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Rui Wang

Publications and source records attributed to Rui Wang.

At least 19 recordsLinked to original sources
arXiv2026

Unconventional SOC-activated Altermagnetic Spin Splitting and Half-Angle Locking of Spin Transport

Conventional altermagnetic spin splitting arises from staggered exchange and sublattice-dependent anisotropic hopping without requiring spin-orbit coupling (SOC). Here we derive a low-energy effective model showing that SOC and staggered exchange can jointly activate additional spin splitting on momentum planes where the nonrelativistic contribution vanishes. In hexagonal $α$-MnTe, this SOC-activated contribution produces d-wave spin splitting near $Γ$ with out-of-plane spin polarization. Its leading angular pattern counter-rotates by half the in-plane Néel-vector rotation angle. First-principles spin-transport calculations reveal a corresponding approximate half-angle locking between the Néel-vector orientation and the electric-field directions maximizing the signed longitudinal and transverse spin conductivities. Analytical transport calculations based on the effective model trace this locking to the same angular coupling between momentum and the Néel vector that governs the spin-splitting pattern. Our findings connect the microscopic mechanism of SOC-activated altermagnetic spin splitting to directional spin transport, providing a theoretical basis for probing Néel-vector orientation and controlling spin-current generation.

cond-mat.mtrl-sci↗
arXiv2026

JevAdvBench: A Benchmark and Black-Box Attacks for Reinforcement Learning for Calibrated Decisions Models

Typed decision models (TDMs), such as Jev, answer a question about an input with a probability, an option, or an ordinal level. Web services execute these decisions automatically above a confidence threshold. An attacker who writes part of the input can change a decision that the service then executes. Current adversarial benchmarks cannot measure this risk, because they judge generated text. A TDM writes no text, and its wrong decisions have a valid format. We present JevAdvBench, to our knowledge the first unified benchmark for the adversarial robustness of TDMs. It contains 862 questions with audited answers and twelve black-box attack methods that edit the input or the options by adaptive search. Because a changed decision is not always wrong, a review keeps only 5,586 answer-preserving variants, and we attack only correct decisions. The attacks flip 46.3% of Jev's correct decisions, while identical re-runs flip only 0.17%. A flawed argument without a command flips 77.2%, close to direct prompt injection at 92.0%. The same variants flip 34.0% to 51.7% of correct decisions on eight open TDMs. At a 0.8 threshold, 23.5% of the decisions that the service still executes are wrong. On Agentgateway, the Jev guardrail rejects a jailbreak request but accepts it with an inserted argument. Web services should therefore treat TDM inputs as untrusted and should not act on confidence alone. Project website: https://JevAdvBench.github.io/JevAdvBench/

cs.CR↗
arXiv2026

Hessian-Similar Optimization with Capped Participation: Lower Bounds and Virtual Masters

What is the round complexity of Hessian-similar optimization when each round can contact at most $s$ of $m$ clients? Prior rates permit a free master or full participation and do not characterize strict caps. We count every one-vector query and reply. A hidden-client chain gives a lower bound for zero-respecting local algorithms with adaptive cohorts and random stopping: on strongly convex quadratics, the required rounds are $Ω((m/s)\sqrt{\min\{δ_a,\barδ/\sqrt m\}/μ}\,\log(1/ε))$ for $s\leq(m-2)/2$ in the high-accuracy, high-dissimilarity regime. Here $μ$ is strong convexity, while $δ_a$ and $\barδ$ bound anchor and average Hessian variation. To remove dependence on a poor anchor, virtual-master SVRS uses independent estimator and proximal cohorts and averages the proximal replies. With Catalyst, it reaches relative gap $ε$ in $\tilde O(m/s+(\sqrt{m/s}+m^{3/4}/s)\sqrt{\barδ/μ})$ expected rounds on quadratics with strongly convex clients, independent of $δ_a$. Combining this method with charged analyses of existing algorithms matches the lower bound's polynomial dependence for $s\leq\sqrt m$ and $s=Ω(m)$; the worst gap is $O(m^{1/8})$. A held-out 12-seed synthetic study finds fewer rounds than tuned SVRP in nine of ten dip-family incomplete-cohort cells, versus two of 30 other cells. The lower bounds concern zero-respecting algorithms and do not cover unrestricted messages.

