arXiv subjects

Yan Meng

Publications and source records attributed to Yan Meng.

At least 19 recordsLinked to original sources

arXiv2026

An Integrated Video-AI Platform for Action-Level Microanastomosis Training and Performance Feedback

Developing microanastomosis skill requires repeated practice with timely, action-specific feedback, yet expert review of lengthy microscope videos does not scale to frequent or distributed training. We present an integrated video-AI platform that turns a complete simulated procedure into inspectable, interactive feedback through three connected modules. First, a proposed transformer segments the video into six surgical actions. Second, object detection and tracking localize instrument tips within each action; the resulting kinematic features and action statistics drive supervised classification of five NOMAT-aligned performance dimensions. Third, a grounded large language model (LLM) uses these structured outputs to answer user questions about the current scene, actions, motion, and predicted performance through a unified interface. In a two-site study, 17 participants completed 72 procedures comprising 576 suture placements. The action-segmentation module achieved 87.66\% accuracy and 82.86\% F1, increasing to 93.62\% and 88.32\% after workflow-aware refinement. The five performance classifiers achieved 76.0\% mean accuracy, with Cohen's $\kappa$ from 0.63 to 0.93. Although the language interface and educational benefit require prospective evaluation, these results establish the technical basis for an expert-supervised platform that can shorten review, expose the evidence behind performance estimates, and support scalable formative microsurgical training.

cs.CV↗

arXiv2026

All-optical reconstruction of valley polarization through helicity-resolved high-harmonic generation

We theoretically investigate valley-resolved high-order harmonic generation in gapped graphene driven by elliptically polarized laser fields. Using two-band density-matrix simulations and an electron-hole recombination trajectory model, we find that the two inequivalent valleys emit harmonics with opposite helicities. Under an elliptically polarized field, these emissions occur predominantly in different half cycles of the laser field. Time-dependent density functional theory calculations for monolayer MoS$_2$ show the same temporal separation of harmonic emissions with opposite helicities, supporting the generality of this valley-dependent chiral response. We further propose an all-optical scheme to reconstruct valley polarization from helicity-resolved harmonic signals. A circularly polarized pulse first prepares a valley population imbalance. A subsequent elliptically polarized laser induces different changes in the harmonic intensities of opposite helicities through Pauli blocking. The ratio of these intensity changes provides a direct measure of the valley polarization. Our results demonstrate that chiral high-harmonic emission can serve as an all-optical probe of ultrafast valley-dependent carrier dynamics.

physics.optics↗

arXiv2026

Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation

World models give embodied AI a predictive core: they compress observations into states, simulate action-conditioned futures, and enable planning beyond reactive control. This predictive layer, however, opens a new security boundary-compromise can propagate from data, sensors, prompts, or feedback into physical action. Rather than treating world models as an isolated component, this survey traces threats across their entire lifecycle-from data construction and representation learning, through state grounding and imagination, to trajectory evaluation, execution, and long-term adaptation via memory and tools. We show that familiar attack families: poisoning, backdoors, adversarial examples, sensor spoofing, prompt injection, trajectory manipulation, and supply-chain attacks take on distinct meanings when they corrupt world states, learned dynamics, affordance estimates, or safety costs. We also highlight a duality: world models can serve as runtime safety shields, yet when compromised or over-trusted they generate predictive safety illusions. The survey offers a lifecycle taxonomy, maps existing attacks to world-model security properties, outlines evaluation protocols for safety failures, and structures defenses across provenance, robust grounding, uncertainty-aware prediction, trajectory gating, feedback auditing, and deployment assurance.

cs.CR↗

arXiv2026

Observation of fractality-induced topology in photonic crystals

Fractal topology--achieved by integrating nontrivial topology into fractal geometries with self-similarity and non-integer dimensions--has opened new avenues for exploring topological phases of matter. Recent theoretical advances revealed a counterintuitive fractal topology: fractality itself can induce nontrivial topology in an otherwise trivial system. Here, we report the first experimental observation of fractality-induced topology in a tight-binding-like photonic crystal, without relying on traditional driving mechanisms such as magnetic fields, staggered hopping, or spin-orbit coupling. We demonstrate that fractality alone is sufficient to lift the degeneracy of Kagome lattice band structure and induce topological corner states within the bandgap of the resulting fractal Kagome photonic crystal, which is a photonic higher-order topological insulator. This work experimentally reveals a novel mechanism for realizing nontrivial topological states, expanding both the fundamental frontier and potential application of topological physics.

