arXiv subjects

Weisi Guo

Publications and source records attributed to Weisi Guo.

At least 19 recordsLinked to original sources

arXiv2026

AI Evasion and Impersonation Attacks on Facial Re-Identification with Activation Map Explanations

Facial identification systems are increasingly deployed in surveillance and yet their vulnerability to adversarial evasion and impersonation attacks pose a critical risk. This paper introduces a novel framework for generating adversarial patches capable of both evasion and impersonation attacks against deep re-identification models across non-overlapping cameras. Unlike prior approaches that require iterative patch optimisation for each target, our method employs a conditional encoder-decoder network to synthesize adversarial patches in a single forward pass, guided by multi-scale features from source and target images. The patches are optimised with a dual adversarial objective comprising of pull and push terms. To enhance imperceptibility and aid physical deployment, we further integrate naturalistic patch generation using pre-trained latent diffusion models. Experiments on standard pedestrian (Market-1501, DukeMTMCreID) and facial recognition benchmarks (CelebA-HQ, PubFig) datasets demonstrate the effectiveness of the proposed method. Our adversarial evasion attacks reduce mean Average Precision from 90% to 0.4% in white-box settings and from 72% to 0.4% in black-box settings, showing strong cross-model generalization. In targeted impersonation attacks, our framework achieves a success rate of 27% on CelebA-HQ, competing with other patch-based methods. We go further to use clustering of activation maps to interpret which features are most used by adversarial attacks and propose a pathway for future countermeasures. The results highlight the practicality of adversarial patch attacks on retrieval-based systems and underline the urgent need for robust defense strategies.

cs.CV↗

arXiv2026

Agentic Assistant for 6G: Turn-based Conversations for AI-RAN Hierarchical Co-Management

New generations of radio access networks (RAN), especially with native AI services are increasingly difficult for human engineers to manage in real-time. Enterprise networks are often managed locally, where expertise is scarce. Existing research has focused on creating Retrieval-Augmented Generation (RAG) LLMs that can help to plan and configure RAN and core aspects only. Co-management of RAN and edge AI is the gap, which creates hierarchical and dynamic problems that require turn-based human interactions. Here, we create an agentic network manager and turn-based conversation assistant that can understand human intent-based queries that match hierarchical problems in AI-RAN. The framework constructed consists of: (a) a user interface and evaluation dashboard, (b) an intelligence layer that interfaces with the AI-RAN, and (c) a knowledge layer for providing the basis for evaluations and recommendations. These form 3 layers of capability with the following validation performances (average response time 13s): (1) design and planning a service (78\% accuracy), (2) operating specific AI-RAN tools (89\% accuracy), and (3) tuning AI-RAN performance (67\%). These initial results indicate the universal challenges of hallucination but also fast response performance success that can really reduce OPEX costs for small scale enterprise users.

cs.NI↗

arXiv2026

Dialogue Telemetry: Turn-Level Instrumentation for Autonomous Information Gathering

Autonomous systems conducting schema-grounded information-gathering dialogues face an instrumentation gap, lacking turn-level observables for monitoring acquisition efficiency and detecting when questioning becomes unproductive. We introduce Dialogue Telemetry (DT), a measurement framework that produces two model-agnostic signals after each question-answer exchange: (i) a Progress Estimator (PE) quantifying residual information potential per category (with a bits-based variant), and (ii) a Stalling Index (SI) detecting an observable failure signature characterized by repeated category probing with semantically similar, low-marginal-gain responses. SI flags this pattern without requiring causal diagnosis, supporting monitoring in settings where attributing degradation to specific causes may be impractical. We validate DT in controlled search-and-rescue (SAR)-inspired interviews using large language model (LLM)-based simulations, distinguishing efficient from stalled dialogue traces and illustrating downstream utility by integrating DT signals into a reinforcement learning (RL) policy. Across these settings, DT provides interpretable turn-level instrumentation that improves policy performance when stalling carries operational costs.

cs.CL↗

arXiv2025

Guaranteeing and Explaining Stability across Heterogeneous Load Balancing using Calculus Network Dynamics

