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Zhendong Li

Publications and source records attributed to Zhendong Li.

At least 19 recordsLinked to original sources
arXiv2026

Movable Antennas Enabled Wireless Powered Networks: Principles and Technologies

As an emerging framework, movable antenna (MA)-enabled wireless powered networks (WPNs) have attracted growing attention. WPNs integrate wireless communication and energy transfer. MA can dynamically adjust the position of antenna units by introducing additional spatial degrees of freedom, so as to make full use of channel gain, optimize the effect of energy beamforming, and further improve the performance of WPNs. In this article, we first classify the implementations of MA by driving architectures, and review the fundamental principles of WPNs. We then highlight the key advantages of MA-enabled WPNs in enhancing wireless power transfer efficiency, realizing flexible and adaptive beamforming, and improving system robustness and interference resilience. Furthermore, four representative application scenarios and three key enabling technologies are discussed. A case study is also presented to show the reduction in transmit power brought by MA for WPNs. Finally, we discuss the challenges and future directions of MA-enabled WPNs, aiming to provide reference for future research and practice.

cs.NI↗
arXiv2026

UnityBench-Mini3D: Evaluating LLM Agents for 3D Game Development in Unity

Game development stresses coding agents in ways general software engineering does not: correctness depends on how engine APIs, serialized scene state, and runtime behavior interact, not on source code alone. Existing benchmarks mainly target platforms such as Godot and heavily rely on LLM judges or coarse task-level outcomes. We present UnityBench-Mini3D, a benchmark of 77 project-level Unity tasks organized around seven expert-defined skill categories rather than tutorials or genres, with three difficulty tiers. Each task pairs a complete Unity project with natural-language requirements and is evaluated using 1,178 held-out, human-authored milestone tests covering scene structure, component configuration, and runtime behavior. This in-engine evaluation provides deterministic partial credit, localizes failures, and distinguishes partial implementations from complete solutions without relying on subjective judges. Across 21 models and 9 coding harnesses, the average pass rate reaches 80.9% on easy tasks but falls to 35.2% on hard tasks, with just 57.7% overall. UnityBench-Mini3D provides a reproducible, diagnostic testbed distinguishing systems across difficulty levels.

cs.SE↗
arXiv2026

Agents' Last Exam

Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional domains. We argue that this gap is largely an evaluation problem: widely used benchmarks lack sustained performance measurement on real and economically valuable workflows. This paper introduces Agents' Last Exam (ALE), a benchmark designed to evaluate AI agents on long horizon, economically valuable, real world tasks with verifiable outcomes. Developed in collaboration with 250+ industry experts, ALE covers non-physical industries defined with reference to O*NET / SOC 2018 (the U.S. federal occupational taxonomy). It is organized around a task taxonomy with 55 sub fields grouped into 13 industry clusters covering 1K+ tasks. Current results show that the hardest tier remains far from saturated: across mainstream harness and backbone configurations, the average full pass rate is below 1%. ALE is designed as a living benchmark: its task pool grows continuously as new workflows and industries are onboarded. More broadly, ALE is intended not merely as another leaderboard, but as an instrument for closing the gap between benchmark success and GDP relevant impact.

cs.AI↗
arXiv2026

Initial-State Preparation for Strongly Correlated Molecules from Matrix Product States: Benchmarking and Environment-Aware Divide-and-Conquer Compilation

Compiling classically optimized matrix product states (MPSs) into quantum circuits is a promising route to initial-state preparation for strongly correlated molecules, but the accuracy, gate cost, and classical scalability of existing MPS compilation methods have not been systematically assessed. Here, we benchmark representative analytical, variational, and hybrid MPS compilation methods for strongly correlated systems including hydrogen chains and iron-sulfur clusters. We compare state infidelity, CNOT gate count, classical runtime, and parallel scalability across different system sizes and bond dimensions. We also introduce a divide-and-conquer environment-aware compilation algorithm (DCE), which optimizes each local isometry approximation according to its contribution to the prepared-state fidelity. The results reveal complementary operating regimes: direct variational compilation methods are gate-efficient for some smaller molecular systems with $O(10)$ qubits and $O(10)$ bond dimensions or moderate accuracy targets with infidelity of order $10^{-1}$, whereas divide-and-conquer methods scale more favorably for larger systems and relatively higher bond dimensions of $O(10\text{-}10^2)$, particularly when target infidelities below $10^{-1}$ are required. Within the latter family, environment-aware optimization improves accuracy over unitary-distance-based compilation while retaining task-level parallelism. For the FeMo-cofactor (FeMoco), we find the DCE algorithm together with the MPS obtained in the entanglement-minimized orbitals (EMOs) provides a resource-efficient route to initial-state preparation. Our results provide practical guidance for selecting and developing MPS compilation methods for strongly correlated molecular simulations.