cs.LG↗
arXiv2026

A Statistical Framework for Understanding Causal Effects that Vary by Treatment Initiation Time in EHR-based Studies

Standard practice in electronic health record (EHR)-based studies evaluating the comparative effectiveness of bariatric surgery relative to no surgery is to estimate and report a constant treatment effect across calendar time. However, real-world treatment strategies can evolve, particularly when comparators include standard of care or surgical procedures where techniques may improve, making it clinically important to ascertain whether efficacy of bariatric surgery has changed over time. Efforts to determine whether treatment efficacy itself is evolving are complicated by changing patient populations, with potential covariate shift in key effect modifiers. Through a comprehensive analysis of EHR data from Kaiser Permanente comparing two bariatric surgical procedures to standard of care, we develop a statistical framework to estimate calendar time-specific average treatment effects and describe both how and why effects vary across treatment initiation time in EHR-based studies. Our approach projects doubly robust, time-specific treatment effect estimates onto candidate marginal structural models and uses model selection to best describe how effects vary by calendar time. We further introduce a novel summary metric, based on standardization, to quantify the degree to which observed effect changes may be attributed to covariate shift in the population receiving treatment, as opposed to changes in underlying treatment efficacy.

stat.ME↗
arXiv2026

Who pays for urban environment management? The role of the third-party service provider

The dominant contemporary paradigm of direct state-led urban environmental regulation entails significant risks, including high administrative burden, frequent conflicts with polluting entities, and economic vulnerabilities for those sources. To address these deficiencies, this paper integrates third-party service provider (TPSP) into China's existing management framework as a beneficial extension beyond conventional government intervention. Utilizing an evolutionary game approach, we compare two distinct institutional settings, a non-integration management mode and an integration management mode, and scrutinize the evolutionary dynamics across both two- and three-dimensional systems. To enhance system robustness, we embed a strategic behavior-based dynamic punishment mechanism that effectively eliminates the perturbing effects of varied penalty rules, thereby steering all stakeholders toward an ideal stable state. This comparative institutional analysis isolates the marginal contribution of third-party intermediation, specifies when outsourcing environmental services outperforms direct government enforcement, and reframes evolutionary game theory as a design device for institutional self-correction. Furthermore, a predefined variable importance analysis is conducted to highlight critical factors warranting attention in practical policy implementation. Extensive simulation experiments, carried out under diverse system-level and parameter-level conditions, validate the theoretical properties of our model. The findings offer concrete managerial insights to inform the formulation of more effective urban environmental supervision regulations.

math.OC↗
arXiv2026

Spark-Audio 1.0 Technical Report

This paper introduces Spark-Audio 1.0, a speech foundation model trained on domestically produced computing infrastructure. The model accepts text and speech inputs and generates text outputs. It supports multilingual (99 languages) and multi-dialect (202 dialects) speech recognition, speech translation, audio perception and understanding, and both text- and speech-based dialogue, marking a shift from merely ``hearing clearly'' to genuinely ``understanding'' speech. Architecturally, Spark-Audio 1.0 couples a 0.65B-parameter dense audio encoder with a 30B-A3B Mixture-of-Experts (MoE) large language model (LLM). The audio encoder is pretrained with representation-enhanced BEST-RQ and then refined through multi-task supervised training. The audio--LLM is trained through staged pretraining, followed by post-training that combines supervised fine-tuning with on-policy distillation. In total, the model is trained on 13 million hours of audio data together with large-scale text data. We evaluate Spark-Audio 1.0 on mainstream audio and text benchmarks, as well as on in-house test sets drawn from real-world application scenarios. Despite using substantially less audio training data than Qwen3.5-Omni-Flash, Spark-Audio 1.0 achieves comparable performance on most audio and text tasks, outperforms it in multilingual speech recognition, spoken dialogue, and real-world application scenarios, and approaches the performance of larger closed-source models. Nevertheless, there remains room for improvement in instruction following and complex audio reasoning. Finally, the successful training of Spark-Audio 1.0 on domestic hardware, including Huawei Ascend 910B and Hygon BW1000 chips, demonstrates the feasibility of a domestic technical pathway for building speech foundation models.

