arXiv subjects

Hanqing Liu

Publications and source records attributed to Hanqing Liu.

At least 19 recordsLinked to original sources

arXiv2026

On-chip nanoplasma for adaptive electromagnetic protection

Over the past decade, semiconductor diodes have served as the primary switching elements in adaptive electromagnetic (EM) protection, yet their performance has been compromised by parasitic effects and thermal accumulation, rendering them inadequate against the rapidly evolving landscape of high-power microwave (HPM) threats. Here we show that on-chip nanoplasma switches (NPMS), composed of gallium nitride electrodes on silicon carbide substrates, exhibit superior radio frequency (RF) and thermal characteristics, positioning them as ideal field-driven switches in RF front-end protectors. By integrating NPMS into metasurfaces, antennas and circuit limiters, we achieve an adaptive response that ensures low-loss transmission for normal signals and high shielding against HPMs, while offering extended operating bandwidth and substantially higher tolerance than conventional solid-state devices. This robust, nanoscale structure has significant potential for protecting unmanned aerial vehicles, radars, satellites and other highly integrated platforms requiring strength and stability in EM environments. The findings of this study open up new routes to support EM safety of high-precision detection and imaging for next-generation RF front ends, with straightforward scalability to millimetre-wave and terahertz frequencies.

physics.app-ph↗

arXiv2026

SkillReason: Reasoning-Enhanced Agent Skill Retrieval for Implicit User Requests

Large language model agents increasingly rely on reusable skills to extend their capabilities beyond parametric knowl- edge. However, retrieving the appropriate skill from a large- scale library remains challenging because realistic user re- quests are often concise and underspecified, stating only the task goal while leaving the required capabilities and execu- tion steps implicit. Existing benchmarks provide limited cov- erage of such requests. To address this gap, we introduce SkillReason-Bench, a large-scale cross-domain benchmark containing 3,729 queries and a retrieval corpus of 61,228 skills spanning nine domains. We further propose SkillRea- son, a two-stage framework that uses chain-of-thought rea- soning as training-time supervision for skill retrieval. In Stage I, capability reasoning traces generated by a stronger teacher provide explicit supervision through contrastive learning, re- trieval distribution alignment, and language modeling, en- couraging the retriever to internalize capability reasoning in its query representation. In Stage II, a retrieval-guided GRPO objective encourages the model to explore reasoning trajecto- ries better suited to its own capabilities and more effective for retrieval. At inference, SkillReason directly encodes the orig- inal query without autoregressive CoT generation, preserv- ing efficient query-only retrieval. Extensive experiments on SkillReason-Bench, SkillRet, and SRA-Bench show that Skill- Reason achieves state-of-the-art performance across all three benchmarks, demonstrating that reasoning-enhanced training better bridges the semantic gap between high-level task goals and skill capabilities.

cs.AI↗

arXiv2026

Field-driven nonlinear metasurface: self-adaptive transition between high-selectivity transmission and broadband shielding

The escalating complexity of electromagnetic (EM) environment is posing a significant challenge to the reliability of modern electronic information systems. To address the need for the spatial EM safety of electronic devices, we present a field-driven nonlinear metasurface (NMS) that enables effective protection against out-of-band interference and in-band high-intensity radiation. By constructing a reconfigurable hybrid coupling topology and mapping it to the metasurface geometry, the proposed NMS achieves a self-adaptive transition between its transmission and shielding mode depending on the incident power. The experimental results are in good agreement with theoretical analysis and full-wave simulation. We obtain a highly selective passband with roll-off rate larger than 20.6dB/GHz and a broadband shielding with shielding effectiveness exceeding 23.6dB and 60% bandwidth, demonstrating a significantly enhanced performance relative to the literature. Our findings establish a promising route toward comprehensive EM protection on radio frequency front-end systems.

