and enables genuine evidence-grounded reasoning. Our code, data, and models are available at https://geox-lab.github.io/Asking_like_Socrates

cs.CV↗

arXiv2026

Parallel Diffusion Solver via Residual Dirichlet Policy Optimization

Diffusion models (DMs) have achieved state-of-the-art generative performance but suffer from high sampling latency due to their sequential denoising nature. Existing solver-based acceleration methods often face significant image quality degradation under a low-latency budget, primarily due to accumulated truncation errors arising from the inability to capture high-curvature trajectory segments. In this paper, we propose the Ensemble Parallel Direction solver (dubbed as EPD-Solver), a novel ODE solver that mitigates these errors by incorporating multiple parallel gradient evaluations in each step. Motivated by the geometric insight that sampling trajectories are largely confined to a low-dimensional manifold, EPD-Solver leverages the Mean Value Theorem for vector-valued functions to approximate the integral solution more accurately. Importantly, since the additional gradient computations are independent, they can be fully parallelized, preserving low-latency sampling nature. We introduce a two-stage optimization framework. Initially, EPD-Solver optimizes a small set of learnable parameters via a distillation-based approach. We further propose a parameter-efficient Reinforcement Learning (RL) fine-tuning scheme that reformulates the solver as a stochastic Dirichlet policy. Unlike traditional methods that fine-tune the massive backbone, our RL approach operates strictly within the low-dimensional solver space, effectively mitigating reward hacking while enhancing performance in complex text-to-image (T2I) generation tasks. In addition, our method is flexible and can serve as a plugin (EPD-Plugin) to improve existing ODE samplers.

cs.CV↗

arXiv2026

High-fidelity 3D multi-slab diffusion MRI using Slab-shifting for Harmonized 3D Acquisition and Reconstruction with Profile Encoding Networks (SHARPEN)

Three-dimensional (3D) multi-slab imaging is a promising approach for high-resolution in vivo diffusion MRI (dMRI) due to its compatibility with short TR (1-2 s), providing optimal signal-to-noise ratio (SNR) efficiency. A major challenge, however, is slab boundary artifacts arising from non-ideal slab-selective RF excitation. Non-rectangular slab profiles reduce signal intensity at slab boundaries, while profile overlap across adjacent slabs introduces inter-slab crosstalk, where repeated excitation shortens the local TR and limits T1 recovery. To mitigate slab boundary artifacts without increasing scan time, we build on slab profile encoding and propose Slab-shifting for Harmonized 3D Acquisition and Reconstruction with Profile Encoding Networks (SHARPEN). For different diffusion directions, SHARPEN applies inter-volume field-of-view shifts along the slice direction to provide complementary slab profile encoding without prolonging acquisition. Slab profiles are estimated using a lightweight self-supervised neural network that exploits consistency across shifted acquisitions and known physical properties of slab profiles and diffusion images, and corrected images are reconstructed accordingly. SHARPEN was validated using simulated and prospectively acquired high-resolution in vivo data and demonstrates accurate slab profile estimation and robust boundary artifact correction, even in the presence of inter-volume motion. SHARPEN does not require high-quality reference training data and supports subject-specific training. Its efficient GPU-based implementation delivers faster and more accurate correction than NPEN, yielding slice-wise quantitative profiles that closely match those from reference 2D acquisitions. SHARPEN enables high-quality dMRI at 0.7 mm isotropic resolution on a 3T clinical scanner, highlighting its potential to advance submillimeter dMRI for neuroscience research.

physics.med-ph↗

arXiv2026

From Prompt to Graph: Comparing LLM-Based Information Extraction Strategies in Domain-Specific Ontology Development

Ontologies are essential for structuring domain knowledge, improving accessibility, sharing, and reuse. However, traditional ontology construction relies on manual annotation and conventional natural language processing (NLP) techniques, making the process labour-intensive and costly, especially in specialised fields like casting manufacturing. The rise of Large Language Models (LLMs) offers new possibilities for automating knowledge extraction. This study investigates three LLM-based approaches, including pre-trained LLM-driven method, in-context learning (ICL) method and fine-tuning method to extract terms and relations from domain-specific texts using limited data. We compare their performances and use the best-performing method to build a casting ontology that validated by domian expert.

