arXiv subjects

Yibo Wang

Publications and source records attributed to Yibo Wang.

At least 55 records · Page 3Linked to original sources

arXiv2026

Modulation of the Nernst Thermoelectrics by Regulating the Anomalous Hall and Nernst Angles

The large anomalous Nernst effect in magnetic Weyl semimetals is one of the most intriguing transport phenomena, which draws significant attention for its potential applications in topological thermoelectrics. Despite frequent reports of substantial anomalous Nernst conductivity (ANC), methods to optimize Nernst thermoelectrics remain limited. Our research reveals that the magnitude of the ANC is directly related to the sum of the anomalous Nernst and Hall angles. While the sign of the anomalous Hall angle is relatively stable in a certain material, the sign of the anomalous Nernst angle can be intrinsically tuned. Therefore, the ANC can be effectively optimized by regulating these angles to work in concert. This finding is verified by experimental modulation from iron-doped magnetic topological material Co3Sn2S2. Additionally, we observed a robust TlnT scaling law of the ANC over the temperature range of 40 to 140 K in all studied samples, suggesting an intrinsic origin of the ANC. Considering the common opposite sign of the anomalous Nernst and Hall angles in many magnetic topological materials, our research offers an applicable scheme for optimizing the Nernst thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2026

TabClean: Reusable LLM-Synthesized Programs for Tabular Data Cleaning

Reliable analytics and machine-learning pipelines depend on clean tabular data, yet production tables often contain missing values, typographical errors, inconsistent formats, violated dependencies, unit mismatches, and ambiguous categorical values. Existing cleaning systems make different trade-offs. Constraint-based systems need experts to specify rules. Learning-based systems need labels or retraining. Recent LLM-based cleaners reduce setup effort, but many call an LLM on rows, cells, or repeated workflow steps, so their cost grows with table size and with every recurring batch. We present TabClean, a model-training-free system that compiles LLM reasoning into reusable guarded cleaning programs. Given a dirty table and a small annotated development set, TabClean profiles table evidence, diagnoses repair mechanisms, synthesizes executable Python transformations, validates candidates with cell-level feedback, and commits the best program for reuse on schema-compatible batches. The key abstraction is an evidence-backed guarded repair clause. A deterministic transformation may fire only when its dirty pattern, target-negative condition, evidence support, and scope constraints are satisfied. Across six benchmarks, TabClean achieves high precision, improves F1 over representative rule-based, learning-based, and LLM-based baselines on five datasets, and substantially reduces recurring runtime and API cost by replacing repeated LLM inference with deterministic program execution.

cs.DB↗

arXiv2026

Discovery of a Featureless Tidal Disruption Event at z~1 with the Wide Field Survey Telescope

We report the discovery of tidal disruption event (TDE) WFST250820mmsw/AT2025wet by the 2.5-meter Wide Field Survey Telescope (WFST). It exhibits a blue nuclear flare throughout the observed evolution with a g-band peak magnitude ~22, which is about 3 magnitudes brighter than its host galaxy. A Keck/LRIS spectrum taken near the optical peak reveals a featureless blue continuum, with no discernible emission lines. However, its redshift can be accurately determined to be 1.037 by its host galaxy absorption lines. Blackbody fits to the multiband spectral energy distribution (SED) of AT2025wet yield a constant temperature of ~19,000K and a peak luminosity of (8.27 +0.92 -0.71)*10^44 erg s^-1 while actually the SED likely peaks at a much shorter wavelength than a 19,000K blackbody. The SED modeling of the host galaxy implies a stellar mass of ~10^11.2 M_odot and an estimated central black hole mass of ~10^8 M_odot, with no evidence of significant active galactic nucleus activity prior to the flare. All of these observations are well consistent with a featureless TDE scenario, making it the highest-redshift non-jetted TDE known to date. TDEs at such high redshift provide us a unique opportunity to explore the intrinsic SEDs of TDEs, particularly to test whether they peak in the extreme-UV regime, thereby addressing the missing energy puzzle and the origin of optical emission in TDEs. Ongoing surveys represented by WFST and the Legacy Survey of Space and Time (LSST) are expected to discover an increasing number of TDEs at higher redshifts, which will extend our census of SMBHs across redshift space and help unravel the mysteries of optical TDEs through direct probes of their UV emission.

