arXiv subjects

Charles Zhang

Publications and source records attributed to Charles Zhang.

At least 19 recordsLinked to original sources

arXiv2026

ORCA: Observability-Grounded Program Repair for Microservice Incidents

Microservice failures are often diagnosed from operational telemetry. However, automated program repair systems usually start from issue reports, localized code context, or failing tests. This mismatch leaves a gap between telemetry-based diagnosis and patch generation. We present ORCA, an observability-grounded APR pipeline for microservice incidents. ORCA first distills the differences in paired failure and reference telemetry into a fault signature, then uses the signature to identify candidate code and deployment-configuration locations. Repair graph agents and an Exploration agent generate unified-diff patch candidates from these locations. ORCA evaluates generated patches with a Telemetry-Grounded Patch Verifier that separates patch validity, syntactic and semantic correctness, test-oracle integrity, and telemetry replay. On a 575-case benchmark, ORCA outperforms all evaluated baselines in terms of cost-effectiveness. Results show that operational telemetry can be transformed from diagnostic evidence into actionable repair context: paired telemetry supports repair-oriented localization, while repair graph agents convert localized code and configuration evidence into constrained patch-generation context for the LLM. Telemetry-grounded verification then exposes repair outcomes that issue- or test-only evaluation would miss.

cs.SE↗

arXiv2026

ConCovUp: Effective Agent-Based Test Driver Generation for Concurrency Testing

Concurrency testing is essential to improve the reliability and security of multi-threaded programs. Dynamic analysis tools, such as TSan, depend on high-quality test drivers that reach critical shared-memory interactions at runtime. However, current testing practices predominantly focus on sequential logic, leaving a gap in automated concurrent test generation. Recently, large language models (LLMs) have shown promise in generating sequential tests, but they struggle to produce effective concurrent tests without a deep understanding of concurrency semantics. This paper presents ConCovUp, a multi-agent framework that combines LLMs with program analysis. ConCovUp grounds test generation in static analysis to extract shared memory accesses and their calling contexts. To trigger hard-to-reach accesses, it introduces an LLM-driven backward tracing approach, leveraging the model's semantic reasoning to deduce concrete inputs that satisfy complex path constraints, and iteratively refines the generated tests via dynamic execution feedback. Our evaluation on nine real-world C/C++ libraries shows that ConCovUp improves average Shared Memory Access Pair Coverage (SMAP Coverage) from 36.6% to 68.1% over the general Claude Code agent baseline.

cs.SE↗

arXiv2026

DAInfer+: Neurosymbolic Inference of API Specifications from Documentation via Embedding Models

Modern software systems heavily rely on various libraries, which require understanding the API semantics in static analysis. However, summarizing API semantics remains challenging due to complex implementations or unavailable library code. This paper presents DAInfer+, a novel approach for inferring API specifications from library documentation. We employ Natural Language Processing (NLP) to interpret informal semantic information provided by the documentation, which enables us to reduce the specification inference to an optimization problem. Specifically, we investigate the effectiveness of sentence embedding models and Large Language Models (LLMs) in deriving memory operation abstractions from API descriptions. These abstractions are used to retrieve data-flow and aliasing relations to generate comprehensive API specifications. To solve the optimization problem efficiently, we propose neurosymbolic optimization, yielding precise data-flow and aliasing specifications. Our evaluation of popular Java libraries shows that zero-shot sentence embedding models outperform few-shot prompted LLMs in robustness, capturing fine-grained semantic nuances more effectively. While our initial attempts using two-stage LLM prompting yielded promising results, we found that the embedding-based approach proved superior. Specifically, these models achieve over 82% recall and 85% precision for data-flow inference and 88% recall and 79% precision for alias relations, all within seconds. These results demonstrate the practical value of DAInfer+ in library-aware static analysis.

cs.SE↗

arXiv2025

Experimental Reconstruction of Source 4D Phase Space Without Prior Knowledge of Transfer Matrix

We experimentally demonstrate a method for reconstructing the transverse 4D phase space of an electron beam at the time of emission from downstream diagnostics of the 4D phase space. This method does not rely on detailed knowledge of the beamline transport, besides assuming that linearity and symplecticity are satisfied. We apply this method to measure the transverse position and momentum phase space of electrons emitted from a spatially-structured alkali-antimonide cathode. This method can uncover local correlations between emission location and momentum spread. We formulate this method analytically and investigate resolution limits.

physics.acc-ph↗

arXiv2025

Atomic-scale mapping of interfacial phonon modes in epitaxial YBa2Cu3O7-{\delta} / (La,Sr)(Al,Ta)O3 thin films: The role of surface phonons