quant-ph↗
arXiv2026

Spin-Adapted Spin-Flip Tamm-Dancoff Approximation (XSF-TDA) for Singlet-Triplet Gaps in MR-TADF and INVEST Emitters

In this work, we assess the recently introduced spin-adapted TDA for spin-flip-down excitations (XSF-TDA) [Mol. Phys. e2631735 (2026)] as a practical approach to this problem. Starting from a high-spin restricted open-shell Kohn--Sham reference, XSF-TDA generates spin-adapted low-spin roots arising from open--open (OO), core--open (CO), open--virtual (OV), and core--virtual (CV) types of spin-flip-down excitations. Across four application sets comprising 243 benchmark cases, XSF-TDA substantially reduces the systematic positive bias observed for TDA and unrestricted spin-flip TDA (USF-TDA). With the B3LYP functional, XSF-TDA reduces the equal-set-weighted macro-MAE relative to USF-TDA from 0.252 eV (0.158 eV) to 0.102~eV (0.056~eV) with the ALDA0 (multicollinear) kernel. Its accuracy is competitive with state-specific $Δ$DFT and quasiparticle-energy DFT (QE-DFT), while being able to provide deeper mechanistic insight through the response vector. In particular, spin-adapted CV configurations provide selected higher-excitation character that strengthens the OO--CV dynamical-spin-polarization channel, which preferentially stabilizes the singlet states. We also identify an INVEST emitter with appreciable OO--OV mixing, suggesting a possible additional contribution to singlet stabilization when the CV contribution is small. The XSF-TDA method therefore provides practical singlet--triplet gap predictions together with interpretable state analysis for the screening and mechanistic understanding of MR-TADF and INVEST emitters.

physics.chem-ph↗
arXiv2026

Joint Beamforming Optimization and Dynamic Tracking in RIS-Enabled Secure ISAC Systems

This paper investigates a reconfigurable intelligent surface (RIS)-enabled secure integrated sensing and communication (ISAC) system, where the direct links between the base station (BS) and users are blocked, and the mobile eavesdropper is treated as the sensing target. The mobility of the eavesdropper induces time-varying wiretap channels, potentially compromising secure transmission when outdated eavesdropper information is used. To address this challenge, the BS dynamically tracks the eavesdropper over consecutive time slots and exploits the predicted state information to adapt its transmission strategy. A sum secrecy rate maximization problem is formulated by jointly optimizing the BS beamforming, artificial noise, and RIS reflection coefficients, subject to a constraint on the tracking error covariance to ensure the required sensing accuracy. To tackle the resulting nonconvex optimization problem with temporal coupling, an iterative algorithm integrating an extended Kalman filter (EKF) with an alternating optimization framework is developed. Specifically, the EKF recursively predicts and updates the eavesdropper's state, while the joint optimization problem is decomposed into four tractable subproblems that are solved iteratively. Simulation results demonstrate that the proposed algorithm achieves improved secrecy performance while effectively tracking the trajectory of the mobile eavesdropper, compared with benchmark schemes.

eess.SP↗
arXiv2026

Movable Antenna-Enabled Multi-Static Cooperative Secure ISAC: Architecture, Mechanisms, and Enabling Technologies

