arXiv subjects

Mingyue Zhang

Publications and source records attributed to Mingyue Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Three asymptotic regimes for a Keller--Segel system with volume-filling effect

We study three asymptotic regimes for a parabolic--elliptic Keller--Segel system with porous-medium diffusion and the volume-filling sensitivity function $u(1-u)$. First, when $D=ε^2\to0$, $δ=1$, $1\le m<2$, and the chemotactic sensitivity coefficient is below an explicit $m$-dependent parabolicity threshold, we prove strong convergence to a scalar nonlinear diffusion equation. Second, as $δ=ε\to0$ with $D=1$, we obtain a hyperbolic--elliptic Keller--Segel limit by means of a kinetic reduction argument. Third, under the scaling $χ=ε^{-1}$, $D=ε^2$, $δ=1$, and for characteristic initial data of finite perimeter, we prove convergence on fixed time intervals to the time-independent initial patch in $BV(Ω;\{0,1\})$. Because the sensitivity function is nonlinear in the density, weak convergence alone is insufficient to identify the chemotactic flux. Strong compactness of the density is therefore required in all three regimes and is obtained, respectively, through energy--entropy estimates, kinetic reduction, and $BV$ compactness.

math.AP↗

arXiv2026

Analysis of a multispecies cross-diffusion Keller-Segel system with volume filling

A chemotaxis-driven multiphase multispecies diffusion system, arising in the formation of vascular-like structures, is analyzed. The model couples porous-medium-type cross-diffusion equations for the volume fractions of the cellular components with multispecies Keller-Segel equations governing the chemoattractant concentrations, posed in a bounded domain with no-flux boundary conditions. The system is derived within a multiphase framework based on mass and force balance laws, together with a characterization of the mixture pressure gradient, which follows from the volume-filling constraint. The existence of a global weak solution, the weak-strong uniqueness property, the exponential decay to the constant steady state, and the vanishing diffusion limit are established. The existence proof extends the boundedness-by-entropy method to solution codomains that are bounded in some directions only, while the other results are based on various entropy estimates and uniform dissipation bounds.

math.AP↗

arXiv2026

Simulation of Language Evolution under Regulated Social Media Platforms: A Synergistic Approach of Large Language Models and Genetic Algorithms

Social media platforms frequently impose restrictive policies to moderate user content, prompting the emergence of creative evasion language strategies. This paper presents a multi-agent framework based on Large Language Models (LLMs) to simulate the iterative evolution of language strategies under regulatory constraints. In this framework, participant agents, as social media users, continuously evolve their language expression, while supervisory agents emulate platform-level regulation by assessing policy violations. To achieve a more faithful simulation, we employ a dual design of language strategies (constraint and expression) to differentiate conflicting goals and utilize an LLM-driven GA (Genetic Algorithm) for the selection, mutation, and crossover of language strategies. The framework is evaluated using two distinct scenarios: an abstract password game and a realistic simulated illegal pet trade scenario. Experimental results demonstrate that as the number of dialogue rounds increases, both the number of uninterrupted dialogue turns and the accuracy of information transmission improve significantly. Furthermore, a user study with 40 participants validates the real-world relevance of the generated dialogues and strategies. Moreover, ablation studies validate the importance of the GA, emphasizing its contribution to long-term adaptability and improved overall results.

cs.SI↗

arXiv2026

SAGE: Semantic-Aware Gray-Box Game Regression Testing with Large Language Models

The rapid iteration cycles of modern live-service games make regression testing indispensable for maintaining quality and stability. However, existing regression testing approaches face critical limitations, especially in common gray-box settings where full source code access is unavailable: they heavily rely on manual effort for test case construction, struggle to maintain growing suites plagued by redundancy, and lack efficient mechanisms for prioritizing relevant tests. These challenges result in excessive testing costs, limited automation, and insufficient bug detection. To address these issues, we propose SAGE, a semanticaware regression testing framework for gray-box game environments. SAGE systematically addresses the core challenges of test generation, maintenance, and selection. It employs LLM-guided reinforcement learning for efficient, goal-oriented exploration to automatically generate a diverse foundational test suite. Subsequently, it applies a semantic-based multi-objective optimization to refine this suite into a compact, high-value subset by balancing cost, coverage, and rarity. Finally, it leverages LLM-based semantic analysis of update logs to prioritize test cases most relevant to version changes, enabling efficient adaptation across iterations. We evaluate SAGE on two representative environments, Overcooked Plus and Minecraft, comparing against both automated baselines and human-recorded test cases. Across all environments, SAGE achieves superior bug detection with significantly lower execution cost, while demonstrating strong adaptability to version updates.