We investigate the behavior of phonons at the epitaxial interface between YBa2Cu3O7-{\delta} thin film and (La,Sr)(Al,Ta)O3 substrate using vibrational electron energy loss spectroscopy. Interfacial phonon modes with different degrees of scattering localization were identified. We find evidence that surface contributions from the surrounding environment can impose additional scattering modulation into local EELS measurements at the interface. A method to remove those contributions is then used to isolate the phonon information at the interface. This work unveils interfacial phonon modes in a high-Tc cuprate superconductor, that are not accessible with traditional phonon spectroscopy techniques, and provides a method for probing interfacial phonons in complex oxide heterostructures.

cond-mat.supr-con↗

arXiv2025

Boosting Path-Sensitive Value Flow Analysis via Removal of Redundant Summaries

Value flow analysis that tracks the flow of values via data dependence is a widely used technique for detecting a broad spectrum of software bugs. However, the scalability issue often deteriorates when high precision (i.e., path-sensitivity) is required, as the instantiation of function summaries becomes excessively time- and memory-intensive. The primary culprit, as we observe, is the existence of redundant computations resulting from blindly computing summaries for a function, irrespective of whether they are related to bugs being checked. To address this problem, we present the first approach that can effectively identify and eliminate redundant summaries, thereby reducing the size of collected summaries from callee functions without compromising soundness or efficiency. Our evaluation on large programs demonstrates that our identification algorithm can significantly reduce the time and memory overhead of the state-of-the-art value flow analysis by 45\% and 27\%, respectively. Furthermore, the identification algorithm demonstrates remarkable efficiency by identifying nearly 80\% of redundant summaries while incurring a minimal additional overhead. In the largest \textit{mysqld} project, the identification algorithm reduces the time by 8107 seconds (2.25 hours) with a mere 17.31 seconds of additional overhead, leading to a ratio of time savings to paid overhead (i.e., performance gain) of 468.48 $\times$. In total, our method attains an average performance gain of 632.1 $\times$.

cs.SE↗

arXiv2025

A Survey of Operating System Kernel Fuzzing

The Operating System (OS) kernel is foundational in modern computing, especially with the proliferation of diverse computing devices. However, its development also comes with vulnerabilities that can lead to severe security breaches. Kernel fuzzing, a technique used to uncover these vulnerabilities, poses distinct challenges when compared to user-space fuzzing. These include the complexity of configuring the testing environment and addressing the statefulness inherent to both the kernel and the fuzzing process. Despite the significant interest from the community, a comprehensive understanding of kernel fuzzing remains lacking, hindering further progress in the field. In this paper, we present the first systematic study focused specifically on OS kernel fuzzing. We begin by outlining the unique challenges of kernel fuzzing, which distinguish it from those in user space. Following this, we summarize the progress of 107 academic studies from top-tier venues between 2017 and 2025. To structure this analysis, we introduce a stage-based fuzzing model and a novel fuzzing taxonomy that highlights nine core functionalities unique to kernel fuzzing. Each of these functionalities is examined in conjunction with the methodological approaches employed to address them. Finally, we identify remaining gaps in addressing challenges and outline promising directions to guide forthcoming research in kernel security.

cs.CR↗

arXiv2025

From In Silico to In Vitro: A Comprehensive Guide to Validating Bioinformatics Findings

The integration of bioinformatics predictions and experimental validation plays a pivotal role in advancing biological research, from understanding molecular mechanisms to developing therapeutic strategies. Bioinformatics tools and methods offer powerful means for predicting gene functions, protein interactions, and regulatory networks, but these predictions must be validated through experimental approaches to ensure their biological relevance. This review explores the various methods and technologies used for experimental validation, including gene expression analysis, protein-protein interaction verification, and pathway validation. We also discuss the challenges involved in translating computational predictions to experimental settings and highlight the importance of collaboration between bioinformatics and experimental research. Finally, emerging technologies, such as CRISPR gene editing, next-generation sequencing, and artificial intelligence, are shaping the future of bioinformatics validation and driving more accurate and efficient biological discoveries.

q-bio.GN↗

arXiv2025

From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financial technology, where decision-making processes must account for uncertainty. This review explores the evolution of uncertainty quantification techniques in AI, distinguishing between aleatoric and epistemic uncertainties, and discusses the mathematical foundations and methods used to quantify these uncertainties. We provide an overview of advanced techniques, including probabilistic methods, ensemble learning, sampling-based approaches, and generative models, while also highlighting hybrid approaches that integrate domain-specific knowledge. Furthermore, we examine the diverse applications of UQ across various fields, emphasizing its impact on decision-making, predictive accuracy, and system robustness. The review also addresses key challenges such as scalability, efficiency, and integration with explainable AI, and outlines future directions for research in this rapidly developing area. Through this comprehensive survey, we aim to provide a deeper understanding of UQ's role in enhancing the reliability, safety, and trustworthiness of AI systems.