The broadcast nature of integrated sensing and communication (ISAC) signals exposes them to physical-layer security (PLS) threats, including eavesdropping and jamming. Conventional fixed-position antennas provide limited spatial flexibility under highly correlated legitimate, sensing, and adversarial channels, while multi-static cooperative ISAC offers distributed spatial diversity but remains vulnerable to information leakage. To address these limitations, this article proposes a novel movable antenna (MA)-enabled multi-static cooperative secure ISAC framework centered on dual-scale spatial reconfiguration. Specifically, continuous MA repositioning reshapes local propagation channels, while distributed multi-static nodes provide complementary network-level spatial diversity. Their synergy transforms local antenna geometry and distributed network geometry into a unified controllable spatial resource for jointly enhancing communication, sensing, and PLS. This article first presents the system architecture of the proposed framework and its representative application scenarios, followed by an analysis of the physical-layer mechanisms underlying the resulting gains. Subsequently, we elaborate on the key enabling technologies for practical deployment. A case study further demonstrates the communication-sensing-security trade-off and antenna-saving potential of the proposed framework. Finally, open challenges and future research directions are identified toward practical MA-enabled multi-static secure ISAC networks.

eess.SP↗
arXiv2026

Dual Secure Sensing and Communications in MA-Enabled Networked ISAC Systems

Integrated sensing and communication (ISAC) is envisioned as a cornerstone technology for next-generation networks, yet it introduces significant security risks in both communication and sensing domains. To mitigate the coverage limitations inherent in conventional mono-static or bi-static setups, this paper proposes a networked ISAC framework empowered by movable antennas (MAs). By leveraging flexible antenna reconfiguration, MAs provide additional spatial degrees of freedom to significantly boost beamforming gain and interference suppression. Specifically, we investigate a novel dual-domain security paradigm where networked base stations collaboratively protect uplink (UL) and downlink (DL) transmissions from communication eavesdropping while simultaneously safeguarding target sensing data against sensing eavesdroppers (SEs). To this end, we formulate a joint optimization problem to maximize the sum of secrecy rates, subject to sensing requirements, SE eavesdropping probability constraint, and practical MA deployment constraints, by jointly optimizing receive combiners, transmit beamformers, UL power allocation, and MA positions. To address this non-convex problem with highly coupled variables, an efficient alternating optimization algorithm is proposed, integrating closed-form solutions for receive combiners, successive convex approximation for transmit beamforming and UL power, and a tailored sensing-aware multi-candidate particle swarm optimization for the high-dimensional MA positioning. Extensive numerical results validate the effectiveness of the proposed algorithms, demonstrating that the proposed MA-enabled networked cooperative ISAC design significantly outperforms conventional fixed-position antenna-based designs in terms of security performance, while achieving notable power- and antenna-saving gains.

eess.SP↗
arXiv2026

Robust Design of Sensing-Assisted Secure Transmission for RIS-Aided ISAC System under Eavesdropper 3D Positioning Uncertainty

Localization errors in estimating the position of a potential eavesdropper (Eve) pose a critical challenge to secure transmission in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems. To address this issue, this paper proposes a sensing-assisted robust secure transmission framework that exploits ISAC sensing to characterize the positioning uncertainty induced by these errors. The RIS is partitioned into a reflection region and a sensing reception region, supporting sensing and communication through spatial separation. By jointly exploiting observations collected at the base station (BS) and the RIS sensing reception region, maximum likelihood estimation (MLE) is employed to estimate the arrival angles, from which Eve's three-dimensional (3D) position is obtained through cross localization. The resulting localization Cramer-Rao lower bound (CRLB) is then used to construct an ellipsoidal uncertainty region around the estimated position. A two-stage optimization strategy is developed. In Stage I, the sensing beamformer and RIS reflection coefficients are jointly optimized to improve localization accuracy. In Stage II, the resulting positioning uncertainty is incorporated into a robust secure transmission design, where the communication beamformer and RIS reflection coefficients are jointly optimized to maximize the worst-case secrecy rate. Simulation results demonstrate that the proposed approach outperforms benchmark schemes without RIS assistance and with random RIS configurations in terms of secrecy performance. The proposed framework effectively mitigates secrecy performance degradation caused by localization errors in Eve's position.

eess.SP↗
arXiv2026

Sensing-Driven Secure Transmission with Mobile Eavesdropper Tracking in RIS-Assisted ISAC

