arXiv subjects

Ziqi Yang

Publications and source records attributed to Ziqi Yang.

At least 19 recordsLinked to original sources

arXiv2026

Progressive Replacement Sequence Planning for Laser-Enhanced BeiDou Navigation Constellations

The early satellites of the third-generation BeiDou Navigation Satellite System (BDS-3) are approaching the end of their design lifetime, making progressive constellation replacement an inevitable engineering task. Meanwhile, laser inter-satellite links (LISLs) provide high-precision time transfer, sub-millimeter-level ranging, and high-rate data forwarding capabilities, offering a promising upgrade path for future BeiDou satellites. This paper investigates the replacement-sequence planning problem for progressively replacing legacy microwave satellites with laser-enabled satellites from a networking perspective. To address this problem, three main contributions are made: (i) BeiDou progressive replacement is formulated as a network-aware launch sequence planning problem over evolving laser--microwave hybrid constellation states; (ii) an integer linear programming (ILP) model is developed to evaluate the networking gain of each candidate launch action under practical engineering constraints; and (iii) a priority-aware search heuristic is introduced to reduce the action evaluation space for efficient round-wise decision making. Simulations based on the actual BDS-3 constellation demonstrate that the proposed replacement sequence achieves a more favorable hybrid-network evolution than the historical BDS-3 launch order, including earlier formation of a ground-accessible laser-enhanced network structure, better utilization of laser-link resources, fewer residual non-anchor satellites, and lower microwave-layer waiting delay. The proposed framework can serve as a network-aware decision-support tool for future BeiDou evolution, and the resulting replacement sequence can provide direct engineering guidance for BeiDou replacement planning.

eess.SY↗

arXiv2026

DeFiScreener: Efficient DeFi Attack Pre-screening in Smart Contracts via Historical Case Matching

Blockchain and its killer applications, particularly decentralized finance (DeFi), are gaining widespread adoption, with over 5,200 DeFi projects deployed on mainstream blockchains as of January 2026. At the same time, security risks in DeFi are becoming increasingly serious. However, existing DeFi detection tools usually cover only specific attack types, exhibiting severely limited detection coverage. In this paper, we argue that an effective way to address this gap is to pre-screen vulnerable instances from large volumes of smart contract functions and call sequences. This is motivated by a key phenomenon we term "perilous temporal asymmetry". Inspired by this, we propose DeFiScreener, the first automated pre-screening framework for DeFi attacks that uses historical exploit cases to identify potentially vulnerable functions and call sequences. Given the full source code of a target project, DeFiScreener builds Function Call Trees (FCTs) and generates semantic embeddings for each function using a large language model (LLM), allowing both program structure and function intent to be analyzed together. It then applies a dual-level screening process. At the function level, function embeddings are matched against an Attack Pattern Library of historically exploited functions. At the sequence level, the proposed Attack Pattern Oriented Monte Carlo Tree Search (APO-MCTS) efficiently explores the FCTs and screens vulnerable call sequences. The identified candidates are ultimately passed to an LLM for further interpretive and security analysis. We empirically evaluate the DeFiScreener over datasets comprising 207 real-world DeFi attack incidents. Experimental results demonstrate that DeFiScreener achieves a remarkable 98.55% recall and 84.30% precision in attack pre-screening.

cs.CR↗

arXiv2026

Have You Ever Seen Them? Entity-level Membership Inference through Interrogating Large Language Models