cs.AI↗

arXiv2026

Research on Optimal Control Problem Based on Reinforcement Learning under Knightian Uncertainty

Considering that the decision-making environment faced by reinforcement learning (RL) agents is full of Knightian uncertainty, this paper describes the exploratory state dynamics equation in Knightian uncertainty to study the entropy-regularized relaxed stochastic control problem in a Knightian uncertainty environment. By employing stochastic analysis theory and the dynamic programming principle under nonlinear expectation, we derive the Hamilton-Jacobi-Bellman (HJB) equation and solve for the optimal policy that achieves a trade-off between exploration and exploitation. Subsequently, for the linear-quadratic (LQ) case, we examine the agent's optimal randomized feedback control under both state-dependent and state-independent reward scenarios, proving that the optimal randomized feedback control follows a Gaussian distribution in the LQ framework. Furthermore, we investigate how the degree of Knightian uncertainty affects the variance of the optimal feedback policy. Additionally, we establish the solvability equivalence between non-exploratory and exploratory LQ problems under Knightian uncertainty and analyze the associated exploration cost. Finally, we provide an LQ example and validate the theoretical findings through numerical simulations.

math.OC↗

arXiv2025

Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study

Cardiovascular diseases (CVDs) are the leading cause of global mortality, necessitating accessible and accurate diagnostic tools. While cardiac magnetic resonance imaging (CMR) provides gold-standard insights into cardiac structure and function, its clinical utility is limited by high cost and complexity. In contrast, electrocardiography (ECG) is inexpensive and widely available but lacks the granularity of CMR. We propose CardioNets, a deep learning framework that translates 12-lead ECG signals into CMR-level functional parameters and synthetic images, enabling scalable cardiac assessment. CardioNets integrates cross-modal contrastive learning and generative pretraining, aligning ECG with CMR-derived cardiac phenotypes and synthesizing high-resolution CMR images via a masked autoregressive model. Trained on 159,819 samples from five cohorts, including the UK Biobank (n=42,483) and MIMIC-IV-ECG (n=164,550), and externally validated on independent clinical datasets (n=3,767), CardioNets achieved strong performance across disease screening and phenotype estimation tasks. In the UK Biobank, it improved cardiac phenotype regression R2 by 24.8% and cardiomyopathy AUC by up to 39.3% over baseline models. In MIMIC, it increased AUC for pulmonary hypertension detection by 5.6%. Generated CMR images showed 36.6% higher SSIM and 8.7% higher PSNR than prior approaches. In a reader study, ECG-only CardioNets achieved 13.9% higher accuracy than human physicians using both ECG and real CMR. These results suggest that CardioNets offers a promising, low-cost alternative to CMR for large-scale CVD screening, particularly in resource-limited settings. Future efforts will focus on clinical deployment and regulatory validation of ECG-based synthetic imaging.

eess.IV↗

arXiv2025

Automated Penetration Testing with LLM Agents and Classical Planning

While penetration testing plays a vital role in cybersecurity, achieving fully automated, hands-off-the-keyboard execution remains a significant research challenge. In this paper, we introduce the "Planner-Executor-Perceptor (PEP)" design paradigm and use it to systematically review existing work and identify the key challenges in this area. We also evaluate existing penetration testing systems, with a particular focus on the use of Large Language Model (LLM) agents for this task. The results show that the out-of-the-box Claude Code and Sonnet 4.5 exhibit superior penetration capabilities observed to date, substantially outperforming all prior systems. However, a detailed analysis of their testing processes reveals specific strengths and limitations; notably, LLM agents struggle with maintaining coherent long-horizon plans, performing complex reasoning, and effectively utilizing specialized tools. These limitations significantly constrain its overall capability, efficiency, and stability. To address these limitations, we propose CHECKMATE, a framework that integrates enhanced classical planning with LLM agents, providing an external, structured "brain" that mitigates the inherent weaknesses of LLM agents. Our evaluation shows that CHECKMATE outperforms the state-of-the-art system (Claude Code) in penetration capability, improving benchmark success rates by over 20%. In addition, it delivers substantially greater stability, cutting both time and monetary costs by more than 50%.

cs.CR↗

arXiv2025

GraphFaaS: Serverless GNN Inference for Burst-Resilient, Real-Time Intrusion Detection

