arXiv subjects

Kai Wei

Publications and source records attributed to Kai Wei.

At least 19 recordsLinked to original sources

arXiv2026

Chameleon: Robust Defense Against Tor Website Fingerprinting via Many-to-Many Traffic Morphing

Website fingerprinting (WF) attacks can infer users' browsing activities from encrypted Tor traffic by exploiting side-channel features. Although many WF defenses have been proposed, we find that most existing defenses create learnable web trace mapping features. We further show that robustness against adversarial training does not necessarily imply robustness against defense-aware autoencoder (DAAE)-based attacks. To address these limitations, we present Chameleon, a robust WF defense based on many-to-many randomized traffic morphing. Chameleon selects morphing candidates with high intra-class diversity and low inter-class disparity. Chameleon randomly maps each webpage trace to multiple candidates, and allows different webpages to share morphing targets, thereby increasing adversarial uncertainty. For practical Tor deployment, Chameleon introduces a radix-trie-based synchronization mechanism that enables pluggable transport (PT) endpoints to identify consistent morphing traces using packet-direction prefixes, together with trace mutation and normalized prefix matching to reduce overhead. We evaluate Chameleon against six state-of-the-art defenses and five WF attacks on three public datasets in closed- and open-world settings. Compared with Adaptive Tamaraw, Chameleon reduces adversarial-training-based attack accuracy by up to 36.74% while reducing bandwidth and time overhead by 34.12% and 60.38%, respectively. Under DAAE-based RF attacks on GTT23, Chameleon limits attack performance to 35.19% F1-score while Adaptive Tamaraw only limits it to 88.22% F1-score. In the real-world PT bridge evaluation, Chameleon substantially reduces the effectiveness of strong WF attacks while incurring only 16.25% time overhead.

cs.CR↗

arXiv2026

Electro-Optic Active Metasurfaces for High-Speed Photonic Applications

Metasurfaces are artificially engineered ultrathin nanostructured surfaces, capable of flexibly manipulating light-matter interactions on compact platforms, and thereby of great significance for a wide range of applications within modern optics and photonics, including communications, computing, sensing, and quantum technologies. However, the inherently static nature of conventional metasurfaces severely limits their functionalities and thus range of possible applications. Benefiting from integration of the metasurface platform for shaping optical wavefronts with ultrafast electro-optic (EO) materials, active EO metasurfaces have emerged as a frontier research direction targeting advanced photonic devices. This paper systematically reviews the latest progress in this field, featuring a comprehensive comparison of performances and application scenarios of mainstream EO materials such as lithium niobate, barium titanate and organic EO polymers. Modulation mechanisms based on the Pockels and Kerr effects along with the corresponding active metasurface implementations are summarized. Furthermore, improvements in modulation efficiency enabled by advantageously exploiting resonant structural designs and associated phenomena, including Fabry-Perot resonances, Mie resonances, surface plasmon polaritons, quasi-bound states in the continuum, surface lattice resonances, and guided-mode resonances, are presented and summerized in detail. Current challenges related to metasurface design, nanofabrication, performance and heterogeneous integration are also discussed. Finally, future research directions are outlined, highlighting interdisciplinary developments, novel material engineering, and AI-assisted design as key pathways to enable practical use of active EO metasurfaces in modern optics and photonics, including quantum information technologies.

physics.optics↗

arXiv2026

Large-Aperture All-Solid-State Cascaded Liquid-Crystal Beam Steering for High-Resolution Wide-Field Imaging

High-resolution wide-field imaging is essential for applications requiring simultaneous global coverage and local detail, yet conventional approaches face a fundamental trade-off: wide-FOV cameras sacrifice spatial sampling density by distributing finite detector pixels over a broad angular range, while telephoto systems resolve fine features at the cost of scene coverage. Beam-steering devices can mitigate this trade-off but are currently limited in achieving simultaneously all-solid-state, large aperture, and high-speed operation. Here, we report an all-solid-state large-aperture cascaded liquid-crystal beam-steering (CaLiBS) imaging system that extends the effective angular range of a high-resolution narrow-FOV camera by electrically steering sub-FOVs. The CaLiBS module comprises cascaded liquid crystal waveplates and liquid crystal Pancharatnam-Berry phase gratings; a theoretical voltage-prediction model with a hierarchical search algorithm enables efficient calibration under oblique incidence and 10 times faster calibration speed compared with conventional methods. The calibrated system addresses sub-FOVs across 30.3{\deg} * 30.3{\deg} at 2{\deg} intervals with diffraction efficiency above 60%. Sequential sub-FOV acquisition reconstructs a 34.7 * 34.7 composite image, an 8.6-fold enhancement in spatial-bandwidth product over a single-shot wide-FOV camera using the same detector. Combined with object tracking methods, sub-FOV switching further enables high-resolution tracking of moving vehicles within the wide-area scene. This cascaded LC architecture offers a scalable pathway toward compact, vibration-free, and high-resolution wide-field observation.