physics.optics↗

arXiv2026

DIPBox: A Multi-scale Testing Framework for Tracking Dataset Regeneration

Training datasets have tremendous proprietary value and are vulnerable to unauthorized copying. Existing defenses mainly focus on tracking individual data points, but pay little attention to the threat of dataset regeneration. Through a measurement study of public tumor datasets, we identify substantial real-world partial-dataset replication, raising concerns about potential license noncompliance. To counter the challenge of tracking previously unknown adversarial regeneration, our key insight is that regeneration that preserves model utility inevitably preserves measurable signals across multiple feature scales. We categorize these dataset features into sample-, set-, and distribution-level features and design four similarity metrics to accurately identify regeneration. Based on these metrics, we develop DIPBox, which to our knowledge is the first testing framework that tracks regeneration suspects via multi-scale similarity testing across a spectrum of defender access settings, from limited to full information. We further provide a learning-theoretic analysis that justifies these multi-scale metrics and formalizes an inherent utility--divergence trade-off, implying fundamental limits on evasive regeneration. Extensive experiments on 16 vision and text base datasets, 320 regenerated datasets, and 590 derived models validate that DIPBox outperforms previous solutions while characterizing its robustness and limits under three adaptive attacks.

cs.CR↗

arXiv2026

Superconductivity in the pressure-amorphized topological insulator CrP$_4$

The interplay among superconductivity, magnetism, and nontrivial band topology represents one of the most compelling frontiers in condensed matter physics. The exploration of novel superconductivity in 3d transition-metal compounds, particularly the rare Cr-based systems containing strongly magnetic Cr ions, has long attracted attention owing to their unconventional pairing mechanisms that challenge conventional wisdom. Yet, Cr-based superconductors remain scarce, especially those possessing nontrivial topological character, underscoring the urgent need to uncover new members. Here we report the observation of superconductivity in pressure-amorphized Cr-based topological insulator CrP$_4$. Upon compression, CrP$_4$ undergoes an anomalous quantum phase transition from a metallic to a semiconducting-like state at around 15 GPa, driven by significant changes in the electronic structure. At approximately 70 GPa, re-metallization with superconductivity occurs alongside an irreversible amorphization. The superconducting transition temperature Tc increases monotonically with pressure, reaching 4.8 K at 141.3 GPa. Furthermore, theoretical calculations predict multiple topological phase transitions from a strong topological insulator to a trivial state and finally back to a strong topological state under pressure. Our study not only establishes CrP$_4$ as the first Cr-based amorphous superconductor but also opens a new paradigm for exploring superconducting and topological properties in amorphous materials.

cond-mat.supr-con↗

arXiv2026

Strain-Induced Tuning of Third-Harmonic Generation in Monolayer Black Phosphorene

Based on the tight-binding model and the semiconductor Bloch equations, this work systematically reveals the microscopic mechanism of strain engineering in turning of third-harmonic generation (THG) in monolayer black phosphorene (BP). % The results show that under strain-free conditions, monolayer BP exhibits significant in-plane anisotropy, and its dominant susceptibility component reaches a maximum of $\chi^{(3);xxxx} = 1.8 \times 10^{-17} \, \text{m}^2/\text{V}^2$, agreeing well with the experimental results. % By applying uniaxial and biaxial strains along the armchair ($x$), zigzag ($y$), and out-of-plane ($z$) directions, we find that the THG response presents strong direction dependence and unique spectral shifting behaviors: in-plane compressive strain and out-of-plane tensile strain both significantly enhance the THG conductivity and induce a redshift, whereas in-plane tensile strain and out-of-plane compression lead to suppression and a blueshift, with the tuning efficiency following the order of $z > y > x$. The microscopic origin of these phenomena is identified as the synergistic modulation of the bandgap and Berry connection by strain. % Furthermore, the synergistic or competitive effects of biaxial strain further enrich the manipulation of THG signals. % Strain engineering can serve as an effective strategy for dynamically controlling nonlinear optical processes in two-dimensional materials, and it also lays a theoretical foundation for the development of high-performance reconfigurable infrared photonic devices.