Provenance-based intrusion detection is an increasingly popular application of graphical machine learning in cybersecurity, where system activities are modeled as provenance graphs to capture causality and correlations among potentially malicious actions. Graph Neural Networks (GNNs) have demonstrated strong performance in this setting. However, traditional statically-provisioned GNN inference architectures fall short in meeting two crucial demands of intrusion detection: (1) maintaining consistently low detection latency, and (2) handling highly irregular and bursty workloads. To holistically address these challenges, we present GraphFaaS, a serverless architecture tailored for GNN-based intrusion detection. GraphFaaS leverages the elasticity and agility of serverless computing to dynamically scale the GNN inference pipeline. We parallelize and adapt GNN workflows to a serverless environment, ensuring that the system can respond in real time to fluctuating workloads. By decoupling compute resources from static provisioning, GraphFaaS delivers stable inference latency, which is critical for dependable intrusion detection and timely incident response in cybersecurity operations. Preliminary evaluation shows GraphFaaS reduces average detection latency by 85% and coefficient of variation (CV) by 64% compared to the baseline.

cs.CR↗

arXiv2025

anyECG-chat: A Generalist ECG-MLLM for Flexible ECG Input and Multi-Task Understanding

The advent of multimodal large language models (MLLMs) has sparked interest in their application to electrocardiogram (ECG) analysis. However, existing ECG-focused MLLMs primarily focus on report generation tasks, often limited to single 12-lead, short-duration (10s) ECG inputs, thereby underutilizing the potential of MLLMs. To this end, we aim to develop a MLLM for ECG analysis that supports a broader range of tasks and more flexible ECG inputs. However, existing ECG-QA datasets are often monotonous. To address this gap, we first constructed the anyECG dataset, which encompasses a wide variety of tasks, including report generation, abnormal waveform localization, and open-ended question answering. In addition to standard hospital ECGs, we introduced long-duration reduced-lead ECGs for home environments and multiple ECG comparison scenarios commonly encountered in clinical practice. Furthermore, we propose the anyECG-chat model, which supports dynamic-length ECG inputs and multiple ECG inputs. We trained the model using a three-stage curriculum training recipe with the anyECG dataset. A comprehensive evaluation was conducted, demonstrating that anyECG-chat is capable of supporting various practical application scenarios, including not only common report generation tasks but also abnormal waveform localization for long-duration reduced-lead ECGs in home environments and comprehensive comparative analysis of multiple ECGs. Our code and data are available at: https://github.com/CuCl-2/anyECG-chat.

cs.CL↗

arXiv2025

Neuromorphic Computing with Multi-Frequency Oscillations: A Bio-Inspired Approach to Artificial Intelligence

Despite remarkable capabilities, artificial neural networks exhibit limited flexible, generalizable intelligence. This limitation stems from their fundamental divergence from biological cognition that overlooks both neural regions' functional specialization and the temporal dynamics critical for coordinating these specialized systems. We propose a tripartite brain-inspired architecture comprising functionally specialized perceptual, auxiliary, and executive systems. Moreover, the integration of temporal dynamics through the simulation of multi-frequency neural oscillation and synaptic dynamic adaptation mechanisms enhances the architecture, thereby enabling more flexible and efficient artificial cognition. Initial evaluations demonstrate superior performance compared to state-of-the-art temporal processing approaches, with 2.18\% accuracy improvements while reducing required computation iterations by 48.44\%, and achieving higher correlation with human confidence patterns. Though currently demonstrated on visual processing tasks, this architecture establishes a theoretical foundation for brain-like intelligence across cognitive domains, potentially bridging the gap between artificial and biological intelligence.

cs.AI↗

arXiv2025

Benchmarking Non-perturbative Many-Body Approaches in the Exactly Solvable Hatsugai-Kohmoto Model

The accurate simulation of strongly correlated electron systems remains a central challenge in condensed matter physics, motivating the development of various non-perturbative many-body methods. Such methods are typically benchmarked against the numerical exact determinant quantum Monte Carlo (DQMC) in the Hubbard model; however, DQMC is limited by the fermionic sign problem and the uncertainties of numerical analytic continuation. To address these issues, we use the exactly solvable Hatsugai-Kohmoto (HK) model as a benchmarking platform to evaluate three many-body approximations: $GW$, $HGW$, and $SGW$. We compare the Green's functions, spectral functions, and response functions obtained from these approximations with the exact solutions. Our analysis shows that the $GW$ approximation, often considered insufficient for describing strong correlation, exhibits a previously unreported solution branch that accurately reproduces Mott physics in the HK model. In addition, using a covariant formalism, we find that $HGW$ provides an accurate description of charge response, while $SGW$ performs well for spin correlations. Overall, our work demonstrates that the HK model can effectively benchmark many-body approximations and helps refine the understanding of $GW$ methods in strongly correlated regimes.

