arXiv subjects

Yue Xiao

Publications and source records attributed to Yue Xiao.

At least 19 recordsLinked to original sources

arXiv2026

Integrated Sensing and Communication via Channel Knowledge Maps Built from Beam Feedback Coefficients

Channel knowledge maps (CKMs) support wireless localization by linking channel features to user locations. Detailed channel fingerprints, however, can require substantial storage. This paper proposes a CKM-based localization framework that uses beam feedback coefficients collected during communication. These coefficients serve as compact location fingerprints within the beam management process, without requiring full channel feedback or additional sensing measurements. Using a coarse position prior, the base station searches nearby CKM entries and refines the location estimate through local interpolation. We analyze the relationship between spatial fingerprint variation and localization accuracy, and evaluate the effect of feedback quantization. Simulations show favorable storage--accuracy trade-offs relative to dense and sparsified spectral fingerprinting in the tested multipath environments.

eess.SP↗

arXiv2026

Understanding the Hierarchical Structure and Functional Landscape of the Model Context Protocol Ecosystem

AI agents increasingly rely on tools exposed through the Model Context Protocol (MCP) to complete user tasks. Hundreds of thousands of MCP servers are listed across marketplaces, yet they are organized only by coarse, marketplace-specific server categories. This makes it difficult for agents and users to identify tools for a given operation, find functional alternatives, and assess how those alternatives differ. We present MCPacific, the largest tool-level, cross-marketplace map of the MCP ecosystem. MCPacific collects 368,754 MCP server listings corresponding to 124,267 unique servers across 17 marketplaces, statically extracts 1,328,233 tool specifications from these servers in seven languages, and organizes them into a hierarchical functional taxonomy of 58,915 capabilities. We construct the taxonomy through an iterative LLM-driven design-test-refine process and map the full corpus to it using calibrated embedding routing. Our study reveals that MCP extends well beyond developer tooling, with 85% of tools serving other domains. Functional alternatives are widespread but unevenly distributed: 98.5% of tools have at least one alternative, yet nearly a quarter of capabilities are supported by only one tool. Functionally comparable tools also differ in security alerts, code complexity, and project maintenance, with complexity differing by more than 2.5x in 41% of comparable tool pairs. Finally, presenting candidate tools through the taxonomy rather than a flat list improves task completion rate across all four evaluated models, with gains of up to 12 percentage points in Pass@0.75 for crowded candidate sets.

cs.SE↗

arXiv2026

Observation of the electronic Pomeranchuk effect in generalized Wigner crystals of twisted MoS$_2$

Moire superlattices in transition metal dichalcogenides provide a highly tunable platform for exploring strongly correlated electronic phases, such as generalized Wigner crystals. While these crystalline states typically melt with increasing thermal fluctuations, an electronic analogue of the Pomeranchuk effect can stabilize the localized solid phase at elevated temperature through isospin entropy. Here, we report the observation of an electronic Pomeranchuk effect at the fractional filling factors of $ν= 1/3$ and $ν= 1/4$ in AB-stacked twisted bilayer MoS$_2$ with twist angles of 4.1$^\circ$ and 3.9$^\circ$, respectively. At ultra-low temperature, the system exhibits a highly conducting, itinerant behavior at these fractional fillings, characteristic of a compressible Fermi-liquid ground state. Upon heating, the system exhibits a counterintuitive increase in the longitudinal resistivity, signaling an isospin-entropy-driven transition into a localized generalized Wigner crystal state. Our findings highlight the unique capacity of flat bands in twisted bilayer MoS$_2$ for stabilizing highly degenerate magnetic configurations, offering new insights into the thermodynamic phase diagrams of low-dimensional correlated systems.

cond-mat.mes-hall↗

arXiv2026

Low-Interference N-Continuous OFDM via Optimized Time-Domain Smoothing

A novel basis signal optimization method is proposed for reducing the interference in the N-continuous orthogonal frequency division multiplexing (NC-OFDM) system. Compared to conventional NC-OFDM, the proposed scheme is capable of improving the transmission performance while maintaining an identical sidelobe suppression performance imposed by the linear combination of two groups of basis signals. Our performance results demonstrate that with a low-complexity overhead, the proposed scheme is capable of striking a better trade-off among the bit error rate (BER), complexity, and the sidelobe suppression performance compared to its conventional counterparts.

eess.SP↗

arXiv2026

Evaluating Scaffolding-Oriented Multi-Agent Large Language Model System for Clinical Interview Training