The mobility of potential eavesdroppers (Eves) poses a critical challenge to physical-layer security (PLS) in integrated sensing and communication (ISAC) systems, since rapidly varying Eve positions lead to time-varying eavesdropping channels and outdated security decisions. To address this challenge, this paper investigates sensing-driven secure transmission for reconfigurable intelligent surface (RIS)-assisted ISAC systems with a maneuvering Eve. To this end, by exploiting the dual functionality of the ISAC waveform, a multi-view angle estimation method jointly exploiting the base station (BS) and RIS is developed based on MUSIC and maximum likelihood estimation (MLE), enabling three-dimensional (3D) Eve localization, whose accuracy is characterized by the Cramer-Rao lower bound (CRLB). Subsequently, an interacting multiple model extended Kalman filter (IMM-EKF) with out-of-sequence measurement (OOSM) compensation is employed to track and predict the maneuvering Eve. Based on the predicted trajectory, the time-varying eavesdropping channel is updated, and the transmit beamforming and RIS phase shifts are jointly optimized subject to a sensing CRLB constraint. Furthermore, an alternating optimization (AO) algorithm integrating fractional programming, semidefinite relaxation (SDR), and Riemannian manifold optimization is developed to solve the resulting non-convex problem. Finally, numerical results demonstrate the effectiveness of the proposed approach in accurately tracking maneuvering Eves and enhancing secure transmission in dynamic ISAC environments.

eess.SP↗
arXiv2026

Antenna Positioning and Beamforming Optimization in MA Enabled Secure ISAC Systems: A Gradient-Based Meta Learning Approach

Integrated sensing and communications (ISAC) significantly improves spectral efficiency but introduces security risks regarding the interception of embedded communication signals. This paper proposes an movable antenna (MA)-enabled secure ISAC system that utilizes the spatial degrees of freedom of MA to mitigate these risks. Then, a problem is formulated to maximize the system secrecy rate by jointly optimizing antenna positioning, transmit beamforming, and artificial noise. However, the principal challenge arises from the non-convexity of the optimization problem and the strong coupling of the optimization variables. Generally, traditional optimization methods for this problem suffer from complex mathematical derivations, while existing deep learning approaches rely heavily on the training data distribution. To address these issues, we introduce a gradient-based meta learning (GML) algorithm, which works without pre-training and demonstrates favorable performance. Specifically, the algorithm establishes a neural network for each optimization variable, where the gradient of the objective function with respect to the variable serves as the input, and the output of the network determines the variable's update step. By handling the constraints and constructing penalty terms, the global loss function is used to guide the optimization process. Extensive numerical simulations confirm that the proposed algorithm achieves satisfactory performance in terms of both communication security and sensing capabilities.

eess.SP↗
arXiv2026

Adaptive Beam Hopping and Power Control for Dual-Layer Over-the-Air Online Federated Learning in LEO Satellite Networks

This paper investigates over-the-air (OTA) computation enabled online federated learning (FL) in low-Earth orbit (LEO) satellite networks. Specifically, we consider a dual-layer OTA aggregation architecture, where ground devices upload analog model updates to serving satellites via uplink OTA aggregation, and satellites forward the aggregated signals to a data processing center through the second round OTA aggregation. Then, we formulate a long-term data-utilization maximization problem in which devices continuously collect new data and untrained samples gradually lose freshness. The problem is subject to the satellite beam budget, transmit-power limit, and global mean squared error (MSE) constraint that governs end-to-end aggregation distortion. This yields a coupled mixed-integer nonlinear programming (MINLP) problem, involving tightly coupled discrete beam-hopping decisions and continuous power control. Due to the combinatorial action space and nonconvex constraints, the problem is NP-hard and computationally intractable. Furthermore, the time-varying satellite topology and dynamic data generation render it a sequential decision-making problem, necessitating adaptive online scheduling. To address these issues, we cast the problem as a Markov decision process and develop a proximal policy optimization (PPO)-based deep reinforcement learning framework that jointly optimizes adaptive beam hopping and power control, using an MSE-aware reward to balance data utilization and aggregation accuracy. Numerical simulation results verify that the proposed algorithm consistently outperforms other benchmark schemes, achieving superior long-term data utilization and faster FL convergence while satisfying the MSE requirement.

cs.IT↗
arXiv2026

Beamforming and Trajectory Optimization for Aerial RIS-Aided Secure ISAC via Model Predictive Control