physics.optics↗

arXiv2026

Structured Cavity Quantum Electrodynamics

A cavity quantum electrodynamics (cQED) system consisting of a confined single photon and a single quantum emitter serves as a fundamental block for quantum optics and photonic quantum technologies. The canonical optical mode employed in the conventional cavity quantum electrodynamics features a uniform polarization distribution, leading to the scalar light-matter interaction in most existing experiments. Despite the rapid progress in the generation of structured light with spatially varied polarizations, the structured light-matter interaction, especially at the single quanta level, is highly intriguing yet largely unexplored. Here, we present the structured light-matter interaction at the single-photon level in a semiconductor cavity quantum electrodynamics system. Four distinct structured cavity modes that are spectrally close to each other are constructed in a micropillar cavity. By spatially locating a single epitaxial quantum dot (QD) at the periphery of a semiconductor micropillar cavity and spectrally tuning the QD emission wavelength into the resonances of the structured cavity modes, cavity-enhanced single-photon emissions with spin-locked chiral orbital angular momentum (OAM) and engineerable spin-orbit entanglements are achieved within a single wavelength-scale device. Our work opens an unexplored paradigm of structured quantum light-matter interactions and may further advance chiral quantum optics and high-dimensional photonic quantum technology.

physics.optics↗

arXiv2026

Experimental quantum cryptography with single photons and imperfect devices

Quantum key distribution (QKD) allows for provably secure key distribution between two trusted parties. Because the security and performance of QKD protocols rely on devices that behave according to specific assumptions, idealized or inaccurate assumptions about device behavior can introduce security loopholes. Real devices can never be perfectly characterized, and their performance metrics are always subject to certain error margins, which must be accounted for in a rigorous theoretical analysis. Only recently have rigorous finite-size results allowed for imperfect characterizations of devices (where device parameter have uncertainty margins) - an advance yet to be considered in experimental implementations of the BB84 protocol. In this work, we prove the security and analyze the performance of an implementation of the BB84 protocol using single photons generated by a semiconductor quantum dot light source in combination with dynamic polarization-state encoding. We consider the presence of incompletely characterized devices by accounting for imperfections in the single-photon source (in terms of finite g(2)(0)) as well as the receiver (non-ideal beam-splitters, finite detector efficiencies, and dark counts), all with error margins. The resulting protocol implementation shows competitive performance, paving the way towards practical and loop-hole free implementations of QKD.

quant-ph↗

arXiv2026

Cordon: Semantic Transactions for Tool-Using LLM Agents

Tool-using LLM agents are shifting the unit of computation from explicit human-issued commands to model-driven tasks with stateful consequences. Yet today's agent runtimes still expose tools as isolated RPCs. This interface gives runtimes a convenient integration point, but it lacks a task-scoped execution boundary for commit, rollback, recovery, and audit across multi-step agent workflows. We argue that this mismatch calls for a runtime containment boundary rather than another per-call guardrail. This paper introduces Cordon, a transactional runtime system for staging and validating irreversible agent effects before commit. A semantic transaction is a task-level execution boundary that binds tool intents and runtime-tracked result lineage to reversible local state, staged external effects, delegated authority, and audit metadata. Cordon implements this abstraction with a transaction manager that tracks derived result objects, executes reversible mutations in shadow state, stages outward-facing actions in an effect outbox, and records recovery metadata. The runtime then validates the composed execution flow before it commits state or releases external effects. Our evaluation across adversarial and benign workflows shows that Cordon exposes cross-step violations missed by existing defenses. It also reduces irreversible-effect failures while preserving benign task completion with modest approval and latency overhead.

cs.OS↗

arXiv2026

RE-VLM: Event-Augmented Vision-Language Model for Scene Understanding

Conventional vision-language models (VLMs) struggle to interpret scenes captured under adverse conditions (e.g., low light, high dynamic range, or fast motion) because standard RGB images degrade in such environments. Event cameras provide a complementary modality: they asynchronously record per-pixel brightness changes with high temporal resolution and wide dynamic range, preserving motion cues where frames fail. We propose RE-VLM, the first dual-stream vision-language model that jointly leverages RGB images and event streams for robust scene understanding across both normal and challenging conditions. RE-VLM employs parallel RGB and event encoders together with a progressive training strategy that aligns heterogeneous visual features with language. To address the scarcity of RGB-Event-Text supervision, we further propose a graph-driven pipeline that converts synchronized RGB-Event streams into verifiable scene graphs, from which we synthesize captions and question-answer (QA) pairs. To develop and evaluate RE-VLM, we construct two datasets: PEOD-Chat, targeting illumination-challenged scenes, and RGBE-Chat, covering diverse scenarios. On captioning and VQA benchmarks, RE-VLM consistently outperforms state-of-the-art RGB-only and event-only models with comparable parameter counts, with particularly large gains under challenging conditions. These results demonstrate the effectiveness of event-augmented VLMs in achieving robust vision-language understanding across a wide range of real-world environments.