cs.SE↗

arXiv2026

Frequency Differences between Clocks on the Earth and the Moon

Based on general relativity, clock frequency comparisons support various geodetic applications on Earth, such as determining the gravitational potential and realizing a global height system. Future lunar clocks could extend these capabilities to the Moon, connect to terrestrial clock-comparison networks, and, in principle, serve as reference clocks. Meanwhile, especially driven by future lunar navigation plans, an independent lunar time system is becoming an urgent requirement and such a system would necessarily remain linked to terrestrial time standards. To support these future applications, we establish a comprehensive model for Earth-Moon clock frequency comparisons. This paper simulates fractional frequency differences between clocks on the Earth (E) and Moon (L) through four time transformations:proper-to-coordinate time for E-clocks and for L-clocks (both linked to the local gravity potential), the conversion between the Earth and Moon coordinate times, and the time signal propagation between E- and L-clocks. Gravity potential differences between the E- and L-clocks impact the frequency difference at the 10^-10 level. The effect of the coordinate time ratio is at 10^-11 level. Contributions from static, tidal, and non-tidal potentials, body self-rotation, and different celestial bodies are evaluated. Furthermore, we quantify the Doppler, atmospheric and Shapiro delay effects in Earth-Moon time signal propagation. For a single link, the first-order Doppler term dominates at the 10^-6 level and masks the gravity-potential and coordinate-time terms. A Doppler-cancelling multi-link strategy is needed to suppress the signal-propagation effect and extract these terms.

physics.geo-ph↗

arXiv2026

Well-posedness and asymptotic limits for a degenerate Keller-Segel system with volume filling

A class of parabolic-parabolic Keller-Segel systems with degenerate diffusion and volume filling is studied in a bounded domain subject to no-flux boundary conditions. The equations are derived from a multiphase fluid model. The interplay between nonlinear diffusion and density saturation leads to a rich variety of behaviors across different parameter regimes. We establish the existence of global weak solutions, a weak-strong uniqueness result, the exponential convergence to the homogeneous steady state, pattern formation in one spatial dimension, as well as the parabolic-elliptic and vanishing diffusion limits. The analysis relies on a priori estimates derived from suitable entropy functionals. Pattern formation is demonstrated by reducing the system to a first-order equation and conducting a detailed analysis of the resulting nonlinearity. Numerical simulations from a one-dimensional finite-volume scheme illustrate the asymptotic regimes.

math.AP↗

arXiv2026

LightGuard: Transparent WiFi Security via Physical-Layer LiFi Key Bootstrapping

WiFi is inherently vulnerable to eavesdropping because RF signals may penetrate many physical boundaries, such as walls and floors. LiFi, by contrast, is an optical method confined to line-of-sight and blocked by opaque surfaces. We present LightGuard, a dual-link architecture built on this insight: cryptographic key establishment can be offloaded from WiFi to a physically confined LiFi channel to mitigate the risk of key exposure over RF. LightGuard derives session keys over a LiFi link and installs them on the WiFi interface, ensuring cryptographic material never traverses the open RF medium. A prototype with off-the-shelf WiFi NICs and our LiFi transceiver frontend validates the design.