Load balancing between base stations (BSs) allows BS capacity to be efficiently utilised and avoid outages. Currently, data-driven mechanisms strive to balance inter-BS load and reduce unnecessary handovers. The challenge is that over a large number of BSs, networks observe an oscillatory effect of load evolution that causes high inter-BS messaging. Without a calculus function that integrates network topology to describe the evolution of load states, current data-driven algorithms cannot explain the oscillation phenomenon observed in load states, nor can they provide theoretical guarantees on the stability of the ideal synchronised state. Whilst we know load state oscillation is coupled with the load balancing process algorithms and the topology structure of inter-BS boundary relations, we do not have a theoretical framework to prove this and a pathway to improving load balancing algorithms. Here, we abstract generic and heterogeneous data-driven algorithms into a calculus dynamics space, so that we can establish the synchronization conditions for networked load balancing dynamics with any network topology. By incorporating what is known as "non-conservative error" and the eigenvalue spectrum of the networked dynamics, we can adjust the inter-BS load balancing mechanisms to achieve high efficiency and convergence guarantee, or to mitigate the oscillation when the synchronisation condition cannot be satisfied.

eess.SY↗

arXiv2025

Real-Time Bayesian Detection of Drift-Evasive GNSS Spoofing in Reinforcement Learning Based UAV Deconfliction

Autonomous unmanned aerial vehicles (UAVs) rely on global navigation satellite system (GNSS) pseudorange measurements for accurate real-time localization and navigation. However, this dependence exposes them to sophisticated spoofing threats, where adversaries manipulate pseudoranges to deceive UAV receivers. Among these, drift-evasive spoofing attacks subtly perturb measurements, gradually diverting the UAVs trajectory without triggering conventional signal-level anti-spoofing mechanisms. Traditional distributional shift detection techniques often require accumulating a threshold number of samples, causing delays that impede rapid detection and timely response. Consequently, robust temporal-scale detection methods are essential to identify attack onset and enable contingency planning with alternative sensing modalities, improving resilience against stealthy adversarial manipulations. This study explores a Bayesian online change point detection (BOCPD) approach that monitors temporal shifts in value estimates from a reinforcement learning (RL) critic network to detect subtle behavioural deviations in UAV navigation. Experimental results show that this temporal value-based framework outperforms conventional GNSS spoofing detectors, temporal semi-supervised learning frameworks, and the Page-Hinkley test, achieving higher detection accuracy and lower false-positive and false-negative rates for drift-evasive spoofing attacks.

cs.LG↗

arXiv2025

KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligence

The emergence of large language models (LLMs) and agentic systems is enabling autonomous 6G networks with advanced intelligence, including self-configuration, self-optimization, and self-healing. However, the current implementation of individual intelligence tasks necessitates isolated knowledge retrieval pipelines, resulting in redundant data flows and inconsistent interpretations. Inspired by the service model unification effort in Open-RAN (to support interoperability and vendor diversity), we propose KP-A: a unified Network Knowledge Plane specifically designed for Agentic network intelligence. By decoupling network knowledge acquisition and management from intelligence logic, KP-A streamlines development and reduces maintenance complexity for intelligence engineers. By offering an intuitive and consistent knowledge interface, KP-A also enhances interoperability for the network intelligence agents. We demonstrate KP-A in two representative intelligence tasks: live network knowledge Q&A and edge AI service orchestration. All implementation artifacts have been open-sourced to support reproducibility and future standardization efforts.

cs.NI↗

arXiv2025

Meta Policy Switching for Secure UAV Deconfliction in Adversarial Airspace

Autonomous UAV navigation using reinforcement learning (RL) is vulnerable to adversarial attacks that manipulate sensor inputs, potentially leading to unsafe behavior and mission failure. Although robust RL methods provide partial protection, they often struggle to generalize to unseen or out-of-distribution (OOD) attacks due to their reliance on fixed perturbation settings. To address this limitation, we propose a meta-policy switching framework in which a meta-level polic dynamically selects among multiple robust policies to counter unknown adversarial shifts. At the core of this framework lies a discounted Thompson sampling (DTS) mechanism that formulates policy selection as a multi-armed bandit problem, thereby minimizing value distribution shifts via self-induced adversarial observations. We first construct a diverse ensemble of action-robust policies trained under varying perturbation intensities. The DTS-based meta-policy then adaptively selects among these policies online, optimizing resilience against self-induced, piecewise-stationary attacks. Theoretical analysis shows that the DTS mechanism minimizes expected regret, ensuring adaptive robustness to OOD attacks and exhibiting emergent antifragile behavior under uncertainty. Extensive simulations in complex 3D obstacle environments under both white-box (Projected Gradient Descent) and black-box (GPS spoofing) attacks demonstrate significantly improved navigation efficiency and higher conflict free trajectory rates compared to standard robust and vanilla RL baselines, highlighting the practical security and dependability benefits of the proposed approach.

