The emergence of multimodal large language models has redefined the agent paradigm by integrating language and vision modalities with external data sources, enabling agents to better interpret human instructions and execute increasingly complex tasks. However, in this paper, we identify a critical yet previously overlooked security vulnerability in multimodal agents: cross-modal prompt injection attacks. To exploit this vulnerability, we propose CrossInject, a novel attack framework in which attackers embed adversarial perturbations across multiple modalities to align with target malicious content, allowing external instructions to hijack the agent's decision-making process and execute unauthorized tasks. Our approach incorporates two key coordinated components. First, we introduce Visual Latent Alignment, where we optimize adversarial features to the malicious instructions in the visual embedding space based on a text-to-image generative model, ensuring that adversarial images subtly encode cues for malicious task execution. Subsequently, we present Textual Guidance Enhancement, where a large language model is leveraged to construct the black-box defensive system prompt through adversarial meta prompting and generate an malicious textual command that steers the agent's output toward better compliance with attackers' requests. Extensive experiments demonstrate that our method outperforms state-of-the-art attacks, achieving at least a +30.1% increase in attack success rates across diverse tasks. Furthermore, we validate our attack's effectiveness in real-world multimodal autonomous agents, highlighting its potential implications for safety-critical applications.

Vision-Language Models (VLMs) have recently emerged as a promising paradigm in autonomous driving (AD). However, current performance evaluation protocols for VLM-based AD systems (ADVLMs) are predominantly confined to open-loop settings with static inputs, neglecting the more realistic and informative closed-loop setting that captures interactive behavior, feedback resilience, and real-world safety. To address this, we introduce Bench2ADVLM, a unified hierarchical closed-loop evaluation framework for real-time, interactive assessment of ADVLMs across both simulation and physical platforms. Inspired by dual-process theories of cognition, we first adapt diverse ADVLMs to simulation environments via a dual-system adaptation architecture. In this design, heterogeneous high-level driving commands generated by target ADVLMs (fast system) are interpreted by a general-purpose VLM (slow system) into standardized mid-level control actions suitable for execution in simulation. To bridge the gap between simulation and reality, we design a physical control abstraction layer that translates these mid-level actions into low-level actuation signals, enabling, for the first time, closed-loop testing of ADVLMs on physical vehicles. To enable more comprehensive evaluation, Bench2ADVLM introduces a self-reflective scenario generation module that automatically explores model behavior and uncovers potential failure modes for safety-critical scenario generation. Overall, Bench2ADVLM establishes a hierarchical evaluation pipeline that seamlessly integrates high-level abstract reasoning, mid-level simulation actions, and low-level real-world execution. Experiments on diverse scenarios across multiple state-of-the-art ADVLMs and physical platforms validate the diagnostic strength of our framework, revealing that existing ADVLMs still exhibit limited performance under closed-loop conditions.