cond-mat.str-el↗

arXiv2025

CEBench: A Benchmarking Toolkit for the Cost-Effectiveness of LLM Pipelines

Online Large Language Model (LLM) services such as ChatGPT and Claude 3 have transformed business operations and academic research by effortlessly enabling new opportunities. However, due to data-sharing restrictions, sectors such as healthcare and finance prefer to deploy local LLM applications using costly hardware resources. This scenario requires a balance between the effectiveness advantages of LLMs and significant financial burdens. Additionally, the rapid evolution of models increases the frequency and redundancy of benchmarking efforts. Existing benchmarking toolkits, which typically focus on effectiveness, often overlook economic considerations, making their findings less applicable to practical scenarios. To address these challenges, we introduce CEBench, an open-source toolkit specifically designed for multi-objective benchmarking that focuses on the critical trade-offs between expenditure and effectiveness required for LLM deployments. CEBench allows for easy modifications through configuration files, enabling stakeholders to effectively assess and optimize these trade-offs. This strategic capability supports crucial decision-making processes aimed at maximizing effectiveness while minimizing cost impacts. By streamlining the evaluation process and emphasizing cost-effectiveness, CEBench seeks to facilitate the development of economically viable AI solutions across various industries and research fields. The code and demonstration are available in https://github.com/amademicnoboday12/CEBench.

cs.PF↗

arXiv2025

A brain-inspired paradigm for scalable quantum vision

One of the fundamental tasks in machine learning is image classification, which serves as a key benchmark for validating algorithm performance and practical potential. However, effectively processing high-dimensional, detail-rich images, a capability that is inherent in biological vision, remains a persistent challenge. Inspired by the human brain's efficient ``Forest Before Trees'' cognition, we propose a novel Guiding Paradigm for image recognition, leveraging classical neural networks to analyze global low-frequency information and guide targeted quantum circuit towards critical high-frequency image regions. We present the Brain-Inspired Quantum Classifier (BIQC), implementing this paradigm via a complementarity architecture where a quantum pathway analyzes the localized intricate details identified by the classical pathway. Numerical simulations on diverse datasets, including high-resolution images, show the BIQC's superior accuracy and scalability compared to existing methods. This highlights the promise of brain-inspired, hybrid quantum-classical approach for developing next-generation visual systems.

quant-ph↗

arXiv2025

DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities

Accurate segmentation of brain images typically requires the integration of complementary information from multiple image modalities. However, clinical data for all modalities may not be available for every patient, creating a significant challenge. To address this, previous studies encode multiple modalities into a shared latent space. While somewhat effective, it remains suboptimal, as each modality contains distinct and valuable information. In this study, we propose DC-Seg (Disentangled Contrastive Learning for Segmentation), a new method that explicitly disentangles images into modality-invariant anatomical representation and modality-specific representation, by using anatomical contrastive learning and modality contrastive learning respectively. This solution improves the separation of anatomical and modality-specific features by considering the modality gaps, leading to more robust representations. Furthermore, we introduce a segmentation-based regularizer that enhances the model's robustness to missing modalities. Extensive experiments on the BraTS 2020 and a private white matter hyperintensity(WMH) segmentation dataset demonstrate that DC-Seg outperforms state-of-the-art methods in handling incomplete multimodal brain tumor segmentation tasks with varying missing modalities, while also demonstrate strong generalizability in WMH segmentation. The code is available at https://github.com/CuCl-2/DC-Seg.

cs.CV↗

arXiv2025

Phenotype-Guided Generative Model for High-Fidelity Cardiac MRI Synthesis: Advancing Pretraining and Clinical Applications