cs.LG↗

arXiv2025

Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments

Autonomous unmanned aerial vehicles (UAVs) increasingly rely on reinforcement learning (RL) for navigation. However, global navigation satellite system (GNSS) spoofing attacks can induce out-of-distribution observation shifts that corrupt value estimation and degrade mission performance. Existing robust RL approaches typically improve resilience against specific attack models but often fail to generalize to attacks not encountered during training. To address this limitation, we propose a curriculum-guided adaptation framework that progressively exposes a robust policy to gradient-based adversarial observation perturbations of increasing intensity while aligning temporal-difference (TD) error distributions across curriculum stages. Rather than adapting to a particular attack model, the proposed approach preserves TD-error consistency to promote transferability across attack conditions. We further derive a TD-space generalization certificate showing that if the TD-error distribution induced by a test-time attack remains sufficiently close to that of the final curriculum stage, the resulting performance degradation is bounded. The framework is evaluated in a UAV deconfliction environment with dynamic 3D obstacles under previously unseen fixed and dynamic GNSS spoofing attacks. Under fixed spoofing conditions, the curriculum-adapted policy achieved near-perfect mission success rates, compared with 20-56% for standard and robust RL baselines. Under dynamic obstacle-luring spoofing attacks, it achieved the highest episodic rewards while reducing mission completion steps by up to 45% across increasing aerial traffic densities.

cs.LG↗

arXiv2025

Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks

The growing integration of UAVs into civilian airspace underscores the need for resilient and intelligent intrusion detection systems (IDS), as traditional anomaly detection methods often fail to identify novel threats. A common approach treats unfamiliar attacks as out-of-distribution (OOD) samples; however, this leaves systems vulnerable when mitigation is inadequate. Moreover, conventional OOD detectors struggle to distinguish stealthy adversarial attacks from genuine OOD events. This paper introduces a conditional generative adversarial network (cGAN)-based framework for crafting stealthy adversarial attacks that evade IDS mechanisms. We first design a robust multi-class IDS classifier trained on benign UAV telemetry and known cyber-attacks, including Denial of Service (DoS), false data injection (FDI), man-in-the-middle (MiTM), and replay attacks. Using this classifier, our cGAN perturbs known attacks to generate adversarial samples that misclassify as benign while retaining statistical resemblance to OOD distributions. These adversarial samples are iteratively refined to achieve high stealth and success rates. To detect such perturbations, we implement a conditional variational autoencoder (CVAE), leveraging negative log-likelihood to separate adversarial inputs from authentic OOD samples. Comparative evaluation shows that CVAE-based regret scores significantly outperform traditional Mahalanobis distance-based detectors in identifying stealthy adversarial threats. Our findings emphasize the importance of advanced probabilistic modeling to strengthen IDS capabilities against adaptive, generative-model-based cyber intrusions.

cs.LG↗

arXiv2025

A Signal Contract for Online Language Grounding and Discovery in Decision-Making

Autonomous systems increasingly receive time-sensitive contextual updates from humans through natural language, yet embedding language understanding inside decision-makers couples grounding to learning or planning. This increases redeployment burden when language conventions or domain knowledge change and can hinder diagnosability by confounding grounding errors with control errors. We address online language grounding where messy, evolving verbal reports are converted into control-relevant signals during execution through an interface that localises language updates while keeping downstream decision-makers language-agnostic. We propose LUCIFER (Language Understanding and Context-Infused Framework for Exploration and Behavior Refinement), an inference-only middleware that exposes a Signal Contract. The contract provides four outputs, policy priors, reward potentials, admissible-option constraints, and telemetry-based action prediction for efficient information gathering. We validate LUCIFER in a search-and-rescue (SAR)-inspired testbed using dual-phase, dual-client evaluation: (i) component benchmarks show reasoning-based extraction remains robust on self-correcting reports where pattern-matching baselines degrade, and (ii) system-level ablations with two structurally distinct clients (hierarchical RL and a hybrid A*+heuristics planner) show consistent necessity and synergy. Grounding improves safety, discovery improves information-collection efficiency, and only their combination achieves both.