eess.AS↗
arXiv2026

Towards Adversarially Robust Multigraph Neural Networks for Financial Crime Analysis

Financial transaction networks are naturally represented as multigraphs, where repeated transactions between the same pair of accounts are modeled as distinct edges. Recently proposed multigraph neural network architectures leverage this structure explicitly to achieve a strong performance on anti-money laundering (AML) tasks. However, their robustness to adversaries that manipulate transaction structures to evade detection remains largely unexplored. We present the first scalable framework for structural attacks and adversarial training of multigraph neural networks using node-edge incidence matrices, adapting projected gradient descent and projected randomized block coordinate descent methods to optimize structural perturbations over such matrices. We further introduce a cycle-based regularizer that penalizes changes in the global counts of directed 2- and 3-cycles, providing a structural measure of attack plausibility. Experiments on synthetic pattern-detection and realistic AML datasets show that our learned attacks substantially outperform non-learnable perturbation baselines in reducing detection performance (PR-AUC), while adversarial training consistently improves robustness against these attacks. These results demonstrate that multigraph neural networks are vulnerable to learned structural attacks and that adversarial training provides an effective defense. Our code is available at https://github.com/denheijmans/robust_multignn.

cs.LG↗
arXiv2026

Detecting Misbehaviors of Large Vision-Language Models by Evidential Uncertainty Quantification

%Large vision-language models (LVLMs) have shown substantial advances in multimodal understanding and generation. However, when presented with incompetent or adversarial inputs, they frequently produce unreliable or even harmful content, such as fact hallucinations or dangerous instructions. This misalignment with human expectations, referred to as \emph{misbehaviors} of LVLMs, raises serious concerns for deployment in critical applications. These misbehaviors are found to stem from epistemic uncertainty, specifically either conflicting internal knowledge or the absence of supporting information. However, existing uncertainty quantification methods, which typically capture only overall epistemic uncertainty, have shown limited effectiveness in identifying such issues. To address this gap, we propose Evidential Uncertainty Quantification (EUQ), a fine-grained method that captures both information conflict and ignorance for effective detection of LVLM misbehaviors. In particular, we interpret features from the model output head as either supporting (positive) or opposing (negative) evidence. Leveraging Evidence Theory, we model and aggregate this evidence to quantify internal conflict and knowledge gaps within a single forward pass. %We extensively evaluate our method across four categories of misbehavior, including hallucinations, jailbreaks, adversarial vulnerabilities, and out-of-distribution (OOD) failures, using state-of-the-art LVLMs, and find that EUQ consistently outperforms strong baselines, showing that hallucinations correspond to high internal conflict and OOD failures to high ignorance. Furthermore, layer-wise evidential uncertainty dynamics analysis helps interpret the evolution of internal representations from a new perspective. The source code is available at https://github.com/HT86159/EUQ.

cs.LG↗
arXiv2026

Orientation-Dependent Universality of Spin-Transport Fluctuations in Two-Dimensional Altermagnets

Universal conductance fluctuations are conventionally organized by symmetry class, but crystalline anisotropy introduces additional structure in spin transport. Here we demonstrate orientation-dependent universal spin-transport fluctuations in disordered two-dimensional $d$-wave altermagnets without spin-orbit coupling. Rotating the altermagnetic orientation changes both longitudinal spin-conductance and transverse spin-Hall conductance fluctuations, while each conserved-spin sector remains in class AI. Altermagnetic orientation controls both the fluctuation amplitudes and the development of disorder-insensitive plateaus. These plateaus become flatter and less sensitive to the altermagnetic strength over the studied parameter ranges as the orientation approaches $45^\circ$, with representative fluctuation amplitudes of approximately $0.60e/(4π)$ for the longitudinal spin conductance and $0.40\,e/(4π)$ for the spin-Hall conductance. The longitudinal fluctuation amplitude correlates strongly with anisotropy of the clean-band Fermi-velocity tensor. Spin-sector covariances explain how the conserved channels combine into total fluctuations. Our results extend anisotropy-sensitive universal-fluctuation physics to conserved-spin altermagnetic transport and establish crystalline orientation as a control parameter within a fixed symmetry class.