cs.CR↗

arXiv2026

TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code

Large Language Models (LLMs) often generate code with subtle but critical bugs, especially for complex tasks. Existing automated repair methods typically rely on superficial pass/fail signals, offering limited visibility into program behavior and hindering precise error localization. In addition, without a way to learn from prior failures, repair processes often fall into repetitive and inefficient cycles. To overcome these challenges, we present TraceCoder, a collaborative multi-agent framework that emulates the observe-analyze-repair process of human experts. The framework first instruments the code with diagnostic probes to capture fine-grained runtime traces, enabling deep insight into its internal execution. It then conducts causal analysis on these traces to accurately identify the root cause of the failure. This process is further enhanced by a novel Historical Lesson Learning Mechanism (HLLM), which distills insights from prior failed repair attempts to inform subsequent correction strategies and prevent recurrence of similar mistakes. To ensure stable convergence, a Rollback Mechanism enforces that each repair iteration constitutes a strict improvement toward the correct solution. Comprehensive experiments across multiple benchmarks show that TraceCoder achieves up to a 34.43\% relative improvement in Pass@1 accuracy over existing advanced baselines. Ablation studies verify the significance of each system component, with the iterative repair process alone contributing a 65.61\% relative gain in accuracy. Furthermore, TraceCoder significantly outperforms leading iterative methods in terms of both accuracy and cost-efficiency.

cs.SE↗

arXiv2026

FAROS: Robust Federated Learning with Adaptive Scaling against Backdoor Attacks

Federated Learning (FL) enables multiple clients to collaboratively train a shared model without exposing local data. However, backdoor attacks pose a significant threat to FL. These attacks aim to implant a stealthy trigger into the global model, causing it to mislead on inputs that possess a specific trigger while functioning normally on benign data. Although pre-aggregation detection is a main defense direction, existing state-of-the-art defenses often rely on fixed defense parameters. This reliance makes them vulnerable to single-point-of-failure risks, rendering them less effective against sophisticated attackers. To address these limitations, we propose FAROS, an enhanced FL framework that incorporates Adaptive Differential Scaling (ADS) and Robust Core-set Computing (RCC). The ADS mechanism adjusts the defense's sensitivity dynamically, based on the dispersion of uploaded gradients by clients in each round. This allows it to counter attackers who strategically shift between stealthiness and effectiveness. Furthermore, the RCC effectively mitigates the risk of single-point failure by computing the centroid of a core set comprising clients with the highest confidence. We conducted extensive experiments across various datasets, models, and attack scenarios. The results demonstrate that our method outperforms current defenses in both attack success rate and main task accuracy.

cs.LG↗

arXiv2025

Graph Contextual Reinforcement Learning for Efficient Directed Controller Synthesis

Controller synthesis is a formal method approach for automatically generating Labeled Transition System (LTS) controllers that satisfy specified properties. The efficiency of the synthesis process, however, is critically dependent on exploration policies. These policies often rely on fixed rules or strategies learned through reinforcement learning (RL) that consider only a limited set of current features. To address this limitation, this paper introduces GCRL, an approach that enhances RL-based methods by integrating Graph Neural Networks (GNNs). GCRL encodes the history of LTS exploration into a graph structure, allowing it to capture a broader, non-current-based context. In a comparative experiment against state-of-the-art methods, GCRL exhibited superior learning efficiency and generalization across four out of five benchmark domains, except one particular domain characterized by high symmetry and strictly local interactions.

cs.AI↗

arXiv2025

Synergizing Code Coverage and Gameplay Intent: Coverage-Aware Game Playtesting with LLM-Guided Reinforcement Learning

The widespread adoption of the "Games as a Service" model necessitates frequent content updates, placing immense pressure on quality assurance. In response, automated game testing has been viewed as a promising solution to cope with this demanding release cadence. However, existing automated testing approaches typically create a dichotomy: code-centric methods focus on structural coverage without understanding gameplay context, while player-centric agents validate high-level intent but often fail to cover specific underlying code changes. To bridge this gap, we propose SMART (Structural Mapping for Augmented Reinforcement Testing), a novel framework that synergizes structural verification and functional validation for game update testing. SMART leverages large language models (LLMs) to interpret abstract syntax tree (AST) differences and extract functional intent, constructing a context-aware hybrid reward mechanism. This mechanism guides reinforcement learning agents to sequentially fulfill gameplay goals while adaptively exploring modified code branches. We evaluate SMART on two environments, Overcooked and Minecraft. The results demonstrate that SMART significantly outperforms state-of-the-art baselines; it achieves over 94% branch coverage of modified code, nearly double that of traditional reinforcement learning methods, while maintaining a 98% task completion rate, effectively balancing structural comprehensiveness with functional correctness.