Cardiac Magnetic Resonance (CMR) imaging is a vital non-invasive tool for diagnosing heart diseases and evaluating cardiac health. However, the limited availability of large-scale, high-quality CMR datasets poses a major challenge to the effective application of artificial intelligence (AI) in this domain. Even the amount of unlabeled data and the health status it covers are difficult to meet the needs of model pretraining, which hinders the performance of AI models on downstream tasks. In this study, we present Cardiac Phenotype-Guided CMR Generation (CPGG), a novel approach for generating diverse CMR data that covers a wide spectrum of cardiac health status. The CPGG framework consists of two stages: in the first stage, a generative model is trained using cardiac phenotypes derived from CMR data; in the second stage, a masked autoregressive diffusion model, conditioned on these phenotypes, generates high-fidelity CMR cine sequences that capture both structural and functional features of the heart in a fine-grained manner. We synthesized a massive amount of CMR to expand the pretraining data. Experimental results show that CPGG generates high-quality synthetic CMR data, significantly improving performance on various downstream tasks, including diagnosis and cardiac phenotypes prediction. These gains are demonstrated across both public and private datasets, highlighting the effectiveness of our approach. Code is availabel at https://anonymous.4open.science/r/CPGG.

cs.CV↗

arXiv2025

Brain Inspired Adaptive Memory Dual-Net for Few-Shot Image Classification

Few-shot image classification has become a popular research topic for its wide application in real-world scenarios, however the problem of supervision collapse induced by single image-level annotation remains a major challenge. Existing methods aim to tackle this problem by locating and aligning relevant local features. However, the high intra-class variability in real-world images poses significant challenges in locating semantically relevant local regions under few-shot settings. Drawing inspiration from the human's complementary learning system, which excels at rapidly capturing and integrating semantic features from limited examples, we propose the generalization-optimized Systems Consolidation Adaptive Memory Dual-Network, SCAM-Net. This approach simulates the systems consolidation of complementary learning system with an adaptive memory module, which successfully addresses the difficulty of identifying meaningful features in few-shot scenarios. Specifically, we construct a Hippocampus-Neocortex dual-network that consolidates structured representation of each category, the structured representation is then stored and adaptively regulated following the generalization optimization principle in a long-term memory inside Neocortex. Extensive experiments on benchmark datasets show that the proposed model has achieved state-of-the-art performance.

cs.CV↗

arXiv2025

Magnetic properties and enhanced magnetocaloric effect in EuAl$_3$Si single crystals

This study presents systematic investigations into the growth and physical properties of EuAl$_3$Si single crystals, encompassing magnetic, transport, and thermodynamic analyses. EuAl$_3$Si undergoes a ferromagnetic transition at $T_C$ = 15 K. A significant reversible magnetocaloric effect was observed around $T_C$. Strikingly, with a small change of magnetic field 2 T, the maximum values of magnetic entropy change (13.4 J/kg K), refrigerant capacity (166 J/kg) and adiabatic temperature change (7.2 K) are found. These parameters respectively are 60%, 148% and 64% larger than those of the parent compound EuAl$_4$, and suggest EuAl$_3$Si as an excellent candidate for magnetic refrigeration applications near the temperature of liquid hydrogen. The possible mechanism for this enhancement is also discussed.

cond-mat.str-el↗

arXiv2025

Elastic properties of Cu-6wt\%Ag alloy wires for pulsed magnets investigated by ultrasonic techniques

Conductor materials with good mechanical performance as well as high electrical- and thermal-conductivities are particularly important to break through the current bottle-neck limit ($\sim 100$ T) of pulsed magnets. Here we perform systematic studies on the elastic properties of the Cu-6wt\%Ag alloy wires, a promising candidate material for the new-generation pulsed magnets, by employing two independent ultrasonic techniques - resonant ultrasound spectroscopy (RUS) and ultrasound pulse-echo experiments. Our RUS measurements manifest that the elastic properties of the Cu-6wt\%Ag alloy wires can be improved by an electroplastic drawing procedure as compared with the conventional cold drawing. We also take this chance to test the availability of our newly-built ultrasound pulse-echo facility at Wuhan National High Magnetic Field Center (WHMFC, China), and the results suggest that the elastic performance of the electroplastically-drawn Cu-6wt\%Ag alloy wire remains excellent without anomalous softening under extreme conditions, e.g., ultra-high magnetic field up to 50 T, nitrogen / helium cryogenic liquids.

cond-mat.mtrl-sci↗