physics.optics↗

arXiv2026

Sliding ferroelectricity tunable conventional and anomalous spin Hall effects in bilayer 1T'-WTe2

The spin Hall effect, recognized for its high-speed, low-power, and highly controllable characteristics, is a key enabler for next-generation memory and logic devices. However, a primary challenge lies in achieving 180$^{\circ}$ magnetization switching without an external magnetic field in spin-orbit torque devices. Here, we propose a method to tune the conventional and anomalous spin Hall effects by the intrinsic sliding ferroelectricity. Importantly, the anomalous spin Hall effect can enable the field-free switching of perpendicular magnetization. We find a substantial anomalous spin Hall conductivity of $\sigma_{xy}^{y}$ = 45.62 ($\hbar$/e)S/cm and $\sigma_{yx}^{y}$ = 56.84 ($\hbar$/e)S/cm in monolayer 1T'-WTe$_2$. These values are significantly enhanced to $\sigma_{xy}^{y}$ = -96.77 ($\hbar$/e)S/cm and $\sigma_{yx}^{y}$ = 104.03 ($\hbar$/e)S/cm in the bilayer 1T'-WTe$_2$. More interestingly, the sliding ferroelectricity enables reversible switching of the signs and magnitudes for both the conventional and anomalous spin Hall conductivities. This originates from the fact that the sliding ferroelectric markedly shifts the relative spin Berry curvature contributions from the valence and conduction bands around the $\Gamma$-X path. Our findings not only reveal a strong coupling between sliding ferroelectricity and spin transport, but also propose a strategy for the nonvolatile electrical control of spintronic devices.

cond-mat.mtrl-sci↗

arXiv2026

Physics-Guided Policy Optimization with Self-Distillation

Self-distilled policy optimization (SDPO) has become a popular paradigm for LLM post-training, where a model learns from its own predictions conditioned on privileged information. SDPO, however, is sensitive to how much each update step should be trusted: corrections from a self-teacher can be highly informative on some batches and misleading on others, and applying them uniformly with a fixed step size can destabilize training. Drawing inspiration from viscous-fluid dynamics and formalizing the analogy at the SDE level, we propose Physics-Guided Policy Optimization (PGPO), which introduces an information-modulated step-size multiplier derived from a mutual-information estimate between the student's predictions and the feedback-conditioned teacher. We show that this modulation preserves the order-1 weak-approximation guarantees of vanilla SGD, and incurs negligible overhead per iteration. We evaluate PGPO on the Science-QA dataset, where it outperforms SDPO on 3 of the 4 domains with gains of up to +4.5 points, while remaining stable in a setting where SDPO collapses late in training.

cs.LG↗

arXiv2026

Skill-RAG: Failure-State-Aware Retrieval Augmentation via Hidden-State Probing and Skill Routing

Retrieval-Augmented Generation (RAG) has emerged as a foundational paradigm for grounding large language models in external knowledge. While adaptive retrieval mechanisms have improved retrieval efficiency, existing approaches treat post-retrieval failure as a signal to retry rather than to diagnose -- leaving the structural causes of query-evidence misalignment unaddressed. We observe that a significant portion of persistent retrieval failures stem not from the absence of relevant evidence but from an alignment gap between the query and the evidence space. We propose Skill-RAG, a failure-aware RAG framework that couples a lightweight hidden-state prober with a prompt-based skill router. The prober gates retrieval at two pipeline stages; upon detecting a failure state, the skill router diagnoses the underlying cause and selects among four retrieval skills -- query rewriting, question decomposition, evidence focusing, and an exit skill for truly irreducible cases -- to correct misalignment before the next generation attempt. Experiments across multiple open-domain QA and complex reasoning benchmarks show that Skill-RAG substantially improves accuracy on hard cases persisting after multi-turn retrieval, with particularly strong gains on out-of-distribution datasets. Representation-space analyses further reveal that the proposed skills occupy structured, separable regions of the failure state space, supporting the view that query-evidence misalignment is a typed rather than monolithic phenomenon.