This paper proposes a novel aerial reconfigurable intelligent surface (RIS)-aided secure integrated sensing and communication (ISAC) architecture. An unmanned aerial vehicle (UAV)-mounted RIS establishes virtual line-of-sight links to bypass blockages, serving legitimate users while sensing potential eavesdroppers. To ensure communication security and sensing reliability, we formulate an infinite-horizon dynamic control problem maximizing the long-term average system secrecy rate and minimizing UAV flight energy. This formulation jointly optimizes the UAV trajectory, active beamforming with artificial noise (AN) at the base station, and RIS phase shifts, subject to strict radar signal-to-noise ratio and UAV kinematic constraints. Due to the highly coupled non-convex variables, solving this problem directly is intractable. Therefore, we propose an online joint optimization framework based on model predictive control (MPC). Within each receding horizon, alternating optimization is employed to decouple the problem into three subproblems, efficiently solved via successive convex approximation and difference of convex programming. Extensive simulations demonstrate that our MPC-based algorithm significantly outperforms static baselines in secrecy rate, exhibiting superior online correction capability and robustness against environmental perturbations.

eess.SP↗
arXiv2026

Broadband LEO Satellite Constellations for Next-Generation Navigation: Potentials, Enabling Technologies, and Challenges

The rapid deployment of broadband low Earth orbit (LEO) constellations provides new opportunities for next-generation navigation. Compared with conventional global navigation satellite systems (GNSS), broadband LEO systems provide stronger received signals, wider bandwidths, rapidly varying satellite geometry, and larger constellation scales, offering new capabilities for positioning, navigation, and timing (PNT). Meanwhile, their communication-oriented waveforms, high dynamics, and resource-constrained architectures introduce new challenges that are fundamentally different from those in GNSS. This article provides a comprehensive overview of broadband LEO constellations for next-generation navigation, covering their fundamental characteristics, representative use cases, key enabling technologies, and open challenges. We first compare broadband LEO systems with conventional GNSS and summarize the main technical routes for LEO-based PNT. We then discussed the emerging use cases in GNSS-challenged, multipath-dominant, and integrated communication and navigation (ICAN) scenarios. Key enabling technologies including waveform design, high-dynamic signal processing, and constellation resource scheduling are further reviewed. As a representative case study, the navigation-oriented optimization of the Starlink primary synchronization sequence demonstrates that subcarrier power allocation can improve multipath resistance while maintaining ranging accuracy. Finally, several challenges and future directions of broadband LEO-based navigation are discussed, aiming to provide reference for future researches.

eess.SP↗
arXiv2026

S$^{2}$-PINN: Stochastic Separable Physics-Informed Neural Networks

Uncertainty quantification (UQ) for random partial differential equations (PDEs) is ubiquitous in computational science and engineering. However, classical spectral solvers for this class of problems face the curse of dimensionality, and existing neural solvers often ignore the stochastic structure that makes moments and calibration tractable. We introduce a stochastic separable physics-informed neural network, dubbed S$^{2}$-PINN, that represents the solution $u(t,\mathbf{x},\mathbf{Z})$ of a random PDE with a learnable Gaussian spatial dictionary, Fourier temporal features, and a generalized polynomial chaos (gPC) stochastic basis, coupled by a low-rank Canonical Polyadic (CP) tensor decomposition core. The method is trained with a hybrid strong-form and gPC-projected residual loss. Our theoretical analysis establishes that the separable class is dense in $L^2$ under mild conditions, and the projected residual corresponds exactly to a stochastic Galerkin constraint. Furthermore, we show that mini-batch projection coefficients are logarithmically dependent on the number of gPC modes, and that the orthogonality penalty controls the conditioning of the learned spatial dictionary. Using four manufactured random PDE benchmarks, we show that S$^{2}$-PINN outperforms nine baselines in terms of mean and variance accuracy, as well as calibration, while using significantly fewer parameters. Further evaluations on non-manufactured Poisson and Darcy problems, a stochastic Navier--Stokes problem, a diffusion scaling study of higher random dimensions, and two stochastic inverse problems reveal the generalization capabilities of the proposed structure. Together, these results support stochastic separability as an effective design principle for physics-informed neural UQ. The code for the experiments can be found in https://github.com/DMax1314/s2pinn