cs.AI↗

arXiv2025

Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a feedback loop with four core functionalities: monitoring, analyzing, planning, and execution. Recently, generative artificial intelligence (GenAI), especially the area of large language models, has shown impressive performance in data comprehension and logical reasoning. These capabilities are highly aligned with the functionalities required in SASs, suggesting a strong potential to employ GenAI to enhance SASs. However, the specific benefits and challenges of employing GenAI in SASs remain unclear. Yet, providing a comprehensive understanding of these benefits and challenges is complex due to several reasons: limited publications in the SAS field, the technological and application diversity within SASs, and the rapid evolution of GenAI technologies. To that end, this paper aims to provide researchers and practitioners a comprehensive snapshot that outlines the potential benefits and challenges of employing GenAI's within SAS. Specifically, we gather, filter, and analyze literature from four distinct research fields and organize them into two main categories to potential benefits: (i) enhancements to the autonomy of SASs centered around the specific functions of the MAPE-K feedback loop, and (ii) improvements in the interaction between humans and SASs within human-on-the-loop settings. From our study, we outline a research roadmap that highlights the challenges of integrating GenAI into SASs. The roadmap starts with outlining key research challenges that need to be tackled to exploit the potential for applying GenAI in the field of SAS. The roadmap concludes with a practical reflection, elaborating on current shortcomings of GenAI and proposing possible mitigation strategies.

cs.SE↗

arXiv2025

Observation of Quantum-Criticality-Class Crossover at the LaAlO$_3$/KTaO$_3$ (111) Interface

In two-dimensional (2D) limit, the quantum fluctuation is significantly enhanced which could induce a quantum phase transition. Investigating the quantum criticality is an effective approach to elucidate the underlying physics of 2D superconductivity. Here we report the observation of different universality classes of quantum criticality at the superconducting LaAlO$_3$/KTaO$_3$ (111) interface, i.e. the normal quantum Griffiths singularity (QGS) and the anomalous QGS. The switch of the quantum criticality class is observed from the normal QGS in a low $T_c$ sample with weaker spin-orbit coupling (SOC) to the anomalous QGS in a high $T_c$ sample with stronger SOC. Moreover, owing to the remarkable SOC strength, the high $T_c$ sample exhibits an unprecedentedly enhanced quantum fluctuation at an unusually elevated temperature approaching the Berezinskii-Kosterlitz-Thouless (BKT) transition temperature $T_{BKT}$. This work provides comprehensive recognition of the different quantum criticality classes in the same superconducting LaAlO$_3$/KTaO$_3$ (111) system, which deepens the understanding of the superconducting mechanism of interfacial superconductors.

cond-mat.supr-con↗

arXiv2025

Interplay of spin-orbit coupling and trigonal crystal field enhances superconductivity in $LaAlO_3/KTaO_3$ (111)

In conventional superconductors, bulk physical properties typically degrade as the film thickness approaches the two-dimensional (2D) limit. Here in the (111) oriented LaAlO3/KTaO3 (LAO/KTO) heterostructure, we demonstrate experimental evidence that reducing the conducting layer thickness at the interface significantly enhances superconducting transition temperature Tc, in direct contrast to conventional wisdom. From the sum frequency generation (SFG) spectroscopy and superconducting upper-critical field measurements, both the trigonal symmetry and spin orbit scattering are enhanced with the increased Tc. We attribute the enhanced superconductivity (SC) to the synergic interplay between spin-orbit coupling (SOC) and trigonal crystal field, resulting in an enhanced electron-phonon coupling. Furthermore, we show the existence of unconventional SC: the approaching linear temperature dependence of normal state resistance with increasing Tc and the existence of a quantum critical point (QCP) near the superconducting phase. Our findings provide important insight into the underlying mechanism of the strong orientation-dependent KTO interface SC.