Clinical education must prepare medical students to conduct safe and coherent patient interviews under conditions of uncertainty. Traditional standardized patient (SP) training is resource-intensive and difficult to scale. We developed a scaffolding-oriented multi-agent Large Language Model (LLM) AI Standardized Patient (AI-SP) training platform1. The system includes a patient agent for simulated dialog, a tutor agent providing Socratic prompts without disclosing diagnostic information, and a turn-level evaluator agent that monitors clinical progress without revealing summative scores. In a randomized controlled study (N = 100 medical students), participants were assigned to either a multi-agent (MA) scaffolding condition or a control condition. All students completed two learning sessions under their assigned condition followed by an examination conducted in a patient only environment. Performance was assessed using a standardized Objective Structured Clinical Examination (OSCE) based rubric. While no significant difference was observed in final diagnostic accuracy between groups, the multi-agent AI standardized patient system improved final examination scores compared to the control group utilizing structured progressive information disclosure; the most substantial and consistent improvements were observed in communication, the expression of empathy, and specific history-taking behaviors. These findings suggest that specialized LLM agents enhance the process quality of simulated clinical interviews without artificially inflating examination outcomes. To support future research, we release a multi-expert annotated dataset comprising transcripts, checklist annotations, turn-level evaluations, and OSCE-aligned scoring outcomes. This resource aims to facilitate the development of pedagogically grounded AI-SP systems and advance research on AI-supported clinical reasoning training.

cs.MA↗

arXiv2026

AVP-Inspect: Coordinated Cyber-Physical Testing for Privacy Analysis of COTS Apple Vision Pro Applications

XR devices introduce substantial privacy concerns due to their comprehensive data collection capabilities that surpass traditional computing platforms. While existing works have demonstrated privacy concerns on Android-based XR devices such as Meta Quest series by performing network traffic analysis, little attention has been paid to the Apple Vision Pro (AVP) devices, mainly due to the closed nature and the technical challenges associated with AVP devices. In this work, we make a bold attempt to detect privacy violations of AVP applications from network traffic through automatic testing on AVP devices. Our key insight is that effective AVP application testing requires coordinated control of both cyber (software) and physical (hardware) components, which we term Coordinated Cyber-Physical Testing. Building on this insight, we design and implement AVP-Inspect, an automatic dynamic analysis framework for AVP applications, overcoming significant challenges enforced by the closed-source nature of AVP ecosystem. AVP-Inspect consists of three components: an automatic device controller by building customized hardware devices, a 3D UI explorer by designing a new exploration engine, and a privacy violation detector by constructing a unified privacy taxonomy for AVP. We first evaluated AVP-Inspect on a manually constructed ground truth dataset, then performed a large-scale analysis on 324 AVP applications downloaded from the App Store, with each app tested for 20 minutes. We found that 188 (58.0%) of apps exhibit at least one violation, and more than 60% of the network traffic flows are not properly disclosed.

cs.CR↗

arXiv2026

TheBotCompany: Self-Organizing Multi-agent Systems for Continuous Software Development

Large language model (LLM)-based multi-agent systems have shown promise in automating software development tasks. However, most vibe-coding systems focus on completing small tasks and incremental code changes, leaving persistent, continuous software development largely unexplored. We present TheBotCompany, an open-source orchestration framework for continuous multi-agent software development. TheBotCompany introduces three key innovations: (1) a three-phase state machine (Strategy to Execution to Verification) for milestone-driven development, (2) self-organizing agent teams where manager agents dynamically hire, assign, and retire worker agents based on project needs, and (3) asynchronous human oversight. We evaluate TheBotCompany on real-world software projects over multiple days of continuous development, measuring team adaptation patterns, milestone completion rates, cost efficiency, and code quality. Our results demonstrate that the self-organizing approach enables effective long-term software development with measurable progress, while the verification phase catches defects that would otherwise persist.

cs.SE↗

arXiv2026

Breaking Network Densification Limits with Distributed Cooperative Massive Access (DCMA)

