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Researcher profile

Yijie Wang

Yijie Wang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioncond-mat.mes-hallnucl-exphysics.ins-detComputation and Languagecond-mat.str-eleess.SYMachine Learningmath.OCNeural and Evolutionary Computingquant-phSocial and Information NetworksSystems and Control

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Published work

13 published item(s)

preprint2026arXiv

CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence

Multimodal Large Language Models (MLLMs) have significantly advanced document understanding, yet current Doc-VQA evaluations score only the final answer and leave the supporting evidence unchecked. This answer-only approach masks a critical failure mode: a model can land on the correct answer while grounding it in the wrong passage -- a critical risk in high-stakes domains like law, finance, and medicine, where every conclusion must be traceable to a specific source region. To address this, we introduce CiteVQA, a benchmark that requires models to return element-level bounding-box citations alongside each answer, evaluating both jointly. CiteVQA comprises 1,897 questions across 711 PDFs spanning seven domains and two languages, averaging 40.6 pages per document. To ensure fidelity and scalability, the ground-truth citations are generated by an automated pipeline-which identifies crucial evidence via masking ablation-and are subsequently validated through expert review. At the core of our evaluation is Strict Attributed Accuracy (SAA), which credits a prediction only when the answer and the cited region are both correct. Auditing 20 MLLMs reveals a pervasive Attribution Hallucination: models frequently produce the right answer while citing the wrong region. The strongest system (Gemini-3.1-Pro-Preview) achieves an SAA of only 76.0, and the strongest open-source MLLM reaches just 22.5. Ultimately, towards trustworthy document intelligence, CiteVQA exposes a reliability gap that answer-only evaluations overlook, providing the instrumentation needed to close it. Our repository is available at https://github.com/opendatalab/CiteVQA.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

From inconsistency to decision: explainable operation and maintenance of battery energy storage systems

Battery Energy Storage Systems (BESSs) are increasingly critical to power-system stability, yet their operation and maintenance remain dominated by reactive, expert-dependent diagnostics. While cell-level inconsistencies provide early warning signals of degradation and safety risks, the lack of scalable and interpretable decision-support frameworks prevents these signals from being effectively translated into operational actions. Here we introduce an inconsistency-driven operation and maintenance paradigm for large-scale BESSs that systematically transforms routine monitoring data into explainable, decision-oriented guidance. The proposed framework integrates multi-dimensional inconsistency evaluation with large language model-based semantic reasoning to bridge the gap between quantitative diagnostics and practical maintenance decisions. Using eight months of field data from an in-service battery system comprising 3,564 cells, we demonstrate how electrical, thermal, and aging-related inconsistencies can be distilled into structured operational records and converted into actionable maintenance insights through a multi-agent framework. The proposed approach enables accurate and explainable responses to real-world operation and maintenance queries, reducing response time and operational cost by over 80% compared with conventional expert-driven practices. These results establish a scalable pathway for intelligent operation and maintenance of battery energy storage systems, with direct implications for reliability, safety, and cost-effective integration of energy storage into modern power systems.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Programmable Quantum Anomalous Hall Insulator in Twisted Crystalline Flatbands

The isospin flavors in condensed matters can be continuously broken, forming various symmetry-broken quantum states. In moiré crystals, the competition between different isospin configurations can be effectively tuned by the twist angles and staciking orders. Here we report twisted double rhombohedral-trilayer-gaphene as a new twisted crystalline flatbands system showing rich moiré dependent topological phenomena. In devices with small twist angles, programmable Chern insulators with Chern number C = 3 at integer moiré filling v = 1 have been observed. We have further revealed an exotic hidden order which can quench the Chern insulator as well as multiple first-order transitions between different symmetry-broken phases. Interestly, in the device with a slightly larger twist angle, multiple Chern insulators with C = 1 at fractional moiré fillings including v = 1/4, 1/3 and 1/2 have been observed, whereas the Chern insulator at v = 1 is abscent. Our study demonstrated the twisted flatbands form rhombohedral-multilayer-graphene as a new platform to study tunable high Chern insulators as well as new devices for quantum storage and computation.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Decoupling Constraint from Two Direction in Evolutionary Constrained Multi-objective Optimization