astro-ph.HE↗

arXiv2026

An Obscured Tidal Disruption Event Uncovered by Its Mid- and Near-Infrared Dust Echo in a Star-Forming Galaxy

We present a comprehensive study of an infrared (IR) flare in the star-forming galaxy SDSS J010320.39+140152.5, which is selected from the sample of mid-IR (MIR) outbursts in nearby galaxies (MIRONG). Its MIR luminosity rose rapidly to a peak of $\sim5.4\times10^{43}$ \lum, maintained in the high state for about a year, and decreased continuously afterward. No optical variability was detected throughout the IR flare. Near-IR follow-up observations around the peak pinpointed the flare's location to spatially coincide with the galactic nucleus, with a $3σ$ upper limit of the offset of $\lesssim100$ pc. The IR spectral energy distribution (SED) of the flare is consistent with thermal emission of dust with temperatures of $\sim900$ K. Using a dust radiative transfer model, we inferred a peak UV luminosity of $\sim(4-10)\times10^{44}$ erg s$^{-1}$ and a total energy of $\sim(0.9-2)\times10^{52}$ ergs released. We ruled out the possibility of a supernova, and prefer that the IR flare originated from an obscured tidal disruption event (TDE) rather than a changing-look active galactic nucleus (AGN). This flare stands as one of the most compelling cases to date for the emerging class of dust-obscured TDEs in recent years. They are missed by optical surveys, partly accounting for the observed bias in TDE host galaxies, and represent a crucial, yet often overlooked, component for a complete understanding of the TDE population.

astro-ph.GA↗

arXiv2026

Why LLMs Hallucinate on Structured Knowledge: A Mechanistic Analysis of Reasoning over Linearized Representations

In many reasoning tasks, large language models (LLMs) rely on structured external knowledge, such as graphs and tables, which is typically linearized into sequential token representations. However, even when sufficient knowledge is available, LLMs can still produce hallucinated outputs, and the underlying mechanisms behind such failures remain poorly understood. We investigate these mechanisms and find that hallucinations arise from systematic internal dynamics rather than random noise. First, attention disproportionately concentrates toward shortcut-like structural cues rather than distributing across the full context. Second, feed-forward representations fail to ground the provided knowledge, causing the model to revert to parametric memory. Moreover, our results indicate that hallucination is consistently associated with failures in semantic grounding within feed-forward layers, while attention allocation exhibits greater task-dependent variability. Finally, we show that these mechanistic patterns generalize beyond single-hop graphs to multi-hop and tabular settings, enabling effective hallucination detection across structured knowledge formats.

cs.CL↗

arXiv2026

Data-Driven Predictive Control for Stochastic Descriptor Systems: An Innovation-Based Approach Handling Non-Causal Dependencies

Descriptor systems arise naturally in real-world applications governed by algebraic constraints, such as power networks, robotics and chemical processes. When a descriptor model contains a nontrivial nilpotent block, the discrete-time input--output map may be improper: the current output depends on future inputs and, in the stochastic case, on future noise terms. This letter proposes a data-driven predictive control framework for stochastic descriptor systems that handles these non-causal dependencies without explicitly identifying system matrices. The key idea is to split fast subsystem into noise-driven and input-driven parts, and then combine the former with the slow subsystem such that an innovation-driven Kalman filter can be appropriately defined to reformulate the stochastic descriptor system into an innovation-driven form. Based on this, a new behavioral system representation is derived, which inspires a data-driven innovation-based multi-step output predictor and a practical Inno-DeePC algorithm that enables data-driven predictive control design without known system matrices while implicitly handling algebraic constraints. Numerical experiments on a DC microgrid demonstrate the effectiveness of the proposed approach.

eess.SY↗

arXiv2026

Proton-electron coupled catalyst for ionomer-free electrochemical energy conversion