cs.AI↗

arXiv2025

Learning What Matters Now: A Dual-Critic Context-Aware RL Framework for Priority-Driven Information Gain

Autonomous systems operating in high-stakes search-and-rescue (SAR) missions must continuously gather mission-critical information while flexibly adapting to shifting operational priorities. We propose CA-MIQ (Context-Aware Max-Information Q-learning), a lightweight dual-critic reinforcement learning (RL) framework that dynamically adjusts its exploration strategy whenever mission priorities change. CA-MIQ pairs a standard extrinsic critic for task reward with an intrinsic critic that fuses state-novelty, information-location awareness, and real-time priority alignment. A built-in shift detector triggers transient exploration boosts and selective critic resets, allowing the agent to re-focus after a priority revision. In a simulated SAR grid-world, where experiments specifically test adaptation to changes in the priority order of information types the agent is expected to focus on, CA-MIQ achieves nearly four times higher mission-success rates than baselines after a single priority shift and more than three times better performance in multiple-shift scenarios, achieving 100% recovery while baseline methods fail to adapt. These results highlight CA-MIQ's effectiveness in any discrete environment with piecewise-stationary information-value distributions.

cs.AI↗

arXiv2025

Parameter Estimation using Reinforcement Learning Causal Curiosity: Limits and Challenges

Causal understanding is important in many disciplines of science and engineering, where we seek to understand how different factors in the system causally affect an experiment or situation and pave a pathway towards creating effective or optimising existing models. Examples of use cases are autonomous exploration and modelling of unknown environments or assessing key variables in optimising large complex systems. In this paper, we analyse a Reinforcement Learning approach called Causal Curiosity, which aims to estimate as accurately and efficiently as possible, without directly measuring them, the value of factors that causally determine the dynamics of a system. Whilst the idea presents a pathway forward, measurement accuracy is the foundation of methodology effectiveness. Focusing on the current causal curiosity's robotic manipulator, we present for the first time a measurement accuracy analysis of the future potentials and current limitations of this technique and an analysis of its sensitivity and confounding factor disentanglement capability - crucial for causal analysis. As a result of our work, we promote proposals for an improved and efficient design of Causal Curiosity methods to be applied to real-world complex scenarios.

cs.RO↗

arXiv2025

Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients

Federated Learning (FL) has the potential for simultaneous global learning amongst a large number of parallel agents, enabling emerging AI such as LLMs to be trained across demographically diverse data. Central to this being efficient is the ability for FL to perform sparse gradient updates and remote direct memory access at the central server. Most of the research in FL security focuses on protecting data privacy at the edge client or in the communication channels between the client and server. Client-facing attacks on the server are less well investigated as the assumption is that a large collective of clients offer resilience. Here, we show that by attacking certain clients that lead to a high frequency repetitive memory update in the server, we can remote initiate a rowhammer attack on the server memory. For the first time, we do not need backdoor access to the server, and a reinforcement learning (RL) attacker can learn how to maximize server repetitive memory updates by manipulating the client's sensor observation. The consequence of the remote rowhammer attack is that we are able to achieve bit flips, which can corrupt the server memory. We demonstrate the feasibility of our attack using a large-scale FL automatic speech recognition (ASR) systems with sparse updates, our adversarial attacking agent can achieve around 70% repeated update rate (RUR) in the targeted server model, effectively inducing bit flips on server DRAM. The security implications are that can cause disruptions to learning or may inadvertently cause elevated privilege. This paves the way for further research on practical mitigation strategies in FL and hardware design.

cs.LG↗

arXiv2025

Building AI Service Repositories for On-Demand Service Orchestration in 6G AI-RAN

Efficient orchestration of AI services in 6G AI-RAN requires well-structured, ready-to-deploy AI service repositories combined with orchestration methods adaptive to diverse runtime contexts across radio access, edge, and cloud layers. Current literature lacks comprehensive frameworks for constructing such repositories and generally overlooks key practical orchestration factors. This paper systematically identifies and categorizes critical attributes influencing AI service orchestration in 6G networks and introduces an open-source, LLM-assisted toolchain that automates service packaging, deployment, and runtime profiling. We validate the proposed toolchain through the Cranfield AI Service repository case study, demonstrating significant automation benefits, reduced manual coding efforts, and the necessity of infrastructure-specific profiling, paving the way for more practical orchestration frameworks.