Based on $10.64~\mathrm{fb}^{-1}$ of $e^+e^-$ collision data taken at center-of-mass energies between 4.237 and 4.699 GeV with the BESIII detector, we study the leptonic $D^+_s$ decays using the $e^+e^-\to D^{*+}_{s} D^{*-}_{s}$ process. The branching fractions of $D_s^+\to\ell^+\nu_{\ell}\,(\ell=\mu,\tau)$ are measured to be $\mathcal{B}(D_s^+\to\mu^+\nu_\mu)=(0.547\pm0.026_{\rm stat}\pm0.016_{\rm syst})\%$ and $\mathcal{B}(D_s^+\to\tau^+\nu_\tau)=(5.60\pm0.16_{\rm stat}\pm0.20_{\rm syst})\%$, respectively. The product of the decay constant and Cabibbo-Kobayashi-Maskawa matrix element $|V_{cs}|$ is determined to be $f_{D_s^+}|V_{cs}|=(246.5\pm5.9_{\rm stat}\pm3.6_{\rm syst}\pm0.5_{\rm input})_{\mu\nu}~\mathrm{MeV}$ and $f_{D_s^+}|V_{cs}|=(252.7\pm3.6_{\rm stat}\pm4.5_{\rm syst}\pm0.6_{\rm input}))_{\tau \nu}~\mathrm{MeV}$, respectively. Taking the value of $|V_{cs}|$ from a global fit in the Standard Model, we obtain ${f_{D^+_s}}=(252.8\pm6.0_{\rm stat}\pm3.7_{\rm syst}\pm0.6_{\rm input})_{\mu\nu}$ MeV and ${f_{D^+_s}}=(259.2\pm3.6_{\rm stat}\pm4.5_{\rm syst}\pm0.6_{\rm input})_{\tau \nu}$ MeV, respectively. Conversely, taking the value for $f_{D_s^+}$ from the latest lattice quantum chromodynamics calculation, we obtain $|V_{cs}| =(0.986\pm0.023_{\rm stat}\pm0.014_{\rm syst}\pm0.003_{\rm input})_{\mu\nu}$ and $|V_{cs}| = (1.011\pm0.014_{\rm stat}\pm0.018_{\rm syst}\pm0.003_{\rm input})_{\tau \nu}$, respectively.

The generation of safety-critical scenarios in simulation has become increasingly crucial for safety evaluation in autonomous vehicles prior to road deployment in society. However, current approaches largely rely on predefined threat patterns or rule-based strategies, which limit their ability to expose diverse and unforeseen failure modes. To overcome these, we propose ScenGE, a framework that can generate plentiful safety-critical scenarios by reasoning novel adversarial cases and then amplifying them with complex traffic flows. Given a simple prompt of a benign scene, it first performs Meta-Scenario Generation, where a large language model, grounded in structured driving knowledge, infers an adversarial agent whose behavior poses a threat that is both plausible and deliberately challenging. This meta-scenario is then specified in executable code for precise in-simulator control. Subsequently, Complex Scenario Evolution uses background vehicles to amplify the core threat introduced by Meta-Scenario. It builds an adversarial collaborator graph to identify key agent trajectories for optimization. These perturbations are designed to simultaneously reduce the ego vehicle's maneuvering space and create critical occlusions. Extensive experiments conducted on multiple reinforcement learning based AV models show that ScenGE uncovers more severe collision cases (+31.96%) on average than SoTA baselines. Additionally, our ScenGE can be applied to large model based AV systems and deployed on different simulators; we further observe that adversarial training on our scenarios improves the model robustness. Finally, we validate our framework through real-world vehicle tests and human evaluation, confirming that the generated scenarios are both plausible and critical. We hope our paper can build up a critical step towards building public trust and ensuring their safe deployment.

Consumer-grade camera systems often struggle to maintain stable image quality under complex illumination conditions such as low light, high dynamic range, and backlighting, as well as spatial color temperature variation. These issues lead to underexposure, color casts, and tonal inconsistency, which degrade the performance of downstream vision tasks. To address this, we propose ACamera-Net, a lightweight and scene-adaptive camera parameter adjustment network that directly predicts optimal exposure and white balance from RAW inputs. The framework consists of two modules: ACamera-Exposure, which estimates ISO to alleviate underexposure and contrast loss, and ACamera-Color, which predicts correlated color temperature and gain factors for improved color consistency. Optimized for real-time inference on edge devices, ACamera-Net can be seamlessly integrated into imaging pipelines. Trained on diverse real-world data with annotated references, the model generalizes well across lighting conditions. Extensive experiments demonstrate that ACamera-Net consistently enhances image quality and stabilizes perception outputs, outperforming conventional auto modes and lightweight baselines without relying on additional image enhancement modules.