cs.AI↗

arXiv2024

From Bench to Bedside: A Review of Clinical Trials in Drug Discovery and Development

Clinical trials are an indispensable part of the drug development process, bridging the gap between basic research and clinical application. During the development of new drugs, clinical trials are used not only to evaluate the safety and efficacy of the drug but also to explore its dosage, treatment regimens, and potential side effects. This review discusses the various stages of clinical trials, including Phase I (safety assessment), Phase II (preliminary efficacy evaluation), Phase III (large-scale validation), and Phase IV (post-marketing surveillance), highlighting the characteristics of each phase and their interrelationships. Additionally, the paper addresses the major challenges encountered in clinical trials, such as ethical issues, subject recruitment difficulties, diversity and representativeness concerns, and proposes strategies for overcoming these challenges. With the advancement of technology, innovative technologies such as artificial intelligence, big data, and digitalization are gradually transforming clinical trial design and implementation, improving trial efficiency and data quality. The article also looks forward to the future of clinical trials, particularly the impact of emerging therapies such as gene therapy and immunotherapy on trial design, as well as the importance of regulatory reforms and global collaboration. In conclusion, the core role of clinical trials in drug development will continue to drive the progress of innovative drug development and clinical treatment.

cs.CY↗

arXiv2024

Goldbach theorems for group semidomains

A semidomain is a subsemiring of an integral domain. We call a semidomain $S$ additively reduced if $0$ is the only invertible element of the monoid $(S, +)$, while we say that $S$ is additively Furstenberg if every non-invertible element of $(S,+)$ can be expressed as the sum of an atom and an element of $S$. In this paper, we study a variant of the Goldbach conjecture within the framework of group semidomains $S[G]$ and group series semidomains $S[\![G]\!]$, where $S$ is both an additively reduced and additively Furstenberg semidomain and $G$ is a torsion-free abelian group. In particular, we show that every non-constant polynomial expression in $S[G]$ can be written as the sum of at most two irreducibles if and only if the condition $\mathscr{A}_+(S) = S^\times$ holds.

math.AC↗

arXiv2024

From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models

Word embeddings and language models have transformed natural language processing (NLP) by facilitating the representation of linguistic elements in continuous vector spaces. This review visits foundational concepts such as the distributional hypothesis and contextual similarity, tracing the evolution from sparse representations like one-hot encoding to dense embeddings including Word2Vec, GloVe, and fastText. We examine both static and contextualized embeddings, underscoring advancements in models such as ELMo, BERT, and GPT and their adaptations for cross-lingual and personalized applications. The discussion extends to sentence and document embeddings, covering aggregation methods and generative topic models, along with the application of embeddings in multimodal domains, including vision, robotics, and cognitive science. Advanced topics such as model compression, interpretability, numerical encoding, and bias mitigation are analyzed, addressing both technical challenges and ethical implications. Additionally, we identify future research directions, emphasizing the need for scalable training techniques, enhanced interpretability, and robust grounding in non-textual modalities. By synthesizing current methodologies and emerging trends, this survey offers researchers and practitioners an in-depth resource to push the boundaries of embedding-based language models.

cs.CL↗

arXiv2024

From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing

Remote sensing has evolved from simple image acquisition to complex systems capable of integrating and processing visual and textual data. This review examines the development and application of multi-modal language models (MLLMs) in remote sensing, focusing on their ability to interpret and describe satellite imagery using natural language. We cover the technical underpinnings of MLLMs, including dual-encoder architectures, Transformer models, self-supervised and contrastive learning, and cross-modal integration. The unique challenges of remote sensing data--varying spatial resolutions, spectral richness, and temporal changes--are analyzed for their impact on MLLM performance. Key applications such as scene description, object detection, change detection, text-to-image retrieval, image-to-text generation, and visual question answering are discussed to demonstrate their relevance in environmental monitoring, urban planning, and disaster response. We review significant datasets and resources supporting the training and evaluation of these models. Challenges related to computational demands, scalability, data quality, and domain adaptation are highlighted. We conclude by proposing future research directions and technological advancements to further enhance MLLM utility in remote sensing.

cs.CV↗

arXiv2024

Unveiling the Power of Intermediate Representations for Static Analysis: A Survey

Static analysis techniques enhance the security, performance, and reliability of programs by analyzing and portraiting program behaviors without the need for actual execution. In essence, static analysis takes the Intermediate Representation (IR) of a target program as input to retrieve essential program information and understand the program. However, there is a lack of systematic analysis on the benefit of IR for static analysis, besides serving as an information provider. In general, a modern static analysis framework should possess the ability to conduct diverse analyses on different languages, producing reliable results with minimal time consumption, and offering extensive customization options. In this survey, we systematically characterize these goals and review the potential solutions from the perspective of IR. It can serve as a manual for learners and practitioners in the static analysis field to better understand IR design. Meanwhile, numerous research opportunities are revealed for researchers.