In this work, we investigate the performance of the distributed cooperative massive access (DCMA) framework in large-scale network setups by incorporating stochastic geometry modeling. A partially centralized cell-free cloud-radio access (C-RAN) architecture is considered where remote radio heads (RRHs) decode transmitted messages and cooperate with each other to enhance system performance. Specifically, they can share decoded messages via feedback links, allowing receivers to cancel inter-user interference through successive interference cancellation (SIC), thus improving the decoding capabilities of the system. For such a network, we propose a novel synergetic decoding algorithm that efficiently resolves the assignment and message sharing routing for each user while accounting for practical network constraints. Furthermore, using game theory, we develop a merge-and-split algorithm with lexicographic preference to solve the problem of minimizing the RRHs utilized without compromising the performance. Simulation results show that the proposed framework significantly outperforms systems that do not implement SIC or take advantage of the cooperation between RRHs in terms of outage probability. Finally, we evaluate the performance of the proposed algorithms and validate their efficiency.

cs.GT↗

arXiv2026

Tracking the boundary between absolute/convective instability using adjoint equations

Determining absolute/convective instability boundaries conventionally requires repeated saddle searches in the complex-wavenumber plane and a subsequent scan of the physical parameter space to locate zero absolute growth. Such nested calculations become costly and sensitive to modal branch association for large non-normal eigenvalue problems. This work develops a direct continuation method for neutral stationary-saddle boundaries of frequency-affine generalised eigenvalue problems. The zero-group-velocity condition is expressed as an adjoint solvability residual and solved together with the direct and adjoint eigenproblems, complex gauge constraints and the neutral-growth condition. The resulting one-dimensional solution manifold in the combined state--parameter space is tracked by scaled pseudo-arclength continuation, allowing parameter folds to be crossed without switching the physical continuation variable. The formulation recovers the analytical Ginzburg--Landau boundary and, for a Gaussian-wake Orr--Sommerfeld problem, agrees with separately formulated finite-difference saddle corrections to approximately $10^{-8}$ in relative critical Reynolds number. Compared with nested complex-wavenumber and parameter-plane saddle scanning, the tested scans require $8.1$--$52.2$ times the wall time of the direct adjoint continuation. Extrapolation of the measured cost--accuracy trend to a boundary error of $E_H\sim10^{-6}$ suggests an estimated cost ratio of approximately $1.8\times10^{4}$ in favour of the direct continuation. Application to a coupled Oldroyd--B free-surface film reveals genuine folds of the neutral-saddle manifold and a re-entrant CI--AI--CI boundary geometry for the selected saddle family.

physics.flu-dyn↗

arXiv2026

Measuring the Security of Mobile LLM Agents under Adversarial Prompts from Untrusted Third-Party Channels

Large Language Models (LLMs) have transformed software development, enabling AI-powered applications known as LLM-based agents that promise to automate tasks across diverse apps and workflows. Yet, the security implications of deploying such agents in adversarial mobile environments remain poorly understood. In this paper, we present the first systematic study of security risks in mobile LLM agents. We design and evaluate a suite of adversarial case studies, ranging from opportunistic manipulations such as pop-up advertisements to advanced, end-to-end workflows involving malware installation and cross-app data exfiltration. Our evaluation covers eight state-of-the-art mobile agents across three architectures, with over 2,000 adversarial and paired benign trials. The results reveal systemic vulnerabilities: low-barrier vectors such as fraudulent ads succeed with over 80% reliability, while even workflows requiring the circumvention of operating-system warnings, such as malware installation, are consistently completed by advanced multi-app agents. By mapping these attacks to the MITRE ATT&CK Mobile framework, we uncover novel privilege-escalation and persistence pathways unique to LLM-driven automation. Collectively, our findings provide the first end-to-end evidence that mobile LLM agents are exploitable in realistic adversarial settings, where untrusted third-party channels (e.g., ads, embedded webviews, cross-app notifications) are an inherent part of the mobile ecosystem.

cs.CR↗

arXiv2026

Pinching Antenna-Aided Spatial Multiplexing: Transceiver Design and Performance Analysis

In this paper, a novel pinching antenna-aided spatial multiplexing (PASM) architecture is conceived, which intrinsically amalgamates the benefits of flexible radiating element placement with radio-frequency (RF) chain transmission. Specifically, we leverage the deterministic phase variation along dielectric waveguides as a zero-power phase-control mechanism, where each waveguide fed by a single RF chain drives multiple pinching antennas (PAs) acquiring position-dependent phase shifts. Then, the PASM propagation environment is characterized by a realistic channel model encompassing Rician small-scale fading, correlated shadowing, and large-scale path loss. Based on this, a low-complexity vector approximate message passing (VAMP) detector is conceived, which exploits a waveguide-structured prior for jointly processing the signals associated with all PAs. Moreover, we derive an analytical upper bound on the bit error rate (BER) for the maximum likelihood (ML) detector to quantify the achievable performance limits. Finally, our simulation results demonstrate that the proposed PASM architecture achieves substantial signal-to-noise ratio (SNR) gain over the conventional phase-shifter-aided spatial multiplexing (PSSM), while the VAMP detector strikes an attractive trade-off between the system performance and computational complexity.