Real-world Constrained Multi-objective Optimization Problems (CMOPs) often contain multiple constraints, and understanding and utilizing the coupling between these constraints is crucial for solving CMOPs. However, existing Constrained Multi-objective Evolutionary Algorithms (CMOEAs) typically ignore these couplings and treat all constraints as a single aggregate, which lacks interpretability regarding the specific geometric roles of constraints. To address this limitation, we first analyze how different constraints interact and show that the final Constrained Pareto Front (CPF) depends not only on the Pareto fronts of individual constraints but also on the boundaries of infeasible regions. This insight implies that CMOPs with different coupling types must be solved from different search directions. Accordingly, we propose a novel algorithm named Decoupling Constraint from Two Directions (DCF2D). This method periodically detects constraint couplings and spawns an auxiliary population for each relevant constraint with an appropriate search direction. Extensive experiments on seven challenging CMOP benchmark suites and on a collection of real-world CMOPs demonstrate that DCF2D outperforms five state-of-the-art CMOEAs, including existing decoupling-based methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

An FPGA-based Trigger System for CSHINE

A trigger system of general function is designed using the commercial module CAEN V2495 for heavy ion nuclear reaction experiment at Fermi energies. The system has been applied and verified on CSHINE (Compact Spectrometer for Heavy IoN Experiment). Based on the field programmable logic gate array (FPGA) technology of command register access and remote computer control operation, trigger functions can be flexibly configured according to the experimental physical goals. Using the trigger system on CSHINE, we carried out the beam experiment of 25 MeV/u $ ^{86}{\rm Kr}+ ^{124}{\rm Sn}$ on the Radioactive Ion Beam Line 1 in Lanzhou (RIBLL1), China. The online results demonstrate that the trigger system works normally and correctly. The system can be extended to other experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound

Precise and rapid categorization of images in the B-scan ultrasound modality is vital for diagnosing ocular diseases. Nevertheless, distinguishing various diseases in ultrasound still challenges experienced ophthalmologists. Thus a novel contrastive disentangled network (CDNet) is developed in this work, aiming to tackle the fine-grained image categorization (FGIC) challenges of ocular abnormalities in ultrasound images, including intraocular tumor (IOT), retinal detachment (RD), posterior scleral staphyloma (PSS), and vitreous hemorrhage (VH). Three essential components of CDNet are the weakly-supervised lesion localization module (WSLL), contrastive multi-zoom (CMZ) strategy, and hyperspherical contrastive disentangled loss (HCD-Loss), respectively. These components facilitate feature disentanglement for fine-grained recognition in both the input and output aspects. The proposed CDNet is validated on our ZJU Ocular Ultrasound Dataset (ZJUOUSD), consisting of 5213 samples. Furthermore, the generalization ability of CDNet is validated on two public and widely-used chest X-ray FGIC benchmarks. Quantitative and qualitative results demonstrate the efficacy of our proposed CDNet, which achieves state-of-the-art performance in the FGIC task. Code is available at: https://github.com/ZeroOneGame/CDNet-for-OUS-FGIC .

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Distributionally Robust Observable Strategic Queues

This paper presents an extension of Naor's analysis on the join-or-balk problem in observable M/M/1 queues. While all other Markovian assumptions still hold, we explore this problem assuming uncertain arrival rates under the distributionally robust settings. We first study the problem with the classical moment ambiguity set, where the support, mean, and mean-absolute deviation of the underlying distribution are known. Next, we extend the model to the data-driven setting, where decision makers only have access to a finite set of samples. We develop three optimal joining threshold strategies from the perspective of an individual customer, a social optimizer, and a revenue maximizer, such that their respective worst-case expected benefit rates are maximized. Finally, we compare our findings with Naor's original results and the traditional sample average approximation scheme.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

RayMVSNet: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo

Learning-based multi-view stereo (MVS) has by far centered around 3D convolution on cost volumes. Due to the high computation and memory consumption of 3D CNN, the resolution of output depth is often considerably limited. Different from most existing works dedicated to adaptive refinement of cost volumes, we opt to directly optimize the depth value along each camera ray, mimicking the range (depth) finding of a laser scanner. This reduces the MVS problem to ray-based depth optimization which is much more light-weight than full cost volume optimization. In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth. This sequential modeling, conducted based on transformer features, essentially learns the epipolar line search in traditional multi-view stereo. We also devise a multi-task learning for better optimization convergence and depth accuracy. Our method ranks top on both the DTU and the Tanks \& Temples datasets over all previous learning-based methods, achieving overall reconstruction score of 0.33mm on DTU and f-score of 59.48% on Tanks & Temples.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Reconstruction of Fission Events in Heavy Ion Reactions with CSHINE