The spin and parity of the $Z_c(3900)^\pm$ state are determined to be $J^P=1^+$ with a statistical significance larger than $7\sigma$ over other quantum numbers in a partial wave analysis of the process $e^+e^-\to \pi^+\pi^-J/\psi$. We use a data sample of 1.92 fb$^{-1}$ accumulated at $\sqrt{s}=4.23$ and 4.26 GeV with the BESIII experiment. When parameterizing the $Z_c(3900)^\pm$ with a Flatte-like formula, we determine its pole mass $M_\textrm{pole}=(3881.2\pm4.2_\textrm{stat}\pm52.7_\textrm{syst})\textrm{MeV}/c^2$ and pole width $\Gamma_\textrm{pole}=(51.8\pm4.6_\textrm{stat}\pm36.0_\textrm{syst})\textrm{MeV}$. We also measure cross sections for the process $e^+e^-\to Z_c(3900)^+\pi^-+c.c.\to J/\psi\pi^+\pi^-$ and determine an upper limit at the 90\% confidence level for the process $e^+e^-\to Z_c(4020)^+\pi^-+c.c.\to J/\psi\pi^+\pi^-$.

This paper presents an adaptive ensemble control for stochastic systems subject to asymmetric noises and outliers. Asymmetric noises skew system observations, and outliers with large amplitude deteriorate the observations even further. Such disturbances induce poor system estimation and degraded stochastic system control. In this work, we model the asymmetric noises and outliers by mixed asymmetric Laplace distributions (ALDs), and propose an optimal control for stochastic systems with mixed ALD noises. Particularly, we segregate the system disturbed by mixed ALD noises into subsystems, each of which is subject to a specific ALD noise. For each subsystem, we design an iterative quantile filter (IQF) to estimate the system parameters using system observations. With the estimated parameters by IQF, we derive the certainty equivalence (CE) control law for each subsystem. Then we use the Bayesian approach to ensemble the subsystem CE controllers, with each of the controllers weighted by their posterior probability. We finalize our control law as the weighted sum of the control signals by the sub-system CE controllers. To demonstrate our approach, we conduct numerical simulations and Monte Carlo analyses. The results show improved tracking performance by our approach for skew noises and its robustness to outliers, compared with single ALD based and RLS-based control policy.

Topological Anderson insulators represent a class of disorder-induced, nontrivial topological states. In this study, we propose a feasible strategy to unveil and design the latent-symmetry protected topological Anderson insulators. By employing the isospectral reduction approach from graph theory, we reduce a family of the disordered multi-atomic chains to the disordered dimerized chain characterized by energy-dependent potentials and hoppings, which exhibits the chiral symmetry or inversion symmetry. According to the topological invariants, bulk polarization, and the divergence of localization length of the topological bound edge states in the reduced disordered system, the gapped and ungapped topological Anderson states with latent symmetry could be identified in the original disordered multi-atomic systems. The concept of topological Anderson insulating phases protected by the geometric symmetries and tenfold-way classification is thus extended to the various types of latent symmetry cases. This work paves the way for exploiting topological Anderson insulators in terms of latent symmetries.

We study the $e^+e^-\to\gamma\omega J/\psi$ process using $11.6 ~\rm fb^{-1}$ $e^+ e^-$ annihilation data taken at center-of-mass energies from $\sqrt{s}=4.008~\rm GeV$ to $4.600~\rm GeV$ with the BESIII detector at the BEPCII storage ring. The $X(3872)$ resonance is observed for the first time in the $\omega J/\psi$ system with a significance of more than $5\sigma$. The relative decay ratio of $X(3872)\to\omega J/\psi$ and $\pi^+\pi^- J/\psi$ is measured to be $\mathcal{R}=1.6^{+0.4}_{-0.3}\pm0.2$, where the first error is statistical and the second systematic (the same hereafter). The $\sqrt{s}$-dependent cross section of $e^+e^-\to\gamma X(3872)$ is also measured and investigated, and it can be described by a single Breit-Wigner resonance, referred to as the $Y(4200)$, with a mass of $4200.6^{+7.9}_{-13.3}\pm3.0~{\rm MeV}/c^2$ and a width of $115^{+38}_{-26}\pm12~{\rm MeV}$. In addition, to describe the $\omega J/\psi$ mass distribution above $3.9~\rm GeV/c^2$, we need at least one additional Breit-Wigner resonance, labeled as $X(3915)$, in the fit. The mass and width of the $X(3915)$ are measured to be $3926.4\pm2.2\pm1.2~\rm MeV/c^2$ and $3.8\pm7.5\pm2.6~\rm MeV$, or $3932.6\pm8.7\pm4.7~\rm MeV/c^2$ and $59.7\pm15.5\pm3.7~\rm MeV$, respectively, depending on the fit models. The resonant parameters of the $X(3915)$ agree with those of the $Y(3940)$ in $B\to K\omega J/\psi$ and of the $X(3915)$ in $\gamma\gamma\to\omega J/\psi$ by the Belle and BABAR experiments within errors.