cond-mat.mes-hall↗
arXiv2026

AdaExploreBench: Benchmarking LLM Agents for Proactive Exploration and Test-time Adaptation in Evolving Environments

Large Language Model (LLM) agents are increasingly deployed in unfamiliar and evolving environments, requiring them to proactively explore, understand, and adapt to environmental dynamics. However, existing benchmarks rarely test whether agents can proactively discover how an environment works and adapt as it evolves. To address this gap, we introduce AdaExploreBench, a benchmark for proactive exploration and test-time adaptation in evolving environments. By instantiating environmental dynamics as hidden rules, AdaExploreBench requires agents to actively acquire evidence and infer the rules. Under implicit rule drift, the latent rule changes without announcement, requiring agents to detect from degraded performance that prior knowledge is stale and re-explore. Experiments on eight leading LLMs show that proactive and adaptive exploration remain challenging: GPT-6 Sol achieves only 61.9% calibrated accuracy, while providing sufficient evidence or removing rule drift improves average performance significantly by close to 10%. These results reveal a critical bottleneck in current LLM agents: they can often reason effectively from informative evidence once it is available, but struggle to actively acquire the right evidence and recognize when previously acquired evidence is no longer valid.

cs.CL↗
arXiv2026

PSR~B0355+54: An Unexpected Compendium of Nearly All Scintillation Arc Morphologies

Scintillation arcs are sensitive probes of multipath propagation, offering a window into the distribution of scattering material in the interstellar medium. Contrary to the common observational finding that individual pulsars exhibit only a limited range of scintillation arc morphologies, we report that PSR~B0355+54, observed simultaneously at 2.25/8.60~GHz with the Shanghai Tianma Radio Telescope, displays nearly the full range of known arc patterns, many of which evolve rapidly. Across 54 observations from 2020 to 2026, we detect scintillation arcs in 53/53 usable observations at 2.25~GHz and 33/49 at 8.60~GHz. At 2.25~GHz, the secondary spectra exhibit five broad morphological types: forward arcs, inverted arclets, diffuse arcs, diffuse power, and irregular structures, with prominent displaced arclets appearing in several observations. In contrast, the 8.60-GHz spectra are generally dominated by faint forward arcs. Despite these marked morphological differences, the scaled curvatures and their annual variations agree between the two frequencies, showing that both trace the same dominant scattering screen at a fractional distance of $s=0.069^{+0.015}_{-0.014}$ near the pulsar, further supporting a possible association with the \ion{H}{ii} region Sh~2-205. Simulations show that this diversity can arise from variations in the scattered-image distribution within this screen, while brightness reconstructions at 2.25~GHz resolve localized structure down to $\sim0.02~\mathrm{mas}$. Tracking four arclets over 10~h at 8.60~GHz, including two displaced arclets, reveals a common evolutionary trend consistent with the line of sight sweeping across small-scale scattering structure, helping explain the rapid arc evolution.

astro-ph.GA↗
arXiv2026

Heat-current-induced nonreciprocal phonon damping

Time-reversal symmetry enforces equal equilibrium linewidths for time-reversed phonon modes at opposite wave vectors. Here we show that a steady heat current can produce nonreciprocal anharmonic damping even in centrosymmetric nonmagnetic crystals. The effect arises from nonequilibrium occupations of scattering partners, which change the statistical weights of time-reversed three-phonon channels while their squared matrix elements remain equal. Within the single-mode relaxation-time approximation, we derive a linewidth correction that is linear in the temperature gradient and odd under wave-vector reversal. The response depends on scattering-partner velocities and equilibrium lifetimes and retains signed channel weights absent from the equilibrium linewidth. First-principles calculations for centrosymmetric MgO and diamond yield nonzero, mode-dependent linewidth corrections at selected finite wave vectors, demonstrating the mechanism in both ionic and covalent crystals. Reversing the heat current at fixed local temperature exchanges the broadening and narrowing of paired modes. This current-reversible linewidth contrast provides a spectral signature of occupation feedback, with potential implications for nonlinear phonon transport.

cond-mat.mtrl-sci↗
arXiv2026

Compilation Is Not Correctness: Scan-by-Scan Evaluation of LLM-Generated PLC Programs