cond-mat.mes-hall↗

arXiv2026

Orbital Altermagnetic Photonic Crystal

Altermagnetism features momentum-dependent spin splitting without net magnetization, extending spintronics beyond conventional ferromagnetism and antiferromagnetism. However, the photonic realization of altermagnetism has remained a formidable challenge due to the fundamental differences between fermionic electrons and bosonic photons. Here, we report the first experimental realization of an orbital altermagnetic photonic crystal, based on an antiunitary $C_{4z}\mathcal{T}$ symmetry enforced correspondence between a local $p$-orbital $\sigma/\pi$ doublet and crystal momentum. We experimentally demonstrate that the resulting system exhibits momentum-dependent spin splitting with alternating pseudospin polarization and a $d_{xy}$-wave form factor, as confirmed by measured band structures and iso-frequency contours. Moreover, we show that the orbital altermagnetic photonic crystal supports unique pseudospin-selective transport of electromagnetic waves, including photonic pseudospin splitting and pseudospin filtering. Our results extend the field of alternagnetism to photonic systems, opening a new avenue for designing spinphotonic devices.

physics.optics↗

arXiv2026

Ultrasensitive Terahertz Metasurface Biosensor Based on Quasi-Bound States in the Continuum

The terahertz (THz) spectral regime offers unique opportunities for next-generation biochemical sensing due to its non-destructive, label-free probing capability and strong sensitivity to molecular vibrations. However, conventional THz biosensors remain hampered by intrinsically low-quality factors and limited sensitivity, severely restricting their utility for trace-level biochemical and chemical detection. Here, we report an ultrasensitive THz metasurface biosensor that harnesses quasi-bound states in the continuum (QBICs) with sharp resonances and enhanced light-matter interactions to overcome these limitations. As a proof of concept, the device achieves label-free detection of a sulfur-containing amino acid cysteine, with an ultrahigh sensitivity of 492 GHz/RIU and an ultralow detection limit down to 0.00025 mg/mL. The synergy between QBIC-induced field confinement and meticulous structural optimization of the metasurface underpins this performance, marking a significant advance over conventional THz metasurface biosensing schemes. These results establish QBIC-based metasurfaces as a promising platform for ultrasensitive and high-precision biochemical and chemical sensing, with broad implications for medical diagnostics, food safety, and environmental monitoring.

physics.optics↗

arXiv2026

SurgPhase: Time efficient pituitary tumor surgery phase recognition via an interactive web platform

Accurate surgical phase recognition is essential for analyzing procedural workflows, supporting intraoperative decision-making, and enabling data-driven improvements in surgical education and performance evaluation. In this work, we present a comprehensive framework for phase recognition in pituitary tumor surgery (PTS) videos, combining self-supervised representation learning, robust temporal modeling, and scalable data annotation strategies. Our method achieves 90\% accuracy on a held-out test set, outperforming current state-of-the-art approaches and demonstrating strong generalization across variable surgical cases. A central contribution of this work is the integration of a collaborative online platform designed for surgeons to upload surgical videos, receive automated phase analysis, and contribute to a growing dataset. This platform not only facilitates large-scale data collection but also fosters knowledge sharing and continuous model improvement. To address the challenge of limited labeled data, we pretrain a ResNet-50 model using the self-supervised framework on 251 unlabeled PTS videos, enabling the extraction of high-quality feature representations. Fine-tuning is performed on a labeled dataset of 81 procedures using a modified training regime that incorporates focal loss, gradual layer unfreezing, and dynamic sampling to address class imbalance and procedural variability.

cs.CV↗

arXiv2026

Trojan's Whisper: Stealthy Manipulation of OpenClaw through Injected Bootstrapped Guidance

Autonomous coding agents are increasingly integrated into software development workflows, offering capabilities that extend beyond code suggestion to active system interaction and environment management. OpenClaw, a representative platform in this emerging paradigm, introduces an extensible skill ecosystem that allows third-party developers to inject behavioral guidance through lifecycle hooks during agent initialization. While this design enhances automation and customization, it also opens a novel and unexplored attack surface. In this paper, we identify and systematically characterize guidance injection, a stealthy attack vector that embeds adversarial operational narratives into bootstrap guidance files. Unlike traditional prompt injection, which relies on explicit malicious instructions, guidance injection manipulates the agent's reasoning context by framing harmful actions as routine best practices. These narratives are automatically incorporated into the agent's interpretive framework and influence future task execution without raising suspicion.We construct 26 malicious skills spanning 13 attack categories including credential exfiltration, workspace destruction, privilege escalation, and persistent backdoor installation. We evaluate them using ORE-Bench, a realistic developer workspace benchmark we developed. Across 52 natural user prompts and six state-of-the-art LLM backends, our attacks achieve success rates from 16.0% to 64.2%, with the majority of malicious actions executed autonomously without user confirmation. Furthermore, 94% of our malicious skills evade detection by existing static and LLM-based scanners. Our findings reveal fundamental tensions in the design of autonomous agent ecosystems and underscore the urgent need for defenses based on capability isolation, runtime policy enforcement, and transparent guidance provenance.