We report the reconstruction method of the fast fission events in 25 MeV/u $^{86}$Kr +$^{208}$Pb reactions at the Compact Spectrometer for Heavy IoN Experiment (CSHINE). The fission fragments are measured by three large-area parallel plate avalanche counters, which can deliver the position and the arrival timing information of the fragments. The start timing information is given by the radio frequency of the cyclotron. Using the velocities of the two fission fragments, the fission events are reconstructed. The broadening of both the velocity distribution and the azimuthal difference of the fission fragments decrease with the folding angle, in accordance with the picture that fast fission occurs. The anisotropic angular distribution of the fission axis also reveals consistently the dynamic feature the fission events.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Track Recognition for the $ΔE-E$ Telescopes with Silicon Strip Detectors

For the high granularity and high energy resolution, Silicon Strip Detector (SSD) is widely applied in assembling telescopes to measure the charged particles in heavy ion reactions. In this paper, we present a novel method to achieve track recognition in the SSD telescopes of the Compact Spectrometer for Heavy Ion Experiment (CSHINE). Each telescope consists of a single-sided silicon strip detector (SSSSD) and a double-sided silicon strip detector (DSSSD) backed by $3 \times 3$ CsI(Tl) crystals. Detector calibration and track reconstruction are implemented. Special decoding algorithm is developed for the multi-track recognition procedure to deal with the multi-hit effect convoluted by charge sharing and the missing signals with certain probability. It is demonstrated that the track recognition efficiency of the method is approximately 90\% and 80\% for the DSSSD-CsI and SSSSD-DSSSD events, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Dynamic Transport Characteristics of Side-Coupled Double Quantum-Impurity Systems

A systematic study is made on the time-dependent dynamic transport characteristics of the side-coupled double quantum-impurity system based on the hierarchical equations of motion. It is found that the transport current behaves like a single quantum dot when the coupling strength is low during tunneling or coulomb coupling. The dynamic current oscillates due to the temporal coherence of the electron tunneling device only when the tunneling transition is coupled. The oscillation frequency of the transport current is related to the step voltage applied by the lead, while the $T$, e-e interaction $U$ and the bandwidth $W$ have little influence. The amplitude of the current oscillation exists in positive correlation with $W$ and negative correlation with $U$. With the increase in coupling $t_{12}$ between impurities, the ground state of the system changes from a Kondo singlet of one impurity to a spin-singlet of two impurities. Moreover, lowering the temperature could promote the Kondo effect to intensify the oscillation of the dynamic current. When only the coulomb transition is coupled, it is found that the two split-off Hubbard peaks move upward and have different interference effects on the Kondo peak at the Fermi surface with the increase in $U_{12}$, from the dynamics point of view.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Outlier Detection Ensemble with Embedded Feature Selection

Feature selection places an important role in improving the performance of outlier detection, especially for noisy data. Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance. In this paper, we propose an outlier detection ensemble framework with embedded feature selection (ODEFS), to address this issue. Specifically, for each random sub-sampling based learning component, ODEFS unifies feature selection and outlier detection into a pairwise ranking formulation to learn feature subsets that are tailored for the outlier detection method. Moreover, we adopt the thresholded self-paced learning to simultaneously optimize feature selection and example selection, which is helpful to improve the reliability of the training set. After that, we design an alternate algorithm with proved convergence to solve the resultant optimization problem. In addition, we analyze the generalization error bound of the proposed framework, which provides theoretical guarantee on the method and insightful practical guidance. Comprehensive experimental results on 12 real-world datasets from diverse domains validate the superiority of the proposed ODEFS.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Predictability of real temporal networks

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function. Empirical evidences have shown that the temporal nature of links in many real-world networks is not random. Nonetheless, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here we propose an entropy-rate based framework, based on combined topological-temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological-temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly, we find that for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension the combined topological-temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity of incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes.

0 citations0 reviewsNo code linkedOpen access