cs.CL↗

arXiv2026

Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for post-training reasoning models. However, group-based methods such as Group Relative Policy Optimization (GRPO) face a critical dilemma in sparse-reward settings: pure Reinforcement Learning (RL) suffers from advantage collapse and high-variance gradient estimation, while mixed-policy optimization introduces persistent distributional bias. To resolve this dilemma, we introduce Hindsight-Anchored Policy Optimization (HAPO). HAPO employs the Synthetic Success Injection (SSI) operator, a hindsight mechanism that selectively anchors optimization to teacher demonstrations during failure. This injection is governed by a Thompson sampling-inspired gating mechanism, creating an autonomous, self-paced curriculum. Theoretically, we demonstrate that HAPO achieves \textit{asymptotic consistency}: by naturally annealing the teacher signal as the policy improves, HAPO recovers the unbiased on-policy gradient. This ensures off-policy guidance acts as a temporary scaffold rather than a persistent ceiling, enabling the model to surpass the limitations of static teacher forcing.

cs.LG↗

arXiv2026

Earth Matter Enhanced Axion Dark Matter Search

Laboratory searches for ultralight axion dark matter (DM) have traditionally assumed the terrestrial density of axions is equal to the average density of DM in the solar system. However, quadratic couplings to matter introduce a non-trivial field profile near the Earth. In this work, we present the first dedicated experimental implementation of this environment-aware axion DM wind search framework. Leveraging the extreme sensitivity of a K--Rb--$^{21}$Ne comagnetometer to pseudo-magnetic fields induced by axion DM, we analyzed our data in the context of the massively enhanced local gradient of axions due to interactions with matter, though no signal candidates were found. Consequently, we have set the most stringent limits on axion-neutron derivative interactions for masses $m_a \in [0.041, ~28.9]~\rm feV$, improving from previous experiments that ignore terrestrial matter effects by as much as three orders of magnitude for certain masses. Our work highlights the necessity of accounting for environmental modifications in precision frontier experiments and demonstrates how geophysical variations can be harnessed to act as a natural amplifier for DM possibly enabling future detection in parts of the parameter space that were previously beyond reach.

hep-ph↗

arXiv2026

Cavity, lumped-circuit, and spin-based detection of axion dark matter: differences and similarities

Axions and axion-like particles are compelling candidates for ultralight bosonic dark matter, forming coherent oscillating fields that can be probed by experiments known as haloscopes. A broad range of haloscope concepts has been developed, including resonant cavity haloscopes, lumped-element circuit detectors, and spin-based experiments, each sensitive to different axion couplings and mass ranges. Rather than attempting an exhaustive survey of all existing approaches, this comparative review provides a unified framework for the major haloscope classes, establishing a common language for the descriptions of signal generation, noise properties, data analysis, and scanning strategies. Key properties of ultralight bosonic dark matter relevant for detection are summarized first, including coherence time, spectral linewidth, and stochasticity under the standard halo model. The discussion then compares cavity, Earth-scale, lumped-element, and spin haloscopes, focusing on expected signal shapes, dominant noise sources, and statistical frameworks for axion searches. Particular emphasis is placed on consistent definitions of signal-to-noise ratio and on how detector bandwidth, axion coherence, and noise characteristics determine optimal scan strategies. By systematically comparing operating principles and performance metrics across these detector families, this framework clarifies shared concepts as well as the essential differences that govern sensitivity in different mass and coupling regimes. The resulting perspective synthesizes current search methodologies and offers guidance for optimizing future haloscope experiments.

hep-ph↗

arXiv2025

Search for Ultralight Dark Matter with Quantum Magnetometry in the Earth's Cavity

Ultralight dark matter candidates, such as axions and dark photons, are leading dark matter candidates. They may couple feebly to photons, sourcing oscillating electromagnetic signals in the Earth's conducting cavity formed between the ground and the ionosphere, providing detectable magnetic field signatures at wavelengths above the Earth's size. We carry out a project aiming to search for new physics using an unshielded high-sensitivity atomic magnetometer, termed the Geomagnetic Probe for nEw physiCS (GPEX). In this work, we report our first search for axion and dark photon dark matter, conducted in the desert of XiaoDushan in Gansu Province, China. Analysis of the collection of one-hour data shows no robust evidence for axion- or dark photon-induced magnetic signals. Correspondingly, we set the constraints on the axion-photon coupling with $g_{a\gamma\gamma} < 7\times10^{-10}\, \mathrm{GeV^{-1}}$ and the dark photon kinetic-mixing parameter $\epsilon < 2\times10^{-6}$ in the mass range $3.5 \times 10^{-16}\, \mathrm{eV} \sim 1.8 \times 10^{-14}\, \mathrm{eV}$. Our findings demonstrate the feasibility of using ground-based quantum magnetic sensors for ultralight dark matter searches. Future networks of such detectors operating over extended periods could improve the sensitivity by about three orders of magnitude.