Efficient electrochemical energy devices are vital to renewable energy technology, yet coordinating the effective flow of electrons, ions, and chemical species continues to be a major challenge. In conventional proton-exchange membrane fuel cell (PEMFC) catalyst layers, proton and electron transport are supplied separately through percolating carbon networks and ionomer binders, rendering the catalyst largely passive and imposing fundamental trade-offs between reactant accessibility, ionic conductivity, and catalyst activity. Here, we introduce a one-dimensional proton-electron coupled catalyst (PECC) design, a transport-integrated electrocatalyst architecture in which the catalyst itself simultaneously supplies electronic and protonic transport to catalyst active sites. Using this PECC, PEMFCs can have an ionomer-free cathode catalyst layer (CCL), resulting in a dramatic 95% reduction in non-Fickian oxygen transport and boosting power density by 34% and 85% compared to traditional CCLs, with cathode Pt loadings of approximately 0.090 mg/cm^2 and 0.037 mg/cm^2, respectively. Meanwhile, PECC retains 65% of its mass activity and exhibits 32% higher power density than its ionomer-based CCL counterpart after 30k accelerated stressed test. Similar mass transport improvements have been observed in the electrochemical hydrogen pump (EHP) using PECC in the catalyst layers. Molecular dynamics simulations show the PECC's proton conductivity is 249% higher than Nafion. This PECC catalyst structure addresses core transport problems in PEMFCs, leading to almost 20% improvement in fuel efficiency and opens up new possibilities for designing high-performance, cost-effective electrochemical devices.

cond-mat.mtrl-sci↗

arXiv2026

TodyComm: Task-Oriented Dynamic Communication for Multi-Round LLM-based Multi-Agent System

Multi-round LLM-based multi-agent systems rely on effective communication structures to support collaboration across rounds. However, most existing methods employ a fixed communication topology during inference, which falls short in many realistic applications where the agents' roles may change \textit{across rounds} due to dynamic adversary, task progression, or time-varying constraints such as communication bandwidth. In this paper, we propose addressing this issue through TodyComm, a \textbf{t}ask-\textbf{o}riented \textbf{dy}namic \textbf{comm}unication algorithm. It produces behavior-driven collaboration topologies that adapt to the dynamics at each round, optimizing the utility for the task through policy gradient. Experiments on five benchmarks demonstrate that, under both dynamic adversarial settings and communication budget constraints, TodyComm achieves superior task performance while maintaining token efficiency, scalability, and strong generalizability across varying adversarial conditions.

cs.AI↗

arXiv2026

Position Paper: Denial-of-Service against Multi-Round Transaction Simulation

Transaction simulation is an important subsystem of block building, denial of whose service could lead to severe damage to the blockchain ecosystem and transaction delivery. Denial of block building has been studied, where the existing attack designs either target single-round builders, such as ConditionalExhaust (USENIX Security '24), or target two-round builders, by exploiting cross-round inconsistency, such as GhostTX (USENIX Security '24) and denial of sequencers (CCS '25). This work examines the denial-of-service security of multi-round transaction simulation under a new exploit: inter-transaction dependency that manifests in smart-contract state.

cs.CR↗

arXiv2026

On Min-Max Robust Data-Driven Predictive Control Considering Non-Unique Solutions to Behavioral Representation

Direct data-driven control methods are known to be vulnerable to uncertainty in stochastic systems. In this paper, we propose a new robust data-driven predictive control (DDPC) framework. By analyzing non-unique solutions to behavioral representation, we gain insight into the inherent lack of robustness in subspace predictive control (SPC) and its projection-based regularized variant. This stimulates us to construct an uncertainty set that captures all admissible output trajectories deviating from nominal subspace predictions, which results in a min-max robust formulation of DDPC that endows control sequences with robustness against such unknown deviations. We establish theoretical performance guarantees under bounded additive noise and develop tractable convex reformulations. To mitigate the conservatism of robust design, a feedback robust DDPC scheme is further proposed by incorporating an affine feedback policy. Simulation studies show that the proposed methods effectively robustify SPC and outperform the projection-based regularization.

math.OC↗

arXiv2026

Approximation of the invariant measure for stochastic Allen-Cahn equation via an explicit fully discrete scheme