eess.SP↗

arXiv2026

AgentWard: A Lifecycle Security Architecture for Autonomous AI Agents

Autonomous AI agents extend large language models into full runtime systems that load skills, ingest external content, maintain memory, plan multi-step actions, and invoke privileged tools. In such systems, security failures rarely remain confined to a single interface; instead, they can propagate across initialization, input processing, memory, decision-making, and execution, often becoming apparent only when harmful effects materialize in the environment. This paper presents AgentWard, a lifecycle-oriented, defense-in-depth architecture that systematically organizes protection across these five stages. AgentWard integrates stage-specific, heterogeneous controls with cross-layer coordination, enabling threats to be intercepted along their propagation paths while safeguarding critical assets. We detail the design rationale and architecture of five coordinated protection layers, and implement a plugin-native prototype on OpenClaw to demonstrate practical feasibility. This perspective provides a concrete blueprint for structuring runtime security controls, managing trust propagation, and enforcing execution containment in autonomous AI agents. Our code is available at https://github.com/FIND-Lab/AgentWard .

cs.CR↗

arXiv2026

Quantized Zero-Energy RIS: Residual Phase Modeling and Outage Analysis

Zero-energy reconfigurable intelligent surfaces (zeRISs) have recently emerged as a promising solution for enabling energy-efficient and scalable programmable wireless environments (PWEs) by harvesting their operational energy from impinging radio-frequency signals. However, the operation of zeRIS-assisted systems is inherently constrained by the coupling between energy harvesting and signal reflection, a dependency that becomes more intricate under practical hardware limitations such as finite-resolution phase control. In this paper, we develop a comprehensive analytical framework for zeRIS-assisted communication systems operating under quantized phase shifts and harvest-and-reflect (HaR) schemes. Specifically, we analyze the joint energy-data rate outage probability and the energy efficiency under time switching and element splitting schemes, considering both transmitter-side and user-side deployment scenarios. By explicitly modeling the residual phase error induced by quantization and incorporating its statistical properties into the analysis, we show that quantization jointly affects energy harvesting and signal reflection, thereby inducing non-trivial trade-offs. As a result, the presented framework enables accurate performance evaluation and reveals critical design trade-offs for the selection of the phase resolution, and the applied HaR scheme in zeRIS-assisted wireless networks.

cs.IT↗

arXiv2026

HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation

Housing selection is a high-stakes and largely irreversible decision problem. We study housing consultation as a decision-support interface for housing selection. Existing housing platforms and many LLM-based assistants often reduce this process to ranking or recommendation, resulting in opaque reasoning, brittle multi-constraint handling, and limited guarantees on factuality. We present HabitatAgent, the first LLM-powered multi-agent architecture for end-to-end housing consultation. HabitatAgent comprises four specialized agent roles: Memory, Retrieval, Generation, and Validation. The Memory Agent maintains multi-layer user memory through internal stages for constraint extraction, memory fusion, and verification-gated updates; the Retrieval Agent performs hybrid vector--graph retrieval (GraphRAG); the Generation Agent produces evidence-referenced recommendations and explanations; and the Validation Agent applies multi-tier verification and targeted remediation. Together, these agents provide an auditable and reliable workflow for end-to-end housing consultation. We evaluate HabitatAgent on 100 real user consultation scenarios (300 multi-turn question--answer pairs) under an end-to-end correctness protocol. A strong single-stage baseline (Dense+Rerank) achieves 75% accuracy, while HabitatAgent reaches 95%.

cs.LG↗

arXiv2026

A Novel Low-Complexity Dual-Domain Expectation Propagation Detection Aided AFDM for Future Communications