Programmable logic controllers (PLCs) automate industrial processes through repeated scans, each reading inputs, executing control logic, and updating outputs. Engineers write this logic in Structured Text (ST), a high-level textual language defined in IEC 61131-3. Large language models (LLMs) can now generate ST from natural-language requirements, yet compilation success and checks on selected properties or sampled scans can leave behavioral errors undetected. We introduce CycleBench, a benchmark of 301 ST tasks with behavioral requirements, public interfaces, reference programs, and executable scenarios. Its runtime executes each generated program and its reference under aligned inputs, initialization, and logical time, comparing their outputs after every scan. Across eight LLMs and baseline generation methods, execution pass rate trails compilation pass rate by 11.92 percentage points; observed failures include output-update, arithmetic, and timing errors. On fixed traces of programs directly generated by five LLMs, checking outputs only at every fifth scan misses 21.66% of the 157 failures detected by per-scan comparison; across sampling phases, the missed-failure rate ranges from 12.10% to 28.03%. These misses stem from transient output discrepancies, whose detection depends on the sampling phase. Building on this evaluation, we propose CycleGen, which generates multiple candidate ST programs, repairs their compilation errors with bounded compiler feedback, and selects one using runtime self-tests generated in a separate model context. CycleGen improves the execution pass rate over direct generation by 8.68 percentage points. Component ablations distinguish repair and self-test selection; candidate-count analysis shows diminishing returns, with no execution-pass-rate improvement beyond five candidates in our experiments despite 38.8% higher token consumption at eight.

cs.SE↗
arXiv2026

GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision

Depth estimation and Gaussian reconstruction share a need for multi-scale feature processing, yet use different prediction interfaces and training objectives. Whether a decoder's internal computation can be optimized once and reused across these systems remains a structural design question. Search over primitive configurations alone leaves the organization of transformations fixed. To address this gap, we propose GeoBridge, an evolutionary framework for transferable decoder topologies. First, GeoBridge makes stage-internal topology searchable through residual, sequential, and parallel composition of configurable primitives. Shape-preserving composition retains the stage interfaces of a fixed multi-scale scaffold. Second, a functional-tree encoding supports stage-aware crossover and hierarchical mutation, while a gradient sensitivity-alignment proxy evaluates candidates without training. Multi-objective evolution balances this proxy with computation and parameter cost. Third, selected topologies are instantiated and trained in target systems with task-specific prediction interfaces. A single source search takes approximately 12 GPU hours. GeoBridgeNet-A3 achieves the best depth accuracy among four adapted decoder baselines under a common encoder and training protocol on NYU Depth V2 and KITTI. The same topology reaches 27.11 dB PSNR in Gaussian novel-view synthesis on RealEstate10K, and its refined representations support open-vocabulary segmentation on LERF. These results support decoder topology as a reusable design object across the evaluated geometric vision systems. Code and pretrained weights are available at https://github.com/EMI-Group/GeoBridge-NAS.

cs.CV↗
arXiv2026

The Certification Exponent: Instance-Optimal Sequential Certification of Policy Improvement from History Dependent Logs

Before a new policy replaces a deployed one, we often want a certificate, computed from logged episodes, that it is better. We ask how many episodes such a certificate needs when the logging policy is known but history-dependent, and the error probability must stay below $δ$ in every environment consistent with the logs. The answer is an exponent $K^\star$: the smallest KL divergence from the logged law to a law with the same logged action probabilities under which the target does not improve. No sound certifier stops in fewer than $\log(1/δ)/K^\star$ expected episodes, at any $δ$, and whenever $0<K^\star<\infty$ a plug-in Kelly certifier attains this rate to first order as $δ\to0$. Coverage, computed by one backward induction, decides whether certification is impossible ($K^\star=0$), possible at a finite rate, or possible in a constant number of episodes ($K^\star=\infty$). By convex duality, the optimal certificate bets on the importance-weighted advantage and also places fair bets on the logged actions, so doubly robust control variates act as hedges rather than estimators. As a consequence, no choice of off-policy estimator changes $K^\star$, a coverage ceiling explains the curse of memory, and structural assumptions can be priced in exponent units. A cushion certifier extends the approach to trees too large to enumerate. In experiments the plug-in's median stopping time is 1.13-1.43x the lower bound on memory tasks, 1.13-1.51x on Tiger ($H\le5$) and 1.17-1.64x on random trees. The best scalar betting baselines need up to 2.65x (memory) and 4.1x (Tiger) the plug-in's episodes; only on a few shallow random trees is a doubly robust bet faster, by at most 18%.