In this paper we propose an explicit fully discrete scheme to numerically solve the stochastic Allen-Cahn equation. The spatial discretization is done by a spectral Galerkin method, followed by the temporal discretization by a tamed accelerated exponential Euler scheme. Based on the time-independent boundedness of moments of numerical solutions, we present the weak error analysis in an infinite time interval by using Malliavin calculus. This provides a way to numerically approximate the invariant measure for the stochastic Allen-Cahn equation.

math.NA↗

arXiv2026

Strong convergence of a fully discrete scheme for stochastic Burgers equation with fractional-type noise

We investigate numerical approximations for the stochastic Burgers equation driven by an additive cylindrical fractional Brownian motion with Hurst parameter $H \in (\frac{1}{2}, 1)$. To discretize the continuous problem in space, a spectral Galerkin method is employed, followed by the presentation of a nonlinear-tamed accelerated exponential Euler method to yield a fully discrete scheme. By showing the exponential integrability of the stochastic convolution of the fractional Brownian motion, we present the boundedness of moments of semi-discrete and full-discrete approximations. Building upon these results and the convergence of the fully discrete scheme in probability proved by a stopping time technique, we derive the strong convergence of the proposed scheme.

math.NA↗

arXiv2026

Strong convergence of an explicit full-discrete scheme for stochastic Burgers-Huxley equation

The strong convergence of an explicit full-discrete scheme is investigated for the stochastic Burgers-Huxley equation driven by additive space-time white noise, which possesses both Burgers-type and cubic nonlinearities. To discretize the continuous problem in space, we utilize a spectral Galerkin method. Subsequently, we introduce a nonlinear-tamed exponential integrator scheme, resulting in a fully discrete scheme. Within the framework of semigroup theory, this study provides precise estimations of the Sobolev regularity, $L^\infty$ regularity in space, and Hölder continuity in time for the mild solution, as well as for its semi-discrete and full-discrete approximations. Building upon these results, we establish moment boundedness for the numerical solution and obtain strong convergence rates in both spatial and temporal dimensions. A numerical example is presented to validate the theoretical findings.

math.NA↗

arXiv2026

Giant Domain-Wall Hall Magnetoresistance in Magnetic Topological Semimetal

Magnetic topological semimetals exhibit emerging magneto-transport behaviors, such as the giant anomalous Hall effect (AHE), chiral Hall effect, and antisymmetric magnetoresistance. In this work, based on the magnetic Weyl semimetal Co3Sn2S2, we report an intriguing longitudinal domain-wall Hall magnetoresistance in multi-domain states. According to a multi-domain model, a concise formula of this Hall magnetoresistance was revealed and verified experimentally. Rather than the real change of longitudinal resistance, this Hall magnetoresistance originates from an additional electric field distribution induced by the transverse giant AHE through the domain wall, which can be directly correlated to the Berry phase of topological Weyl bands. In Co3Sn2S2 devices, the Hall magnetoresistance was an order of magnitude larger than that of conventional magnetic materials, indicating its potential for multi-resistance-state modulation via the Weyl-enhanced AHE.

cond-mat.mes-hall↗

arXiv2026

Monocular Depth Estimation From the Perspective of Feature Restoration: A Diffusion Enhanced Depth Restoration Approach

Monocular Depth Estimation (MDE) is a fundamental computer vision task with important applications in 3D vision. The current mainstream MDE methods employ an encoder-decoder architecture with multi-level/scale feature processing. However, the limitations of the current architecture and the effects of different-level features on the prediction accuracy are not evaluated. In this paper, we first investigate the above problem and show that there is still substantial potential in the current framework if encoder features can be improved. Therefore, we propose to formulate the depth estimation problem from the feature restoration perspective, by treating pretrained encoder features as degraded features of an assumed ground truth feature that yields the ground truth depth map. Then an Invertible Transform-enhanced Indirect Diffusion (InvT-IndDiffusion) module is developed for feature restoration. Due to the absence of direct supervision on feature, only indirect supervision from the final sparse depth map is used. During the iterative procedure of diffusion, this results in feature deviations among steps. The proposed InvT-IndDiffusion solves this problem by using an invertible transform-based decoder under the bi-Lipschitz condition. Finally, a plug-and-play Auxiliary Viewpoint-based Low-level Feature Enhancement module (AV-LFE) is developed to enhance local details with auxiliary viewpoint when available. Experiments demonstrate that the proposed method achieves better performance than the state-of-the-art methods on various datasets. Specifically on the KITTI benchmark, compared with the baseline, the performance is improved by 4.09% and 37.77% under different training settings in terms of RMSE. Code is available at https://github.com/whitehb1/IID-RDepth.