cs.PL↗

arXiv2024

Equational Bit-Vector Solving via Strong Gröbner Bases

Bit-vectors, which are integers in a finite number of bits, are ubiquitous in software and hardware systems. In this work, we consider the satisfiability modulo theories (SMT) of bit-vectors. Unlike normal integers, the arithmetics of bit-vectors are modular upon integer overflow. Therefore, the SMT solving of bit-vectors needs to resolve the underlying modular arithmetics. In the literature, two prominent approaches for SMT solving are bit-blasting (that transforms the SMT problem into boolean satisfiability) and integer solving (that transforms the SMT problem into integer properties). Both approaches ignore the algebraic properties of the modular arithmetics and hence could not utilize these properties to improve the efficiency of SMT solving. In this work, we consider the equational theory of bit-vectors and capture the algebraic properties behind them via strong Gröbner bases. First, we apply strong Gröbner bases to the quantifier-free equational theory of bit-vectors and propose a novel algorithmic improvement in the key computation of multiplicative inverse modulo a power of two. Second, we resolve the important case of invariant generation in quantified equational bit-vector properties via strong Gröbner bases and linear congruence solving. Experimental results over an extensive range of benchmarks show that our approach outperforms existing methods in both time efficiency and memory consumption.

cs.LO↗

arXiv2023

Synthesizing Conjunctive Queries for Code Search

This paper presents Squid, a new conjunctive query synthesis algorithm for searching code with target patterns. Given positive and negative examples along with a natural language description, Squid analyzes the relations derived from the examples by a Datalog-based program analyzer and synthesizes a conjunctive query expressing the search intent. The synthesized query can be further used to search for desired grammatical constructs in the editor. To achieve high efficiency, we prune the huge search space by removing unnecessary relations and enumerating query candidates via refinement. We also introduce two quantitative metrics for query prioritization to select the queries from multiple candidates, yielding desired queries for code search. We have evaluated Squid on over thirty code search tasks. It is shown that Squid successfully synthesizes the conjunctive queries for all the tasks, taking only 2.56 seconds on average.

cs.PL↗

arXiv2023

Verifying Data Constraint Equivalence in FinTech Systems

Data constraints are widely used in FinTech systems for monitoring data consistency and diagnosing anomalous data manipulations. However, many equivalent data constraints are created redundantly during the development cycle, slowing down the FinTech systems and causing unnecessary alerts. We present EqDAC, an efficient decision procedure to determine the data constraint equivalence. We first propose the symbolic representation for semantic encoding and then introduce two light-weighted analyses to refute and prove the equivalence, respectively, which are proved to achieve in polynomial time. We evaluate EqDAC upon 30,801 data constraints in a FinTech system. It is shown that EqDAC detects 11,538 equivalent data constraints in three hours. It also supports efficient equivalence searching with an average time cost of 1.22 seconds, enabling the system to check new data constraints upon submission.

cs.PL↗

arXiv2022

Discovery of an electronic crystal in a cuprate Mott insulator

Copper oxide high temperature superconductors universally exhibit multiple forms of electronically ordered phases that break the native translational symmetry of the CuO2 planes. The interplay between these orders and the superconducting ground state, as well as how they arise through doping a Mott insulator, is essential to decode the mechanisms of high-temperature superconductivity. Over the years, various forms of electronic liquid crystal phases including charge/spin stripes and incommensurate charge-density-waves (CDWs) were found to emerge out of a correlated metallic ground state in underdoped cuprates. Early theoretical studies also predicted the emergence of a Coulomb-frustrated 'charge crystal' phase in the very lightly-doped, insulating limit of the CuO2 planes. Here, we use resonant X-ray scattering, electron transport, and muon spin rotation measurements to fully resolve the electronic and magnetic ground state and search for signatures of charge order in very lightly hole-doped cuprates from the RBa2Cu3O7-d family (RBCO; R: Y or rare earth). X-ray scattering data from RBCO films reveal a breaking of translational symmetry more pervasive than was previously known, extending down to the Mott limit. The ordering vector of this charge crystal state is linearly connected to the charge-density-waves of underdoped RBCO, suggesting that the former phase is a precursor to the latter as hole doping is increased. Most importantly, the coexistence of charge and spin order in RBCO suggests that this electronic symmetry-breaking state is common to the CuO2 planes in the very lightly-doped regime. These findings bridge the gap between the Mott insulating state and the underdoped metallic state and underscore the prominent role of Coulomb-frustrated electronic phase separation among all cuprates.

cond-mat.str-el↗