hep-ph↗

arXiv2025

Synthetic Data-Driven Prompt Tuning for Financial QA over Tables and Documents

Financial documents like earning reports or balance sheets often involve long tables and multi-page reports. Large language models have become a new tool to help numerical reasoning and understanding these documents. However, prompt quality can have a major effect on how well LLMs perform these financial reasoning tasks. Most current methods tune prompts on fixed datasets of financial text or tabular data, which limits their ability to adapt to new question types or document structures, or they involve costly and manually labeled/curated dataset to help build the prompts. We introduce a self-improving prompt framework driven by data-augmented optimization. In this closed-loop process, we generate synthetic financial tables and document excerpts, verify their correctness and robustness, and then update the prompt based on the results. Specifically, our framework combines a synthetic data generator with verifiers and a prompt optimizer, where the generator produces new examples that exposes weaknesses in the current prompt, the verifiers check the validity and robustness of the produced examples, and the optimizer incrementally refines the prompt in response. By iterating these steps in a feedback cycle, our method steadily improves prompt accuracy on financial reasoning tasks without needing external labels. Evaluation on DocMath-Eval benchmark demonstrates that our system achieves higher performance in both accuracy and robustness than standard prompt methods, underscoring the value of incorporating synthetic data generation into prompt learning for financial applications.

cs.AI↗

arXiv2025

Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptides

Diffusion models have emerged as a leading framework in generative modeling, poised to transform the traditionally slow and costly process of drug discovery. This review provides a systematic comparison of their application in designing two principal therapeutic modalities: small molecules and therapeutic peptides. We dissect how the unified framework of iterative denoising is adapted to the distinct molecular representations, chemical spaces, and design objectives of each modality. For small molecules, these models excel at structure-based design, generating novel, pocket-fitting ligands with desired physicochemical properties, yet face the critical hurdle of ensuring chemical synthesizability. Conversely, for therapeutic peptides, the focus shifts to generating functional sequences and designing de novo structures, where the primary challenges are achieving biological stability against proteolysis, ensuring proper folding, and minimizing immunogenicity. Despite these distinct challenges, both domains face shared hurdles: the scarcity of high-quality experimental data, the reliance on inaccurate scoring functions for validation, and the crucial need for experimental validation. We conclude that the full potential of diffusion models will be unlocked by bridging these modality-specific gaps and integrating them into automated, closed-loop Design-Build-Test-Learn (DBTL) platforms, thereby shifting the paradigm from mere chemical exploration to the on-demand engineering of novel~therapeutics.

cs.LG↗

arXiv2025

A Comprehensive Survey of Website Fingerprinting Attacks and Defenses in Tor: Advances and Open Challenges

The Tor network provides users with strong anonymity by routing their internet traffic through multiple relays. While Tor encrypts traffic and hides IP addresses, it remains vulnerable to traffic analysis attacks such as the website fingerprinting (WF) attack, achieving increasingly high fingerprinting accuracy even under open-world conditions. In response, researchers have proposed a variety of defenses, ranging from adaptive padding, traffic regularization, and traffic morphing to adversarial perturbation, that seek to obfuscate or reshape traffic traces. However, these defenses often entail trade-offs between privacy, usability, and system performance. Despite extensive research, a comprehensive survey unifying WF datasets, attack methodologies, and defense strategies remains absent. This paper fills that gap by systematically categorizing existing WF research into three key domains: datasets, attack models, and defense mechanisms. We provide an in-depth comparative analysis of techniques, highlight their strengths and limitations under diverse threat models, and discuss emerging challenges such as multi-tab browsing and coarse-grained traffic features. By consolidating prior work and identifying open research directions, this survey serves as a foundation for advancing stronger privacy protection in Tor.

cs.CR↗

arXiv2025

SIPDO: Closed-Loop Prompt Optimization via Synthetic Data Feedback

Prompt quality plays a critical role in the performance of large language models (LLMs), motivating a growing body of work on prompt optimization. Most existing methods optimize prompts over a fixed dataset, assuming static input distributions and offering limited support for iterative improvement. We introduce SIPDO (Self-Improving Prompts through Data-Augmented Optimization), a closed-loop framework for prompt learning that integrates synthetic data generation into the optimization process. SIPDO couples a synthetic data generator with a prompt optimizer, where the generator produces new examples that reveal current prompt weaknesses and the optimizer incrementally refines the prompt in response. This feedback-driven loop enables systematic improvement of prompt performance without assuming access to external supervision or new tasks. Experiments across question answering and reasoning benchmarks show that SIPDO outperforms standard prompt tuning methods, highlighting the value of integrating data synthesis into prompt learning workflows.