cs.CV↗

arXiv2026

MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms

Unit test generation has become a promising and important Large Language Model (LLM) use case. However, existing evaluation benchmarks for LLM unit test generation focus on function- or class-level code (single-file) rather than more practical and challenging multi-file-level codebases. To address such a limitation, we propose MultiFileTest, a multi-file-level benchmark for unit test generation covering Python, Java, and JavaScript. MultiFileTest features 20 moderate-sized and high-quality projects per language. We evaluate eleven frontier LLMs on MultiFileTest, and the results show that most frontier LLMs tested exhibit moderate performance on MultiFileTest, highlighting the difficulty of MultiFileTest. We also conduct a thorough error analysis, which shows that even advanced LLMs, such as Gemini-3.0-Pro, exhibit basic yet critical errors, including executability and cascade errors. Motivated by this observation, we further evaluate all frontier LLMs under manual error-fixing and self-error-fixing scenarios to assess their potential when equipped with error-fixing mechanisms. Our code and dataset is available at \href{https://github.com/YiboWANG214/ProjectTest}{MultiFileTest}.

cs.SE↗

arXiv2026

Theoretical study of spin-dependent transport in WSe$_2$-based vertical spin valves

We theoretically investigate spin-dependent transport in a TMD-based vertical spin valve, taking WSe$_2$ as a representative example. Using effective Hamiltonians for the heterostructure and the Landauer formula, we derive the transmission and reflection coefficients within a transfer-matrix approach. The calculated magnetoresistance shows an oscillatory dependence on the WSe$_2$ thickness when the Fermi level is tuned near the valence-band maximum. The effects of gate voltage and exchange fields on the magnetoresistance are further analyzed. We also identify a Fabry-Pérot-like interference contribution to the magnetoresistance, which can enhance or even induce negative magnetoresistance in certain thickness regimes. Our results provide a qualitative understanding of the negative magnetoresistance observed in WSe$_2$-based spin valves and may offer useful insights for the design of tunable spintronic devices.

cond-mat.mes-hall↗

arXiv2026

MiroEval: Benchmarking Multimodal Deep Research Agents in Process and Outcome

Recent progress in deep research systems has been impressive, but evaluation still lags behind real user needs. Existing benchmarks predominantly assess final reports using fixed rubrics, failing to evaluate the underlying research process. Most also offer limited multimodal coverage, rely on synthetic tasks that do not reflect real-world query complexity, and cannot be refreshed as knowledge evolves. To address these gaps, we introduce MiroEval, a benchmark and evaluation framework for deep research systems. The benchmark comprises 100 tasks (70 text-only, 30 multimodal), all grounded in real user needs and constructed via a dual-path pipeline that supports periodic updates, enabling a live and evolving setting. The proposed evaluation suite assesses deep research systems along three complementary dimensions: adaptive synthesis quality evaluation with task-specific rubrics, agentic factuality verification via active retrieval and reasoning over both web sources and multimodal attachments, and process-centric evaluation audits how the system searches, reasons, and refines throughout its investigation. Evaluation across 13 systems yields three principal findings: the three evaluation dimensions capture complementary aspects of system capability, with each revealing distinct strengths and weaknesses across systems; process quality serves as a reliable predictor of overall outcome while revealing weaknesses invisible to output-level metrics; and multimodal tasks pose substantially greater challenges, with most systems declining by 3 to 10 points. The MiroThinker series achieves the most balanced performance, with MiroThinker-H1 ranking the highest overall in both settings. Human verification and robustness results confirm the reliability of the benchmark and evaluation framework. MiroEval provides a holistic diagnostic tool for the next generation of deep research agents.

cs.AI↗