Large Language Models (LLMs) raise growing concerns about privacy leakage and copyright compliance. Membership inference is a key tool for assessing such risks, but existing studies mainly focus on whether specific samples or sample-based data units are used for training. We argue that LLMs exhibit a human-memory-like behavior: an LLM may not memorize a specific sample verbatim, yet it can accumulate and reveal knowledge about a real-world entity from scattered mentions. This analogy motivates us to examine whether an LLM can be interrogated like a human interviewee to reveal its exposure to entity-related information. Motivated by this question, we propose entity-level membership inference, which determines whether information related to a target entity is used in LLM training. We study this task in the practical label-only black-box setting, where only generated texts are observable. We formalize the task under clue, input, and model constraints, establish the necessary and sufficient conditions for its feasibility, and instantiate five interrogation strategies based on this formalization. The strategies use limited entity clues to construct prompts, elicit entity-related responses, and infer membership from semantic features among the generated texts. We construct entity-level datasets and adapt state-of-the-art sample-level label-only methods to the entity-level setting as baselines. Experiments on person entities show that our methods achieve AUC up to 0.97 and bring gains of 6.0%--17.5% in Balanced Accuracy over the best adapted baseline.

cs.CL↗

arXiv2026

Towards Apples to Apples for AI Evaluations: From Real-World Use Cases to Evaluation Scenarios

AI measurement science has a wide variety of methodologies and measurements for comparing AI systems, resulting in what often appear to be "apples-to-oranges" comparisons across AI evaluations. To move toward "apples-to-apples" comparisons in real-world AI evaluations, this work advocates for methodological transparency in evaluation scenarios, operational grounding, and human-centered design (HCD) principles. We propose a repeatable process for transforming high-level use cases to detailed scenarios by eliciting use cases from subject matter experts (SMEs) via a structured AI Use Case Worksheet with six key elements: use case, sector, user (direct and indirect), intended outcomes, expected impacts (positive and negative), and KPIs and metrics. We demonstrate utility of the worksheet and process in the U.S. financial services sector. This paper reports on example high-level AI use cases identified by financial services sector SMEs: cyber defense enablement, developer productivity, financial crime aggregation, suspicious activity report (SAR) filing, credit memo generation, and internal call center support. These AI use cases provided are illustrative of the process and not exhaustive. Central to our work is a three-stage expansion pipeline combining LLM prompting with human reviews to generate 107 scenarios from those use cases elicited from SMEs. This process integrates iterative human reviews at every juncture to ensure operational grounding: for scenario titles and descriptions; for core scenario elements like users, benefits and risks, and metrics; and for scenario narratives and evaluation objectives. Human checkpoints ensure scenarios remain reflective of real-world usage and human needs. We describe a validation rubric to assess scenario quality. By defining key scenario components, this work supports a more consistent and meaningful paradigm for human-centered AI evaluations.

cs.HC↗

arXiv2026

Position: Embodied AI Requires a Privacy-Utility Trade-off

Embodied AI (EAI) systems are rapidly transitioning from simulations into real-world domestic and other sensitive environments. However, recent EAI solutions have largely demonstrated advancements within isolated stages such as instruction, perception, planning and interaction, without considering their coupled privacy implications in high-frequency deployments where privacy leakage is often irreversible. This position paper argues that optimizing these components independently creates a systemic privacy crisis when deployed in sensitive settings, thereby advancing the position that privacy in EAI is a life cycle-level architectural constraint rather than a stage-local feature. To address these challenges, we propose Secure Privacy Integration in Next-generation Embodied AI (SPINE), a unified privacy-aware framework that treats privacy as a dynamic control signal governing cross-stage coupling throughout the entire EAI life cycle. SPINE decomposes the EAI pipeline into various stages and establishes a multi-criterion privacy classification matrix to orchestrate contextual sensitivity across stage boundaries. We conduct preliminary simulation and real-world case studies to conceptually validate how privacy constraints propagate downstream to reshape system behavior, illustrating the insufficiency of fragmented privacy patches and motivating future research directions into secure yet functional embodied AI systems. We detail the SPINE framework and case studies at https://github.com/rminshen03/EAI_Privacy_Position.