cs.CL↗

arXiv2025

Dynamically Polarized SERF Atomic Comagnetometer

Atomic spin sensors are essential for beyond-the-standard-model exploration, biomagnetic measurement, and quantum navigation. While the traditional DC mode spin-exchange relaxation-free (SERF) comagnetometer achieves ultrahigh sensitivity, further improvements require suppressing technical noise and surpassing standard quantum limit. In this work, we develop a K-Rb-$^{21}$Ne SERF atomic comagnetometer that dynamically polarizes the electron and nuclear spins, shielding signals from direct interference by pump light. We establish a three-phase evolutionary model for hybrid spin ensemble dynamics, yielding a complete analytical solution, and analyze the responses to various spin perturbations. Additionally, we achieve an averaged 38.5 $\%$ suppression of the polarization noise and identify the key factors that limit sensitivity improvements. The dynamically polarized comagnetometer exhibits effective suppression of technical noise and holds the potential to overcome quantum noise limit, while offering promising applications in exploring new physics and precise magnetic field measurements.

physics.atom-ph↗

arXiv2025

Search for a parity-violating long-range spin-dependent interaction

High-sensitivity quantum sensors are a promising tool for experimental searches for beyond-Standard-Model interactions. Here, we demonstrate an atomic comagnetometer operating under a resonantly-coupled hybrid spin-resonance (HSR) regime to probe P-odd, T-even interactions. The HSR regime enables robust nuclear-electron spin coupling, enhancing measurement bandwidth and stability without compromising the high sensitivity of spin-exchange relaxation-free magnetometers. To minimize vibration noise from velocity-modulated sources, we implement a multistage vibration isolation system, achieving a vibration noise reduction exceeding 700-fold. We establish new constraints on vector-boson-mediated parity-violating interactions, improving experimental sensitivity by three orders of magnitude compared to previous limits. The new constraints complement existing astrophysical and laboratory studies of potential extensions to the Standard Model.

quant-ph↗

arXiv2025

Crypto-ncRNA: a bio-inspired post-quantum cryptographic primitive exploiting RNA folding complexity

The imminent realization of fault-tolerant quantum computing precipitates a systemic collapse of classical public-key infrastructure and necessitates an urgent transition to post-quantum cryptography. However, current standardization efforts predominantly rely on structured mathematical problems that may remain vulnerable to unforeseen algorithmic breakthroughs, highlighting a critical need for fundamentally orthogonal security paradigms. Here, we introduce \emph{Crypto-ncRNA} as a biophysically inspired cryptographic primitive that exploits the thermodynamic complexity of non-coding RNA folding as a computational work-factor amplifier. By leveraging the rugged energy landscape inherent to RNA secondary structure prediction, a problem intractable to rapid inversion, we establish a security foundation independent of conventional number-theoretic assumptions. We validate this approach by mapping the folding problem to a Quadratic Unconstrained Binary Optimization model and demonstrate theoretical resilience against quantum optimization attacks including the Quantum Approximate Optimization Algorithm. Functioning as a symmetric key encapsulation and derivation primitive dependent on pre-shared seeds, Crypto-ncRNA achieves throughputs competitive with software-based Advanced Encryption Standard implementations. By utilizing the generated high-entropy keys within a standard stream cipher framework, it exhibits ciphertext entropy that satisfies rigorous NIST SP 800-22 statistical standards. These findings not only articulate a novel bio-computational pathway for cryptographic defense but also provide a rigorous algorithmic blueprint for future physical realization, demonstrating that the thermodynamic complexity of biological systems offers a robust and physically grounded frontier for securing digital infrastructure in the post-quantum era.

cs.CR↗

arXiv2025

Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide

Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance. However, mining data from unstructured and noisy social media content remains a challenging task. We present a systematic framework that leverages large language models (LLMs) to extract medication side effects from social media and organize them into a knowledge graph (KG). We apply this framework to semaglutide for weight loss using data from Reddit. Using the constructed knowledge graph, we perform comprehensive analyses to investigate reported side effects across different semaglutide brands over time. These findings are further validated through comparison with adverse events reported in the FAERS database, providing important patient-centered insights into semaglutide's side effects that complement its safety profile and current knowledge base of semaglutide for both healthcare professionals and patients. Our work demonstrates the feasibility of using LLMs to transform social media data into structured KGs for pharmacovigilance.

cs.AI↗