cond-mat.supr-con↗

arXiv2025

Knowledge Graph-enhanced Large Language Model for Incremental Game PlayTesting

The rapid iteration and frequent updates of modern video games pose significant challenges to the efficiency and specificity of testing. Although automated playtesting methods based on Large Language Models (LLMs) have shown promise, they often lack structured knowledge accumulation mechanisms, making it difficult to conduct precise and efficient testing tailored for incremental game updates. To address this challenge, this paper proposes a KLPEG framework. The framework constructs and maintains a Knowledge Graph (KG) to systematically model game elements, task dependencies, and causal relationships, enabling knowledge accumulation and reuse across versions. Building on this foundation, the framework utilizes LLMs to parse natural language update logs, identify the scope of impact through multi-hop reasoning on the KG, enabling the generation of update-tailored test cases. Experiments in two representative game environments, Overcooked and Minecraft, demonstrate that KLPEG can more accurately locate functionalities affected by updates and complete tests in fewer steps, significantly improving both playtesting effectiveness and efficiency.

cs.AI↗

arXiv2024

On the incompressible limit of Keller-Segel system with volume-filling effects

We consider the Keller-Segel system with a volume-filling effect and study its incompressible limit. Due to the presence of logistic-type sensitivity, $K=1$ is the critical threshold. When $K>1$, as the diffusion exponent tends to infinity, by supposing the weak limit of $u^2_m$, we prove that the limiting system becomes a Hele-Shaw type free boundary problem. For $K\le 1$, we justify that the stiff pressure effect ($ΔP_\infty$) vanishes, resulting in the limiting system being a hyperbolic Keller-Segel system. Compared to previous studies, the new challenge arises from the stronger nonlinearity induced by the logistic chemotactic sensitivity. To address this, our first novel finding is the proof of strong convergence of the density on the support of the limiting pressure, thus confirming the validity of the \emph{complementarity relation} for all $K>0$. Furthermore, specifically for the case $K\le1$, by introducing the \emph{kinetic formulation}, we verify the strong limit of the density required to reach the incompressible limit.

math.AP↗

arXiv2024

BiPC: Bidirectional Probability Calibration for Unsupervised Domain Adaption

Unsupervised Domain Adaptation (UDA) leverages a labeled source domain to solve tasks in an unlabeled target domain. While Transformer-based methods have shown promise in UDA, their application is limited to plain Transformers, excluding Convolutional Neural Networks (CNNs) and hierarchical Transformers. To address this issues, we propose Bidirectional Probability Calibration (BiPC) from a probability space perspective. We demonstrate that the probability outputs from a pre-trained head, after extensive pre-training, are robust against domain gaps and can adjust the probability distribution of the task head. Moreover, the task head can enhance the pre-trained head during adaptation training, improving model performance through bidirectional complementation. Technically, we introduce Calibrated Probability Alignment (CPA) to adjust the pre-trained head's probabilities, such as those from an ImageNet-1k pre-trained classifier. Additionally, we design a Calibrated Gini Impurity (CGI) loss to refine the task head, with calibrated coefficients learned from the pre-trained classifier. BiPC is a simple yet effective method applicable to various networks, including CNNs and Transformers. Experimental results demonstrate its remarkable performance across multiple UDA tasks. Our code will be available at: https://github.com/Wenlve-Zhou/BiPC.

cs.CV↗

arXiv2024

Language Evolution for Evading Social Media Regulation via LLM-based Multi-agent Simulation

Social media platforms such as Twitter, Reddit, and Sina Weibo play a crucial role in global communication but often encounter strict regulations in geopolitically sensitive regions. This situation has prompted users to ingeniously modify their way of communicating, frequently resorting to coded language in these regulated social media environments. This shift in communication is not merely a strategy to counteract regulation, but a vivid manifestation of language evolution, demonstrating how language naturally evolves under societal and technological pressures. Studying the evolution of language in regulated social media contexts is of significant importance for ensuring freedom of speech, optimizing content moderation, and advancing linguistic research. This paper proposes a multi-agent simulation framework using Large Language Models (LLMs) to explore the evolution of user language in regulated social media environments. The framework employs LLM-driven agents: supervisory agent who enforce dialogue supervision and participant agents who evolve their language strategies while engaging in conversation, simulating the evolution of communication styles under strict regulations aimed at evading social media regulation. The study evaluates the framework's effectiveness through a range of scenarios from abstract scenarios to real-world situations. Key findings indicate that LLMs are capable of simulating nuanced language dynamics and interactions in constrained settings, showing improvement in both evading supervision and information accuracy as evolution progresses. Furthermore, it was found that LLM agents adopt different strategies for different scenarios.

cs.SI↗