cs.CV↗

arXiv2026

DSAA: Dual-Stage Attribute Activation for Fine-grained Open Vocabulary Detection

Open-Vocabulary Object Detection (OVD) models break the limitations of closed-set detection, enabling the identification of unseen categories through natural language prompts. However, they exhibit notable limitations in fine-grained detection tasks involving attributes like color, material, and texture. We attribute this performance bottleneck in OVD models to a core issue: when category signals dominate, OVD models tend to marginalize attribute information during inference. This leads to incorrect binding between attributes and target objects. To address this, we propose the Dual-Stage Attribute Activation (DSAA) framework, which enhances fine-grained detection capabilities by strengthening attribute semantics at two critical stages. In the text embedding stage, we employ Attribute Prefix Adapter (APA) module to generate attribute prefixes that inject explicit attribute priors. To further amplify the influence of these attributes, our Key/Value (K/V) Modulator module then intervenes during the BERT encoding phase, selectively enhancing the Key and Value vectors of the corresponding attribute tokens. In addition, we introduce an attribute-aware contrastive loss to improve discrimination among same-category instances with different attributes during training. Experimental results on the FG-OVD benchmark demonstrate the effectiveness of our method across various mainstream open-vocabulary models.

cs.CV↗

arXiv2026

JailWAM: Jailbreaking World Action Models in Robot Control

World Action Models (WAMs) have emerged as a promising paradigm for robotic manipulation, enabling physical interaction across diverse tasks and environments. However, their ability to directly follow high-level instructions and execute physical actions also creates potential safety risks, as adversarially designed instructions may induce unsafe robot behaviors. To systematically assess these risks, we propose JailWAM, the first jailbreak evaluation framework for WAMs. In JailWAM, we integrate three key innovations: Firstly, to address the difficulty of evaluating heterogeneous low-level action outputs, we introduce Visual-Trajectory Mapping, which transforms model-specific actions into unified visual trajectory representations, thereby facilitating consistent risk assessment across WAM architectures. Secondly, to provide efficient and fine-grained assessment of physical risks, we develop a Risk Discriminator supervised by three safety levels ordered according to physical consequence: Safety Compliance, Motion Failure, and Catastrophic Risk. This severity-aware formulation enables the risk discriminator to distinguish different physical outcomes from visual trajectories and support scalable risk screening. Thirdly, to reduce the cost of exhaustively executing adversarial candidates, we design a Dual-Path Verification Strategy that combines rapid risk screening with closed-loop physical simulation, restricting computationally expensive verification to candidates with potential safety risks. Extensive experiments in the RoboTwin simulation environment show that JailWAM achieves an 84.2% attack success rate on LingBot-VA, which indicates that WAMs may be susceptible to jailbreak attacks that induce unsafe physical behaviors. Our findings may motivate further research on the safety evaluation and alignment of future embodied robotic systems.

cs.RO↗

arXiv2026

Engineering energy-time entanglement from resonance fluorescence

Resonance fluorescence from a coherently driven two-level emitter is a minimal quantum optical field that combines phase coherence with single-photon-level nonlinearity. Here we show that it can be engineered, using only passive linear interferometry, into energy-time entanglement. By injecting resonance fluorescence from a single quantum dot into an asymmetric Mach--Zehnder interferometer operated near destructive interference of the single-photon component, we generate an output field whose coincidence statistics are dominated by the simultaneous two-photon contribution |2> and the temporally separated photon-pair contribution |11>. In a Franson geometry, these two sectors are resolved on the coincidence-delay axis, and both exhibit high-visibility nonlocal interference fringes and violate the Clauser--Horne--Shimony--Holt Bell inequality. Our results reveal a general route for engineering entanglement from resonance fluorescence using passive optics.

quant-ph↗

arXiv2026

High-performance Sources of Multidimensionally Engineered Quantum Light Based on Monolithic Microcavity-metalens Interfaces