cs.AI↗

arXiv2026

Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap

Large Language Models (LLMs) have shown promising performance in software vulnerability detection, particularly after domain-specific Supervised Fine-Tuning (SFT). However, it remains unclear whether these models genuinely internalize vulnerability root causes or merely exploit surface-level functional patterns. While prior work documented related failures on pre-trained or zero-shot models, the SFT process itself, and how explicit reasoning supervision modulates it, remains under-explored. We study fine-tuned decoder-only LLMs under vanilla SFT and SFT with reasoning supervision, identifying a failure mode we term the Semantic Trap, characterized by three symptoms: pairing-sensitive performance, gap-dictated decisions, and fragility to semantic-preserving changes. To probe this, we propose TrapEval, an evaluation framework comprising two real-world datasets, V2P (vulnerable paired with patched code) and V2N (vulnerable paired with unrelated normal code), alongside semantic perturbations, CodeBLEU-based gap analysis, and an LLM-assisted reasoning failure taxonomy. Evaluating five representative LLMs fine-tuned with and without explicit reasoning (Chain-of-Thought), our results show vanilla SFT yields deceptively high scores on unpaired data (V2N) while failing all three symptoms. Models suffer high false-positive rates on V2P, degrade under perturbations, and exhibit a systematic dependency on the textual gap between vulnerable and patched code. Finetuning with explicit reasoning reduces these symptoms but costs recall; its lack of measurable gap-dependency partly reflects a floor effect rather than escaping the trap. Furthermore, our taxonomy reveals these models still misinterpret control flow and hallucinate API behavior, indicating current fine-tuning mitigates but does not eliminate reliance on surface features.

cs.CR↗

arXiv2025

Worker Discretion Advised: Co-designing Risk Disclosure in Crowdsourced Responsible AI (RAI) Content Work

Responsible AI (RAI) content work, such as annotation, moderation, or red teaming for AI safety, often exposes crowd workers to potentially harmful content. While prior work has underscored the importance of communicating well-being risk to employed content moderators, designing effective disclosure mechanisms for crowd workers while balancing worker protection with the needs of task designers and platforms remains largely unexamined. To address this gap, we conducted individual co-design sessions with 15 task designers, 11 crowdworkers, and 3 platform representatives. We investigated task designer preferences for support in disclosing tasks, worker preferences for receiving risk disclosure warnings, and how platform representatives envision their role in shaping risk disclosure practices. We identify design tensions and map the sociotechnical tradeoffs that shape disclosure practices. We contribute design recommendations and feature concepts for risk disclosure mechanisms in the context of RAI content work.

cs.HC↗

arXiv2025

Bulk Ferroelectric Heterostructures: Imprinted Actuators

Domain switching is the cornerstone of ferroelectric materials. Most associated functionalities can be tuned via domain switching, including but not limited to piezoelectricity, thermal conductivity, domain wall conductivity and topological structures. However, achieving the full potential of reversible ferroelectric domain switching is restricted by the incomplete access to the entire ferroelectric texture space, as well as the memory effects and energy dissipation associated with the hysteretic nature of ferroelectrics. The manipulation of domain switching behaviour is moderately attainable in epitaxial heterostructures by exploiting the valence or lattice mismatch at heterointerfaces, which is generally constrained by the necessity for two dimensional architectures. In this study, domain-engineered bulk ferroelectric heterostructures (DE-BFH), constructed via elemental partitioning, are employed to unleash full potential of bulk ferroelectrics, providing comprehensive control of domain switching characteristics and adjustable reversibility within the entire range of ferroelectric texture space. Exemplar DE-BFH ceramics exhibit unprecedented enhancement in reversible electrostrain and stability in both axial and shear modes, including a record high peak to peak shear strain up to 0.9% at intermediate field levels, confirmed by digital image correlation measurements and in-situ synchrotron XRD studies. The advancement of domain switching behaviour in DE-BFH could also promote development of new types of lead-free piezoelectric devices, including actuators, energy harvesters, multiple state memory devices, and domain wall switch. Moreover, design concept of DE-BFH could contribute to the creation of distinctive ferroelastic, ferromagnetic, and multiferroic materials by broadening its scope to the entire ferroic family, encompassing polycrystalline, single-crystal, and thin-film forms.