cs.LG↗
arXiv2026

Odd-to-Even-Layer Switch of Spontaneous Excitonic State in $\rm{TiSe_{2}}$ Thin Films

$\rm{TiSe_{2}}$ is a candidate of high temperature excitonic condensates with spontaneous exciton formation in addition to its charge density wave (CDW). However, direct observation and manipulation of this electron-hole paring state formation is still lacking. Here, using varying-temperature angle-resolved photoemission spectroscopy (VT-ARPES), we observe a distinct odd-to-even-layer switch of spontaneous exciton state formation with the evident correlated band structure in $\rm{TiSe_{2}}$ thin films. While the odd-layer $\rm{TiSe_{2}}$ films show a sharp band structure in single-particle picture superimposed by CDW renormalization, in the even-layer $\rm{TiSe_{2}}$ films, the full-filling valence band (VB) opens a distinct notch at the top, and the conductive band (CB) shows a significant extra signal extending above Fermi level with spectra broadening. These features indicate that the extra holes (notched VB) and electrons (broadening CB above Fermi level) form spontaneous excitons in even-layer $\rm{TiSe_{2}}$. Based on a correlated electron-hole model extracted from the tight-binding description, we reproduce the characterized band structures with spontaneous exciton formation, where the interlayer inter-band coupling in $\rm{TiSe_{2}}$ plays a key role, which leads to an odd-to-even-layer switch of the excitonic state formation. Our results provide direct evidences of the spontaneous exciton formation, promising an excitonic condensate as versatile platform for realizing Bose-Einstein condensate (BEC) and investigating novel correlated phenomena.

cond-mat.str-el↗
arXiv2026

CoDy: Codec Dynamics as Complementary Priors for Unsupervised Temporal Action Localization

Unsupervised temporal action localization (UTAL) aims to identify action categories and localize their temporal boundaries without annotation supervision. Existing methods typically derive supervision for both tasks solely from RGB and optical-flow representations, often resulting in noisy category assignments and inaccurate temporal boundaries. In this work, we reveal an overlooked property of video codecs: group-of-pictures quantization patterns preserve category-discriminative dynamics, while motion-vector and residual variations exhibit pronounced responses around action transitions. Based on this insight, we propose CoDy, a codec dynamics-guided framework that exploits the codec as complementary information to the visual view for UTAL. CoDy integrates a codec-enhanced view with RGB and flow through confidence-aware consensus clustering, producing reliable soft pseudo-labels. It further converts codec dynamics into a class-agnostic boundary prior, which supervises visual boundary learning and guides proposal initialization and iterative refinement. The codec branches are only used during the training stage and introduce no extra codec-side computation at inference. Extensive experiments on THUMOS14 and ActivityNet v1.3 demonstrate that CoDy consistently outperforms existing UTAL methods and achieves state-of-the-art localization performance. These results establish codec signals as effective annotation-free priors for jointly inferring which actions occur and when they occur.

cs.CV↗
arXiv2026

Value-Guided Repulsion for Offline Reinforcement Learning

Drifting models have emerged as a highly efficient and stable paradigm for policy design in offline reinforcement learning (RL). However, standard drift policies determine repulsion solely from geometric relationships among generated actions and therefore cannot distinguish them by expected value. To address this limitation, we introduce Value-Guided Repulsion Q-Learning (VRQL) to integrate action value estimates into the particle interaction field. For each state, VRQL combines geometric kernel distance with a normalized Q value scaling. This mechanism assigns greater repulsive influence to particles with lower values while strictly retaining attraction toward behavior action. Consequently, this value-guided interaction field aligns behavior regularization with policy improvement without adding an auxiliary behavior model stage. On 50 OGBench task configurations and 6 D4RL AntMaze configurations, VRQL demonstrates highly competitive performance against established baselines, suggesting that value-guided particle interaction is an effective method for offline RL.

cs.AI↗