cs.CR↗

arXiv2026

SlowBA: An efficiency backdoor attack towards VLM-based GUI agents

Modern vision-language-model (VLM) based graphical user interface (GUI) agents are expected not only to execute actions accurately but also to respond to user instructions with low latency. While existing research on GUI-agent security mainly focuses on manipulating action correctness, the security risks related to response efficiency remain largely unexplored. In this paper, we introduce SlowBA, a novel backdoor attack that targets the responsiveness of VLM-based GUI agents. The key idea is to manipulate response latency by inducing excessively long reasoning chains under specific trigger patterns. To achieve this, we propose a two-stage reward-level backdoor injection (RBI) strategy that first aligns the long-response format and then learns trigger-aware activation through reinforcement learning. In addition, we design realistic pop-up windows as triggers that naturally appear in GUI environments, improving the stealthiness of the attack. Extensive experiments across multiple datasets and baselines demonstrate that SlowBA can significantly increase response length and latency while largely preserving task accuracy. The attack remains effective even with a small poisoning ratio and under several defense settings. These findings reveal a previously overlooked security vulnerability in GUI agents and highlight the need for defenses that consider both action correctness and response efficiency. Code can be found in https://github.com/tu-tuing/SlowBA.

cs.CR↗

arXiv2026

Observation of Chiral Bound States in the Continuum in Self-biased Magneto-optical Photonic Crystals

Chiral bound states in the continuum (BICs) are confined photonic modes with infinite quality factors and chiral response, offering significant potential for chiral optics. Although a novel type of spin-orbital-locking chiral BIC was recently predicted in magneto-optical (MO) photonic crystals (PhCs) that break time-reversal symmetry (TRS), its experimental realization has remained elusive. Here, we report the first experimental observation of such chiral BICs in self-biased MO PhCs, which operate without external magnetic fields. Moreover, we experimentally demonstrate that the chirality and the surrounding near-circular polarization of these chiral BICs can be switched simply by reversing the remanent magnetization, without any structural changes. Unlike conventional chiral BICs that preserve TRS, these magnetically induced chiral BICs exhibit exceptional robustness against structural imperfections and perturbations. This work represents a significant advancement in the topological photonics of chiral BICs, opening new pathways toward robust chiral optical devices.

physics.optics↗

arXiv2026

Pressure-induced reentrant superconductivity in a misfit layered compound $\mathrm{(SnS)_{1.15}(TaS_2)}$

Misfit layered compounds are natural van der Waals heterostructures in which electronically active transition-metal dichalcogenide layers are decoupled by incommensurate blocking layers, enabling bulk realization of quasi-two-dimensional quantum states. Here we investigate the superconducting, transport,and structural properties of the misfit compound $\mathrm{(SnS)_{1.15}(TaS_2)}$ under pressures up to 150 GPa. The low-pressure superconducting phase is gradually suppressed and disappears near 14.7 GPa,accompanied by increasing residual resistance. Remarkably, a distinct superconducting phase reemerges above 80 GPa and persists to the highest pressures achieved. This reentrant superconductivity follows a pressure-induced sign reversal of the Hall coefficient near 60 GPa and a nonmonotonic evolution of the normal-state resistance, indicating an electronic reconstruction. No structural phase transition is detected over the entire pressure range. Our results demonstrate a pressure-driven electronic reconstruction leading to reentrant superconductivity in a misfit layered compound, establishing pressure as an effective route to engineer superconductivity and electronic states in natural van der Waals heterostructures.