This paper presents a dual-domain low-complexity expectation propagation (EP) detection framework for affine frequency division multiplexing (AFDM) systems. By analyzing the structural properties of the effective channel matrices in both the time and affine frequency (AF) domains, our key observation is the domain-specific quasi-banded sparsity patterns, including AF-domain sparsity under frequency-selective channels and time-domain sparsity under doubly-selective channels. Based on these observations, we develop an AF-domain EP (EP-AF) detector for frequency-selective channels and a time-domain EP (EP-T) detector for doubly-selective channels, respectively. By performing iterative inference in the time domain using the Gaussian approximation, the proposed EP-T detector avoids inverting the dense channel matrix in the AF domain. Furthermore, the proposed EP-AF and EP-T detectors leverage the aforementioned quasi-banded sparsity of the AF domain and time domain channel matrices, respectively, to reduce the complexity of matrix inversion from cubic to linear order. Simulation results demonstrate that the proposed low-complexity EP-AF detector achieves nearly identical error rate performance to its conventional counterpart, while the proposed low-complexity EP-T detector offers an attractive trade-off between detection performance and complexity.

eess.SP↗

arXiv2026

Antenna Elements' Trajectory Optimization for Throughput Maximization in Continuous-Trajectory Fluid Antenna-Aided Wireless Communications

Fluid antenna (FA) systems offer novel spatial degrees of freedom (DoFs) with the potential for significant performance gains. Compared to existing works focusing solely on optimizing FA positions at discrete time instants, we introduce the concept of continuous-trajectory fluid antenna (CTFA), which explicitly considers the antenna element's movement trajectory across continuous time intervals and incorporates the inherent kinematic constraints present in practical FA implementations. Accordingly, we formulate the total throughput maximization problem in CTFA-aided wireless communication systems, addressing the joint optimization of continuous antenna trajectories in conjunction with the transmit covariance matrices under kinematic constraints. To effectively solve this non-convex problem with highly coupled optimization variables, we develop an iterative algorithm based on block coordinate descent (BCD) and majorization-minimization (MM) principles with the aid of the weighted minimum mean square error (WMMSE) method. Finally, numerical results are presented to validate the efficacy of the proposed algorithms and to quantify the substantial total throughput advantages afforded by the conceived CTFA-aided system compared to conventional fixed-position antenna (FPA) benchmarks and alternative approaches employing simplified trajectories.

eess.SP↗

arXiv2026

How Far Should We Need to Go : Evaluate Provenance-based Intrusion Detection Systems in Industrial Scenarios

Provenance-based Intrusion Detection Systems (PIDSes) have been widely used to detect Advanced Persistent Threats (APTs). Although many studies achieve high performance in the evaluations of their original papers, their performance in industrial scenarios remains unclear. To fill this gap, we conduct the first systematic evaluation and analysis of PIDSes in industrial scenarios. We first analyze the differences between the data from DARPA datasets and that collected in industrial scenarios, identifying three main new characteristics in industry: heterogeneous multi-source inputs, more powerful attackers, and increasing benign activity complexity. We then build several datasets to evaluate five state-of-the-art PIDSes. The evaluation results reveal challenges for existing PIDSes, including poor portability across different hosts and platforms, low detection performance against real-world attacks, and high false positive rates with ever-changing benign activities. Based on the evaluation results and our industrial practices, we provide several insights to solve or explain the above problems. For example, we propose a method to mitigate the high false positives, which reduces manual effort by 2/3. Finally, we propose several research suggestions to improve PIDSes.

cs.CR↗

arXiv2026

PAPR-Aware Waveform Design for Energy-Efficient MIMO-OFDM SWIPT

Simultaneous wireless information and power transfer (SWIPT) critically depends on waveform design, which governs both reliable data delivery and efficient energy harvesting. Among waveform characteristics, the peak-to-average power ratio (PAPR) plays a pivotal role: low-PAPR signals improve power amplifier (PA) efficiency, while high-PAPR signals exploit rectifier nonlinearities to boost harvested energy. This duality makes PAPR a fundamental design challenge in SWIPT systems. To tackle this issue, we establish a unified analytical framework that characterizes the PAPR-dependent behaviors of both the PA and the rectifier, thereby revealing how waveform statistics determine end-to-end energy transfer efficiency. Building on this insight, we propose a frequency-domain resource allocation strategy for power-splitting SWIPT, where spectral segments are adaptively assigned to balance communication throughput with energy harvesting performance. Here, a key contribution is to extend SWIPT to MIMO-OFDM architectures. Despite concerns over excessive PAPR in large-scale antenna-subcarrier configurations, we demonstrate that appropriate waveform adaptation and resource optimization can transform MIMO-OFDM into an energy-efficient platform for joint data and power transfer. Finally, simulation results confirm significant improvements in PA efficiency, rectifier output, and overall energy transfer, thereby validating the practical benefits of the proposed approach.

eess.SP↗