Using the data sample of an integrated luminosity of 2.93 fb$^{-1}$ taken at the center-of-mass energy of 3.773 GeV, we search for the Majorana neutrino in the lepton number violating decays $D\to K \pi e^+ e^+$. No significant signal is observed, and the upper limits on the branching fraction at the 90\% confidence level are set to be \mathcal{B}\,(D^0 \to K^- \pi^- e^+ e^+)<2.8\times10^{-6}, \mathcal{B}\,(D^+ \to K_S^0 \pi^- e^+ e^+)<3.3\times10^{-6} and \mathcal{B}\,(D^+ \to K^- \pi^0 e^+ e^+)<8.5\times10^{-6}. The Majorana neutrino is searched for with different mass assumptions ranging from 0.25 to 1.0 GeV/$c^2$ in the decays $D^0\to K^- e^+ \nu_N(\pi^- e^+)$ and $D^+\to K_S^0 e^+ \nu_N(\pi^- e^+)$, and the upper limits on the branching fraction at the 90\% confidence level are extracted to be at the level of $10^{-7} \sim 10^{-6}$, depending on the mass of Majorana neutrino. The constraints on the mixing matrix element $|V_{eN}|^2$ are also evaluated.

Using $7.33~\mathrm{fb}^{-1}$ of $e^+e^-$ collision data taken with the BESIII detector at the BEPCII collider, we report the first experimental study of the purely leptonic decay $D_s^{*+}\to e^+\nu_e$. A signal for the decay $D_s^{*+}\to e^+\nu_e$ is observed with a statistical significance of $2.9\sigma$. The branching fraction of ${D_s^{*+}\to e^+\nu_e}$ is measured to be $(2.1{^{+1.2}_{-0.9}}_{\rm stat.}\pm0.2_{\rm syst.})\times 10^{-5}$, corresponding to an upper limit of $4.0\times10^{-5}$ at the 90\% confidence level. Taking the total width of the $D_s^{*+}$~(($0.070\pm0.028$) keV) predicted by lattice quantum chromodynamics as input, the decay constant of the $D^{*+}_s$ is determined to be $f_{D_s^{*+}}=(213.6{^{+61.0}_{-45.8}}_{\rm stat.}\pm43.9_{\rm syst.})$ MeV, corresponding to an upper limit of 353.8 MeV at the 90\% confidence level.

Existing video summarization approaches mainly concentrate on sequential or structural characteristic of video data. However, they do not pay enough attention to the video summarization task itself. In this paper, we propose a meta learning method for performing task-driven video summarization, denoted by MetaL-TDVS, to explicitly explore the video summarization mechanism among summarizing processes on different videos. Particularly, MetaL-TDVS aims to excavate the latent mechanism for summarizing video by reformulating video summarization as a meta learning problem and promote generalization ability of the trained model. MetaL-TDVS regards summarizing each video as a single task to make better use of the experience and knowledge learned from processes of summarizing other videos to summarize new ones. Furthermore, MetaL-TDVS updates models via a two-fold back propagation which forces the model optimized on one video to obtain high accuracy on another video in every training step. Extensive experiments on benchmark datasets demonstrate the superiority and better generalization ability of MetaL-TDVS against several state-of-the-art methods.