The ultimate non-classic light sources for modern photonic quantum technology require on-demand generation of indistinguishable quantum light with high brightness and flexible engineering of quantum emission in multiple degrees of freedom. In this work, we present monolithic microcavity-metalens interfaces consisting of quantum-dot-micropillar single-photon sources and ultra-thin metalenses accurately aligned on opposite sides of an III-V compound semiconductor chip. The pronounced cavity quantum electrodynamics effect enabled by the micropillar cavity facilitates single-photon emission from quantum dots with simultaneous high degrees of single-photon purity, source brightness and photon indistinguishability while the multi-functional metalenses concurrently tailor quantum emission in multiple physical degrees of freedom including radiation divergence, emission directionality, polarization state and orbital angular momentum (OAM). Furthermore, high-fidelity polarization-OAM entanglement and single photons with local spin topologies are successfully generated in our integrated device. In particular, we demonstrate stable propagations of single-photon skrymions in atmospheric turbulence and reveal their topological advantages over the conventional structured quantum light. Our work advances the research fields of integrated quantum photonics and meta-optics, providing integrated high-dimensional quantum light sources for advanced photonic quantum science and technology.

physics.optics↗

arXiv2026

Passive Daytime Radiative Cooling Enabled by Bio-Derived Ceramic-Polymer Coatings on Rapid-Curing Fiberglass Casts

Passive daytime radiative cooling (PDRC) provides an energy-free approach to suppress surface temperatures by reflecting solar irradiation while emitting thermal radiation through the mid-infrared atmospheric window. Despite rapid progress in optical performance, most PDRC systems remain limited by rigid, fragile, or planar substrates, restricting their use on flexible, curved, or wearable surfaces. Here, we report a biocompatible and structurally robust PDRC system integrated onto a commercial rapid-curing fiberglass cast, a conformal substrate widely used in orthopedic and industrial applications. The cooling architecture adopts a bilayer polymer design consisting of a polyvinyl alcohol (PVA) adhesion layer and a polymethyl methacrylate (PMMA) protective layer, both embedded with calcium pyrophosphate (CPP) ceramic particles derived from processed animal bone waste. The bio-derived CPP simultaneously enables broadband solar scattering and high mid-infrared emittance, while offering sustainability and biocompatibility advantages. The resulting composite exhibits over 90% solar reflectance and achieves up to 15 C sub-ambient cooling under direct outdoor sunlight.

physics.optics↗

arXiv2025

Bell Inequality Violation with Vacuum-One-Photon Number Superposition States

Entanglement is a central resource in quantum technologies, and the realization of photonic entanglement necessarily relies on interaction with matter. Resonance fluorescence (RF), originating from the coherent interaction between a driving field and a two-level system, plays a pivotal role in quantum optics. Here, we demonstrate a novel route to entanglement generation based on RF from a single quantum dot. Rather than relying on generation of multiphoton states, our approach directly exploits vacuum-one-photon number superposition states created under resonant excitation. By delocalizing this superposition via a beam splitter, we realize time-bin entanglement and observe a clear violation of the Clauser-Horn-Shimony-Holt Bell inequality using Franson-type interferometry. Our scheme removes the need for multiphoton generation, simplifies the experimental requirements, and establishes a scalable pathway toward solid-state entangled photon sources.

quant-ph↗

arXiv2025

Thermally Activated Dual-Modal Adversarial Clothing against AI Surveillance Systems

Adversarial patches have emerged as a popular privacy-preserving approach for resisting AI-driven surveillance systems. However, their conspicuous appearance makes them difficult to deploy in real-world scenarios. In this paper, we propose a thermally activated adversarial wearable designed to ensure adaptability and effectiveness in complex real-world environments. The system integrates thermochromic dyes with flexible heating units to induce visually dynamic adversarial patterns on clothing surfaces. In its default state, the clothing appears as an ordinary black T-shirt. Upon heating via an embedded thermal unit, hidden adversarial patterns on the fabric are activated, allowing the wearer to effectively evade detection across both visible and infrared modalities. Physical experiments demonstrate that the adversarial wearable achieves rapid texture activation within 50 seconds and maintains an adversarial success rate above 80\% across diverse real-world surveillance environments. This work demonstrates a new pathway toward physically grounded, user-controllable anti-AI systems, highlighting the growing importance of proactive adversarial techniques for privacy protection in the age of ubiquitous AI surveillance.

cs.AI↗

arXiv2025

PEOD: A Pixel-Aligned Event-RGB Benchmark for Object Detection under Challenging Conditions