cs.LG↗
arXiv2026

Tensor-Based Dynamic Channel Estimation for mmWave Movable Antenna MIMO Systems

This paper investigates the dynamic channel estimation algorithm in mmWave movable antenna (MA) multiple-input multiple-output (MIMO) systems. To achieve highly accurate channel estimation, we propose a tensor decomposition-based channel estimation algorithm. First, by leveraging the path response model and utilizing the intrinsic sparsity of mmWave channels, the channel corresponding to MA pairs at the base station and mobile station is transformed into a superposition of channels from sparse paths. Next, the received signal is constructed as a fourth-order tensor to fully capture the high-dimensional structural information of the MA MIMO channel. Then, two tensor decomposition schemes are adopted to extract the factor matrices, and our analysis reveals that the uniqueness of the decomposition can be guaranteed in our model. Subsequently, the propagation loss, frequency offset, angle of arrival/departure, and time delay are obtained based on these factor matrices and the channel matrix can be rebuilt. Additionally, Cramér-Rao bound (CRB) is also derived as a performance evaluation standard, proving that the proposed algorithm achieves a higher estimation accuracy and nearly approaches this minimum bound. Moreover, normalized mean square error (NMSE) is selected as the evaluation metrics for estimation accuracy. Finally, simulation results reveal a notable reduction in the estimation error of the proposed algorithm when compared to the baseline algorithms, confirming its estimation advantage.

eess.SP↗
arXiv2026

RIS-Assisted Secure ISAC: Fundamentals, Applications, and Future Directions

Integrated sensing and communication (ISAC) has emerged as a key enabler for sixth-generation (6G) networks, yet its broadcast nature at the physical layer leaves transmissions vulnerable to malicious eavesdropping, particularly when physical blockages degrade direct links. Reconfigurable intelligent surface (RIS) offers a promising defense paradigm at the physical layer to address these issues, by reconstructing virtual line-of-sight links and dynamically reshaping the spatial propagation environment. In this article, we provide a comprehensive overview of RIS-assisted secure ISAC systems, covering its fundamentals, practical applications, and open future research directions. We first outline cooperative deployment topologies and basic electromagnetic operating principles. We then analyze the technical advantages of passive RIS integration in mitigating bottlenecks in resource competition across communication secrecy, sensing precision, and hardware sustainability. We also present representative applications, including urban vehicular networks, monitoring with unmanned aerial vehicle support, industrial Internet-of-thing, and privacy in smart homes. A case study quantifies gains in secrecy rate and spatial nulling capabilities achieved through joint active and passive beamforming under full blockage of direct links. Finally, we identify key open challenges and future research directions to enable practical, scalable, and intelligent deployment of RIS-assisted secure ISAC systems.

eess.SP↗
arXiv2026

A Tutorial on Movable Antenna-Enabled ISAC Systems: Fundamentals, Parameter Estimation, and Security Issues

Movable antenna (MA) technology has recently emerged as a promising paradigm for enhancing the flexibility and performance of wireless systems by allowing antennas to dynamically adjust their spatial positions within a confined region. Meanwhile, integrated sensing and communication (ISAC) has attracted significant attention as a key technology for next-generation wireless networks, aiming to integrate communication and sensing within a shared hardware and spectral framework. By leveraging the additional spatial degrees of freedom offered by MA, MA-enabled ISAC systems provide new opportunities to enhance both communication performance and sensing accuracy. However, the integration of MA into ISAC also poses new challenges in parameter estimation and security. In this tutorial, we provide a comprehensive overview of MA-enabled ISAC systems, with a particular focus on their fundamentals, parameter estimation, and security issues. First, we introduce the basic principles of MA and ISAC, and review representative MA-enabled ISAC systems and their potential applications. Then, we establish unified mathematical models for communication and sensing, and systematically review representative channel and sensing parameter estimation methods tailored to MA-enabled ISAC systems. Numerical comparisons are provided to illustrate the characteristics and performance of different estimation methods. Furthermore, we investigate the security issues of MA-enabled ISAC systems, introduce the fundamentals of physical-layer security (PLS), and summarize how the spatial reconfigurability of MAs can be exploited to enhance the security of ISAC systems. Representative case studies are presented to demonstrate the performance gains of MA-enabled secure ISAC systems. Finally, we identify several open issues and future research directions toward practical MA-enabled ISAC systems.

eess.SP↗