cond-mat.mtrl-sci↗

arXiv2025

Joint Contact Planning for Navigation and Communication in GNSS-Libration Point Systems

Deploying satellites at Earth-Moon Libration Points (LPs) addresses the inherent deep-space coverage gaps of low-altitude GNSS constellations. Integrating LP satellites with GNSS into a joint constellation enables a more robust and comprehensive Positioning, Navigation, and Timing (PNT) system, while also extending navigation and communication services to spacecraft operating in cislunar space (i.e., users). However, the long propagation delays between LP satellites, users, and GNSS satellites result in significantly different link durations compared to those within the GNSS constellation. Scheduling inter-satellite links (ISLs) is a core task of Contact Plan Design (CPD). Existing CPD approaches focus exclusively on GNSS constellations, assuming uniform link durations, and thus cannot accommodate the heterogeneous link timescales present in a joint GNSS-LP system. To overcome this limitation, we introduce a Joint CPD (J-CPD) scheme tailored to handle ISLs with differing duration units across integrated constellations. The key contributions of J-CPD are: (i):introduction of LongSlots (Earth-Moon scale links) and ShortSlots (GNSS-scale links); (ii):a hierarchical and crossed CPD process for scheduling LongSlots and ShortSlots ISLs; (iii):an energy-driven link scheduling algorithm adapted to the CPD process. Simulations on a joint BeiDou-LP constellation demonstrate that J-CPD surpasses the baseline FCP method in both delay and ranging coverage, while maintaining high user satisfaction and enabling tunable trade-offs through adjustable potential-energy parameters. To our knowledge, this is the first CPD framework to jointly optimize navigation and communication in GNSS-LP systems, representing a key step toward unified and resilient deep-space PNT architectures.

eess.SY↗

arXiv2025

Bulk Ferroelectric Heterostructures for High Temperature Lead-Free Piezoelectrics

Remarkable exploitation of valence and lattice mismatch in epitaxial ferroelectric heterostructures generates physical effects not classically expected for perovskite oxides, such as 2D electron gas and polar skyrmions. However the widespread application of these interfacial properties and functionalities is impeded by the ultrathin layered structure and essential presence of underlying lattice-matched substrates for the deposition of epitaxial thin films. Here, we report a bottom-up pathway to synthesize bulk ferroelectric heterostructures (BFH) with periodic composition fluctuation (8 nm in wavelength) using elemental partitioning by cation diffusion, providing opportunities to exploit novel characteristics of hetero-epitaxial oxide thin films in bulk materials. Exemplar monolithic BiFeO3-BaTiO3 BFH ceramics described herein share common features with their thin film heterostructure counterparts, which facilitates control and stabilisation of ferroelectric polarisation along with a significant enhancement in Curie temperature, Tc, and functionality. BFH ceramics exhibit a record Tc (up to 824 {\deg}C) and a piezoelectric coefficient (d33 = 115 pC N-1 ), in comparison with other perovskite or non-perovskite solid solutions, providing sustainable solutions for emergent high temperature piezoelectric sensing, actuation and energy conversion applications. By creating BFH ceramics using different electromechanical boundary conditions, distinct morphologies of aliovalent A-site cation segregated regions along with different types of ferroelectric order are achieved. This formation mechanism provides unprecedented control over local ferroelectric ordering and domain stabilisation in BFH ceramics; it also paves the way to explore new types of functionality, beyond those achievable in both bulk ferroelectrics and thin film heterostructures.

cond-mat.mtrl-sci↗

arXiv2025

Multi-Agent Systems for Robotic Autonomy with LLMs

Since the advent of Large Language Models (LLMs), various research based on such models have maintained significant academic attention and impact, especially in AI and robotics. In this paper, we propose a multi-agent framework with LLMs to construct an integrated system for robotic task analysis, mechanical design, and path generation. The framework includes three core agents: Task Analyst, Robot Designer, and Reinforcement Learning Designer. Outputs are formatted as multimodal results, such as code files or technical reports, for stronger understandability and usability. To evaluate generalizability comparatively, we conducted experiments with models from both GPT and DeepSeek. Results demonstrate that the proposed system can design feasible robots with control strategies when appropriate task inputs are provided, exhibiting substantial potential for enhancing the efficiency and accessibility of robotic system development in research and industrial applications.