Robust object detection for challenging scenarios increasingly relies on event cameras, yet existing Event-RGB datasets remain constrained by sparse coverage of extreme conditions and low spatial resolution (<= 640 x 480), which prevents comprehensive evaluation of detectors under challenging scenarios. To address these limitations, we propose PEOD, the first large-scale, pixel-aligned and high-resolution (1280 x 720) Event-RGB dataset for object detection under challenge conditions. PEOD contains 130+ spatiotemporal-aligned sequences and 340k manual bounding boxes, with 57% of data captured under low-light, overexposure, and high-speed motion. Furthermore, we benchmark 14 methods across three input configurations (Event-based, RGB-based, and Event-RGB fusion) on PEOD. On the full test set and normal subset, fusion-based models achieve the excellent performance. However, in illumination challenge subset, the top event-based model outperforms all fusion models, while fusion models still outperform their RGB-based counterparts, indicating limits of existing fusion methods when the frame modality is severely degraded. PEOD establishes a realistic, high-quality benchmark for multimodal perception and facilitates future research.

cs.CV↗

arXiv2025

Eva-VLA: Evaluating Vision-Language-Action Models' Robustness Under Real-World Physical Variations

Vision-Language-Action (VLA) models have emerged as promising solutions for robotic manipulation, yet their robustness to real-world physical variations remains critically underexplored. To bridge this gap, we propose Eva-VLA, the first unified framework to systematically evaluate the robustness of VLA models by formulating uncontrollable physical variations as continuous optimization problems. Specifically, our framework addresses two fundamental challenges in VLA models' physical robustness evaluation: 1) how to systematically characterize diverse physical perturbations encountered in real-world deployment while maintaining reproducibility, and 2) how to efficiently discover worst-case scenarios without incurring prohibitive real-world data collection costs. To tackle the first challenge, we decouple real-world variations into three key dimensions: 3D object transformations that affect spatial reasoning, illumination changes that challenge visual perception, and adversarial regions that disrupt scene understanding. For the second challenge, we introduce a continuous black-box optimization mechanism that maps these perturbations into a continuous parameter space, enabling the systematic exploration of worst-case scenarios. Extensive experiments validate the effectiveness of our approach. Notably, OpenVLA exhibits an average failure rate of over 90% across three physical variations on the LIBERO-Long task, exposing critical systemic fragilities. Furthermore, applying the generated worst-case scenarios during adversarial training quantifiably increases model robustness, validating the effectiveness of this approach. Our evaluation exposes the gap between laboratory and real-world conditions, while the Eva-VLA framework can serve as an effective data augmentation method to enhance the resilience of robotic manipulation systems.

cs.RO↗

arXiv2025

Optomechanical method for characterizing thermal transport across van der Waals interfaces

For the development of nanoscale electronics and photonics using atomically thin two-dimensional (2D) materials, it is important to realize van der Waals (vdW) interfaces with low thermal resistance, to minimize performance reduction caused by heat accumulation. However, characterizing the thermal interface resistance between vdW materials is still a challenge. Here, we introduce a novel optomechanical methodology to characterize the thermal transport across interfaces in 2D heterostructures. We first determine the specific heat and thermal conductivity as the function of temperature for the upper and lower material layers separately and then extract the thermal boundary conductance (TBC) of the heterostructure from its thermal time constant. We obtain a TBC of $2.41 \pm 1.03$ and $4.14 \pm 1.74$~\si{MW m^{2} K^{-1}} for FePS$_3$/WSe$_2$ and MoS$_2$/FePS$_3$ interfaces, respectively, which are comparable to values reported in the literature. Moreover, they agree with a Debye model including the acoustic impedance mismatch of flexural phonons. This work enables efficient thermal management down to the nanoscale and offers new insights into energy dissipation in vdW heterostructures.

physics.app-ph↗

arXiv2025

Mechanical Reinforcement of Graphene via Wrinkling

Mechanical cantilevers are central to nanotechnology, with ultimate sensitivity achieved at the atomic limit, where low bending rigidity makes stability the fundamental challenge. Here, we introduce a wrinkle-induced stiffening approach that enhances the bending rigidity of monolayer graphene by several orders of magnitude, enabling the fabrication of mechanically robust graphene cantilevers. When suspended over microcavities, these wrinkled membranes exhibit significant increases in both in-plane and out-of-plane stiffness, as confirmed by nanoindentation and resonance measurements, which also reveal that enhanced bending rigidity strongly influences their vibrational response. This behavior marks a transition from tension-dominated mechanics to a regime where bending effects become prominent, even in a single atomic layer. By sculpting these structures, we realize graphene cantilevers with measured bending rigidities between $10^6$ and $10^7$ eV, while maintaining femtogram-scale mass. These findings open new directions in nanomechanical sensing and cantilever-based technologies.

cond-mat.mes-hall↗