Text-to-image (T2I) models have demonstrated remarkable generative capabilities but remain vulnerable to producing not-safe-for-work (NSFW) content, such as violent or explicit imagery. While recent moderation efforts have introduced soft prompt-guided tuning by appending defensive tokens to the input, these approaches often rely on large-scale curated image-text datasets and apply static, one-size-fits-all defenses at inference time. However, this results not only in high computational cost and degraded benign image quality, but also in limited adaptability to the diverse and nuanced safety requirements of real-world prompts. To address these challenges, we propose PromptSafe, a gated prompt tuning framework that combines a lightweight, text-only supervised soft embedding with an inference-time gated control network. Instead of training on expensive image-text datasets, we first rewrite unsafe prompts into semantically aligned but safe alternatives using an LLM, constructing an efficient text-only training corpus. Based on this, we optimize a universal soft prompt that repels unsafe and attracts safe embeddings during the diffusion denoising process. To avoid over-suppressing benign prompts, we introduce a gated mechanism that adaptively adjusts the defensive strength based on estimated prompt toxicity, thereby aligning defense intensity with prompt risk and ensuring strong protection for harmful inputs while preserving benign generation quality. Extensive experiments across multiple benchmarks and T2I models show that PromptSafe achieves a SOTA unsafe generation rate (2.36%), while preserving high benign fidelity. Furthermore, PromptSafe demonstrates strong generalization to unseen harmful categories, robust transferability across diffusion model architectures, and resilience under adaptive adversarial attacks, highlighting its practical value for safe and scalable deployment.

In the procedure of surface defects detection for large-aperture aspherical optical elements, it is of vital significance to adjust the optical axis of the element to be coaxial with the mechanical spin axis accurately. Therefore, a machine vision method for eccentric error correction is proposed in this paper. Focusing on the severe defocus blur of reference crosshair image caused by the imaging characteristic of the aspherical optical element, which may lead to the failure of correction, an Adaptive Enhancement Algorithm (AEA) is proposed to strengthen the crosshair image. AEA is consisted of existed Guided Filter Dark Channel Dehazing Algorithm (GFA) and proposed lightweight Multi-scale Densely Connected Network (MDC-Net). The enhancement effect of GFA is excellent but time-consuming, and the enhancement effect of MDC-Net is slightly inferior but strongly real-time. As AEA will be executed dozens of times during each correction procedure, its real-time performance is very important. Therefore, by setting the empirical threshold of definition evaluation function SMD2, GFA and MDC-Net are respectively applied to highly and slightly blurred crosshair images so as to ensure the enhancement effect while saving as much time as possible. AEA has certain robustness in time-consuming performance, which takes an average time of 0.2721s and 0.0963s to execute GFA and MDC-Net separately on ten 200pixels 200pixels Region of Interest (ROI) images with different degrees of blur. And the eccentricity error can be reduced to within 10um by our method.

VO2 material is promising for developing energy-saving "smart window", owing to its thermochromic property induced by metal-insulator transition (MIT). However, its practical application is greatly limited by the relatively high critical transition temperature (~68oC), low luminous transmittance (<60%) and poor solar energy regulation ability (<15%). Here we developed a reversible and non-volatile electric-field control on the MIT of monoclinic VO2 film. With a solid electrolyte layer assisted gating treatment, we modulated the insertion/extraction of hydrogens into/from VO2 lattice at room temperature, causing tri-state phase transitions accompanied with controllable transmission adjustment. The dramatic increase of visible/infrared transmittance during the phase transition from the metallic (lightly H-doping) to insulating (heavily H-doping) phase leads to an increased solar energy regulation ability up to 26.5%, while keep 70.8% visible-luminous transmittance. These results beat all previous records and even exceeded the theoretical limit for traditional VO2 smart window, removing intrinsic disadvantages of VO2 for energy-saving utilizations. Our findings not only demonstrated an electric-field controlled phase modulation strategy, but also open the door for high-performance VO2-based smart window applications.