cs.RO↗

arXiv2025

Optimized Contact Plan Design for Reflector and Phased Array Terminals in Cislunar Space Networks

Cislunar space is emerging as a critical domain for human exploration, requiring robust infrastructure to support spatial users-spacecraft with navigation and communication demands. Deploying satellites at Earth-Moon three-body orbits offers an effective solution to construct cislunar space infrastructure (CLSI). However, scheduling satellite links to serve users necessitates an appropriate contact plan design (CPD) scheme. Existing CPD schemes focus solely on inter-satellite link scheduling, overlooking their role in providing services to users. This paper introduces a CPD scheme that considers two classes of satellite transponders: Reflector Links (RL) for high-volume data transfers and Phased Array Links (PL) for fast switching and navigation services. Our approach supports both satellites and spatial users in cislunar space. Simulations validate the scheme, demonstrating effective support for user while meeting satellite ranging and communication requirements. These findings provide essential insights for developing future Cislunar Space Infrastructures.

eess.SY↗

arXiv2025

Contact Plan Design for Cross-Linked GNSSs: An ILP Approach for Extended Applications

Global Navigation Satellite Systems (GNSS) employ inter-satellite links (ISLs) to reduce dependency on ground stations, enabling precise ranging and communication across satellites. Beyond their traditional role, ISLs can support extended applications, including providing navigation and communication services to external entities. However, designing effective contact plan design (CPD) schemes for these multifaceted ISLs, operating under a polling time-division duplex (PTDD) framework, remains a critical challenge. Existing CPD approaches focus solely on meeting GNSS satellites' internal ranging and communication demands, neglecting their extended applications. This paper introduces the first CPD scheme capable of supporting extended GNSS ISLs. By modeling GNSS requirements and designing a tailored service process, our approach ensures the allocation of essential resources for internal operations while accommodating external user demands. Based on the BeiDou constellation, simulation results demonstrate the proposed scheme's efficacy in maintaining core GNSS functionality while providing extended ISLs on a best-effort basis. Additionally, the results highlight the significant impact of GNSS ISLs in enhancing orbit determination and clock synchronization for the Earth-Moon libration point constellation, underscoring the importance of extended GNSS ISL applications.

eess.SY↗

arXiv2025

Distributed Online Task Assignment via Inexact ADMM for unplanned online tasks and its Applications to Security

In multi-robot system (MRS) applications, efficient task assignment is essential not only for coordinating agents and ensuring mission success but also for maintaining overall system security. In this work, we first propose an optimization-based distributed task assignment algorithm that dynamically assigns mandatory security-critical tasks and optional tasks among teams. Leveraging an inexact Alternating Direction Method of Multipliers (ADMM)-based approach, we decompose the task assignment problem into separable and non-separable subproblems. The non-separable subproblems are transformed into an inexact ADMM update by projected gradient descent, which can be performed through several communication steps within the team. In the second part of this paper, we formulate a comprehensive framework that enables MRS under plan-deviation attacks to handle online tasks without compromising security. The process begins with a security analysis that determines whether an online task can be executed securely by a robot and, if so, the required time and location for the robot to rejoin the team. Next, the proposed task assignment algorithm is used to allocate security-related tasks and verified online tasks. Finally, task fulfillment is managed using a Control Lyapunov Function (CLF)-based controller, while security enforcement is ensured through a Control Barrier Function (CBF)-based security filter. Through simulations, we demonstrate that the proposed framework allows MRS to effectively respond to unplanned online tasks while maintaining security guarantees.

cs.MA↗

arXiv2025

RECOVER: Designing a Large Language Model-based Remote Patient Monitoring System for Postoperative Gastrointestinal Cancer Care