cs.AI↗

arXiv2025

Data-driven Method to Ensure Cascade Stability of Traffic Load Balancing in O-RAN Based Networks

Load balancing in open radio access networks (O-RAN) is critical for ensuring efficient resource utilization, and the user's experience by evenly distributing network traffic load. Current research mainly focuses on designing load-balancing algorithms to allocate resources while overlooking the cascade stability of load balancing, which is critical to prevent endless handover. The main challenge to analyse the cascade stability lies in the difficulty of establishing an accurate mathematical model to describe the process of load balancing due to its nonlinearity and high-dimensionality. In our previous theoretical work, a simplified general dynamic function was used to analyze the stability. However, it is elusive whether this function is close to the reality of the load balance process. To solve this problem, 1) a data-driven method is proposed to identify the dynamic model of the load balancing process according to the real-time traffic load data collected from the radio units (RUs); 2) the stability condition of load balancing process is established for the identified dynamics model. Based on the identified dynamics model and the stability condition, the RAN Intelligent Controller (RIC) can control RUs to achieve a desired load-balancing state while ensuring cascade stability.

eess.SY↗

arXiv2025

Contextualized Autonomous Drone Navigation using LLMs Deployed in Edge-Cloud Computing

Autonomous navigation is usually trained offline in diverse scenarios and fine-tuned online subject to real-world experiences. However, the real world is dynamic and changeable, and many environmental encounters/effects are not accounted for in real-time due to difficulties in describing them within offline training data or hard to describe even in online scenarios. However, we know that the human operator can describe these dynamic environmental encounters through natural language, adding semantic context. The research is to deploy Large Language Models (LLMs) to perform real-time contextual code adjustment to autonomous navigation. The challenge not evaluated in literature is what LLMs are appropriate and where should these computationally heavy algorithms sit in the computation-communication edge-cloud computing architectures. In this paper, we evaluate how different LLMs can adjust both the navigation map parameters dynamically (e.g., contour map shaping) and also derive navigation task instruction sets. We then evaluate which LLMs are most suitable and where they should sit in future edge-cloud of 6G telecommunication architectures.

cs.RO↗

arXiv2025

RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning

Mixed-precision computing, a widely applied technique in AI, offers a larger trade-off space between accuracy and efficiency. The recent purposed Mixed-Precision Over-the-Air Federated Learning (MP-OTA-FL) enables clients to operate at appropriate precision levels based on their heterogeneous hardware, taking advantages of the larger trade-off space while covering the quantization overheads in the mixed-precision modulation scheme for the OTA aggregation process. A key to further exploring the potential of the MP-OTA-FL framework is the optimization of client precision levels. The choice of precision level hinges on multifaceted factors including hardware capability, potential client contribution, and user satisfaction, among which factors can be difficult to define or quantify. In this paper, we propose a RAG-based User Profiling for precision planning framework that integrates retrieval-augmented LLMs and dynamic client profiling to optimize satisfaction and contributions. This includes a hybrid interface for gathering device/user insights and an RAG database storing historical quantization decisions with feedback. Experiments show that our method boosts satisfaction, energy savings, and global model accuracy in MP-OTA-FL systems.

cs.LG↗

arXiv2025

End-to-End Edge AI Service Provisioning Framework in 6G ORAN

With the advent of 6G, Open Radio Access Network (O-RAN) architectures are evolving to support intelligent, adaptive, and automated network orchestration. This paper proposes a novel Edge AI and Network Service Orchestration framework that leverages Large Language Model (LLM) agents deployed as O-RAN rApps. The proposed LLM-agent-powered system enables interactive and intuitive orchestration by translating the user's use case description into deployable AI services and corresponding network configurations. The LLM agent automates multiple tasks, including AI model selection from repositories (e.g., Hugging Face), service deployment, network adaptation, and real-time monitoring via xApps. We implement a prototype using open-source O-RAN projects (OpenAirInterface and FlexRIC) to demonstrate the feasibility and functionality of our framework. Our demonstration showcases the end-to-end flow of AI service orchestration, from user interaction to network adaptation, ensuring Quality of Service (QoS) compliance. This work highlights the potential of integrating LLM-driven automation into 6G O-RAN ecosystems, paving the way for more accessible and efficient edge AI ecosystems.

cs.NI↗