As one of the important methods for surface modification of materials and life extension of key components, cemented carbide brazing coatings are widely used in agricultural machinery, oil drilling, aerospace and other fields, it has also attracted the attention of scholars in the field of surface modification at home and abroad. Based on the research reports of recent 20 years at home and abroad, the present research situation of cemented carbide brazed coating additive manufacturing technology are reviewed firstly. The research progress in preparation and performance control of cemented carbide/iron-base, cemented carbide/copper-base, cemented carbide/nickel-base, cemented carbide/silver-base heterogeneous brazing coatings are reviewed in detail. Then the practical applications of cemented carbide heterogenous brazing coatings in the fields of contact soil agricultural machinery parts life extension, aviation parts repair, surface function strengthening and so on are reviewed. In this review, the limitation of the research of cemented carbide heterogenous braze coating technology is discussed. The deficiencies in the research and development of cemented carbide heterogenous braze coating technology are summarized including cemented carbide heterogeneous additive materials and technology need to be expanded, the structure of cemented carbide heterogeneous brazing coatings need to be optimized, the mechanism of interface defects in cemented carbide heterogeneous brazing coatings need to be clarified. Finally, the future development direction of braze coating technology is prospected, too.

The cross section for the process $e^+e^-\to \pi^+\pi^-J/\psi$ is measured precisely at center-of-mass energies from 3.77 to 4.60~GeV using 9~fb$^{-1}$ of data collected with the BESIII detector operating at the BEPCII storage ring. Two resonant structures are observed in a fit to the cross section. The first resonance has a mass of $(4222.0\pm 3.1\pm 1.4)$~MeV/$c^2$ and a width of $(44.1\pm 4.3\pm 2.0)$~MeV, while the second one has a mass of $(4320.0\pm 10.4 \pm 7.0)$~MeV/$c^2$ and a width of $(101.4^{+25.3}_{-19.7}\pm 10.2)$~MeV, where the first errors are statistical and second ones are systematic. The first resonance agrees with the $Y(4260)$ resonance reported by previous experiments. The precision of its resonant parameters is improved significantly. The second resonance is observed in $e^+e^-\to \pi^+\pi^-J/\psi$ for the first time. The statistical significance of this resonance is estimated to be larger than $7.6\sigma$. The mass and width of the second resonance agree with the $Y(4360)$ resonance reported by the $BABAR$ and Belle experiments within errors. Finally, the $Y(4008)$ resonance previously observed by the Belle experiment is not confirmed in the description of the BESIII data.

Using a data sample corresponding to an integrated luminosity of 11.3 $\rm fb^{-1}$ collected at center-of-mass energies from $4.23$ to $4.70$ GeV with the BESIII detector, we measure the product of the $e^+e^-\to \pi^+\pi^-\psi_2(3823)$ cross section and the branching fraction $\mathcal{B}[\psi_2(3823)\to \gamma\chi_{c1}]$. For the first time, resonance structure is observed in the cross section line shape of $e^+e^-\to \pi^+\pi^-\psi_2(3823)$ with significances exceeding $5\sigma$. A fit to data with two coherent Breit-Wigner resonances modeling the $\sqrt{s}$-dependent cross section yields $M(R_1)=4406.9\pm 17.2\pm 4.5$ MeV/$c^2$, $\Gamma(R_1)=128.1\pm 37.2\pm 2.3$ MeV, and $M(R_2)=4647.9\pm 8.6\pm 0.8$ MeV/$c^2$, $\Gamma(R_2)=33.1\pm 18.6\pm 4.1$ MeV. Though weakly disfavored by the data, a single resonance with $M(R)=4417.5\pm26.2\pm3.5$ MeV/$c^2$, $\Gamma(R)=245\pm48\pm13$ MeV is also possible to interpret data. This observation deepens our understanding of the nature of the vector charmoniumlike states. The mass of the $\psi_2(3823)$ state is measured as $(3823.12\pm 0.43\pm 0.13)$ MeV/$c^2$, which is the most precise measurement to date.