Cancer surgery is a key treatment for gastrointestinal (GI) cancers, a group of cancers that account for more than 35% of cancer-related deaths worldwide, but postoperative complications are unpredictable and can be life-threatening. In this paper, we investigate how recent advancements in large language models (LLMs) can benefit remote patient monitoring (RPM) systems through clinical integration by designing RECOVER, an LLM-powered RPM system for postoperative GI cancer care. To closely engage stakeholders in the design process, we first conducted seven participatory design sessions with five clinical staff and interviewed five cancer patients to derive six major design strategies for integrating clinical guidelines and information needs into LLM-based RPM systems. We then designed and implemented RECOVER, which features an LLM-powered conversational agent for cancer patients and an interactive dashboard for clinical staff to enable efficient postoperative RPM. Finally, we used RECOVER as a pilot system to assess the implementation of our design strategies with four clinical staff and five patients, providing design implications by identifying crucial design elements, offering insights on responsible AI, and outlining opportunities for future LLM-powered RPM systems.

cs.HC↗

arXiv2024

Disturbance Observer-Parameterized Control Barrier Function with Adaptive Safety Bounds

This letter presents a nonlinear disturbance observer-parameterized control barrier function (DOp-CBF) designed for a robust safety control system under external disturbances. This framework emphasizes that the safety bounds are relevant to the disturbances, acknowledging the critical impact of disturbances on system safety. This work incorporates a disturbance observer (DO) as an adaptive mechanism of the safety bounds design. Instead of considering the worst-case scenario, the safety bounds are dynamically adjusted using DO. The forward invariance of the proposed method regardless of the observer error is ensured, and the corresponding optimal control formulation is presented. The performance of the proposed method is demonstrated through simulations of a cruise control problem under varying road grades. The influence of road grade on the safe distance between vehicles is analyzed and managed using a DO. The results demonstrate the advantages of this approach in maintaining safety and improving system performance under disturbances.

eess.SY↗

arXiv2024

On the Lack of Robustness of Binary Function Similarity Systems

Binary function similarity, which often relies on learning-based algorithms to identify what functions in a pool are most similar to a given query function, is a sought-after topic in different communities, including machine learning, software engineering, and security. Its importance stems from the impact it has in facilitating several crucial tasks, from reverse engineering and malware analysis to automated vulnerability detection. Whereas recent work cast light around performance on this long-studied problem, the research landscape remains largely lackluster in understanding the resiliency of the state-of-the-art machine learning models against adversarial attacks. As security requires to reason about adversaries, in this work we assess the robustness of such models through a simple yet effective black-box greedy attack, which modifies the topology and the content of the control flow of the attacked functions. We demonstrate that this attack is successful in compromising all the models, achieving average attack success rates of 57.06% and 95.81% depending on the problem settings (targeted and untargeted attacks). Our findings are insightful: top performance on clean data does not necessarily relate to top robustness properties, which explicitly highlights performance-robustness trade-offs one should consider when deploying such models, calling for further research.

cs.CR↗

arXiv2024

SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI

The rapid advancement of AI technology, particularly in generating AI-generated content (AIGC), has transformed numerous fields, e.g., art video generation, but also brings new risks, including the misuse of AI for misinformation and intellectual property theft. To address these concerns, AIGC watermarks offer an effective solution to mitigate malicious activities. However, existing watermarking surveys focus more on traditional watermarks, overlooking AIGC-specific challenges. In this work, we propose a systematic investigation into AIGC watermarking and provide the first formal definition of AIGC watermarking. Different from previous surveys, we provide a taxonomy based on the core properties of the watermark which are summarized through comprehensive literature from various AIGC modalities. Derived from the properties, we discuss the functionality and security threats of AIGC watermarking. In the end, we thoroughly investigate the AIGC governance of different countries and practitioners. We believe this taxonomy better aligns with the practical demands for watermarking in the era of GenAI, thus providing a clearer summary of existing work and uncovering potential future research directions for the community.

cs.CR↗