cond-mat.supr-con↗

arXiv2026

An AI Framework for Microanastomosis Motion Assessment

Proficiency in microanastomosis is a fundamental competency across multiple microsurgical disciplines. These procedures demand exceptional precision and refined technical skills, making effective, standardized assessment methods essential. Traditionally, the evaluation of microsurgical techniques has relied heavily on the subjective judgment of expert raters. They are inherently constrained by limitations such as inter-rater variability, lack of standardized evaluation criteria, susceptibility to cognitive bias, and the time-intensive nature of manual review. These shortcomings underscore the urgent need for an objective, reliable, and automated system capable of assessing microsurgical performance with consistency and scalability. To bridge this gap, we propose a novel AI framework for the automated assessment of microanastomosis instrument handling skills. The system integrates four core components: (1) an instrument detection module based on the You Only Look Once (YOLO) architecture; (2) an instrument tracking module developed from Deep Simple Online and Realtime Tracking (DeepSORT); (3) an instrument tip localization module employing shape descriptors; and (4) a supervised classification module trained on expert-labeled data to evaluate instrument handling proficiency. Experimental results demonstrate the effectiveness of the framework, achieving an instrument detection precision of 97%, with a mean Average Precision (mAP) of 96%, measured by Intersection over Union (IoU) thresholds ranging from 50% to 95% (mAP50-95).

cs.CV↗

arXiv2026

Excitation Energy Transfer in Nanohybrid System of Organic Molecule and Inorganic Transition Metal Dichalcogenides Nanoflake

Excitation energy transfer (EET) in an organic/inorganic nanohybrid system, composed of a single \textit{para}-sexiphenyl (6P) molecule physisorbed on a finite-sized MoS$_2$ nanoflake, is investigated theoretically. % The electronic structure of the MoS$_2$ nanoflake is described by using an 11-band tight-binding model, in which edge states are passivated with H atoms to restore a well-defined bandgap. % Within a configuration-interaction scheme, excitonic states are constructed and, for computational efficiency, approximated by uncorrelated electron-hole pairs in the relevant high-energy window. % The EET rates are evaluated via Fermi's golden rule, incorporating Coulomb coupling, thermal broadening, and spectral overlap between the molecular excitation and the MoS$_2$ nanoflake's electron-hole pairs. % Our results reveal that energy transfer from the molecule to the nanoflake is the dominant process, and its efficiency depends strongly on the size of the MoS$_2$ nanoflake, as well as the molecule's vertical distance and lateral position relative to the nanoflake.

cond-mat.mes-hall↗

arXiv2026

Do Language Models Reason Across Languages?

The real-world information sources are inherently multilingual, which naturally raises a question about whether language models can synthesize information across languages. In this paper, we introduce a simple two-hop question answering setting, where answering a question requires making inferences over two multilingual documents. We find that language models are more sensitive to language variation in answer-span documents than in those providing bridging information, despite the equal importance of both documents for answering a question. Under a step-by-step sub-question evaluation, we further show that in up to 33% of multilingual cases, models fail to infer the bridging information in the first step yet still answer the overall question correctly. This indicates that reasoning in language models, especially in multilingual settings, does not follow a faithful step-by-step decomposition. Subsequently, we show that the absence of reasoning decomposition leads to around 18% composition failure, where both sub-questions are answered correctly but fail for the final two-hop questions. To mitigate this, we propose a simple three-stage SUBQ prompting method to guide the multi-step reasoning with sub-questions, which boosts accuracy from 10.1% to 66.5%.

cs.CL↗

arXiv2025

Kinematic-Based Assessment of Surgical Actions in Microanastomosis

Proficiency in microanastomosis is a critical surgical skill in neurosurgery, where the ability to precisely manipulate fine instruments is crucial to successful outcomes. These procedures require sustained attention, coordinated hand movements, and highly refined motor skills, underscoring the need for objective and systematic methods to evaluate and enhance microsurgical training. Conventional assessment approaches typically rely on expert raters supervising the procedures or reviewing surgical videos, which is an inherently subjective process prone to inter-rater variability, inconsistency, and significant time investment. These limitations highlight the necessity for automated and scalable solutions. To address this challenge, we introduce a novel AI-driven framework for automated action segmentation and performance assessment in microanastomosis procedures, designed to operate efficiently on edge computing platforms. The proposed system comprises three main components: (1) an object tip tracking and localization module based on YOLO and DeepSORT; (2) an action segmentation module leveraging self-similarity matrix for action boundary detection and unsupervised clustering; and (3) a supervised classification module designed to evaluate surgical gesture proficiency. Experimental validation on a dataset of 58 expert-rated microanastomosis videos demonstrates the effectiveness of our approach, achieving a frame-level action segmentation accuracy of 92.4% and an overall skill classification accuracy of 85.5% in replicating expert evaluations. These findings demonstrate the potential of the proposed method to provide objective, real-time feedback in microsurgical education, thereby enabling more standardized, data-driven training protocols and advancing competency assessment in high-stakes surgical environments.

cs.CV↗