arXiv subjects

Chunyi Wang

Publications and source records attributed to Chunyi Wang.

5 recordsLinked to original sources

arXiv2026

ARQ: Agentic CodeQL Query Refinement for C/C++ Vulnerability Detection

Static analyzers have been widely adopted for vulnerability detection in C/C++ programs. Query-based static analyzers (e.g., CodeQL) encode vulnerable code patterns in detection queries and match them against source code. However, existing queries still suffer from false positives (FPs, incorrectly flagging benign code as vulnerable) and false negatives (FNs, missing real vulnerabilities). We present ARQ, an agentic framework that automatically refines C/C++ CodeQL queries using execution-grounded evidence from synthesized C/C++ programs. Our key insight is that a synthesized program exposes a query's weakness whenever its execution disagrees with the query's verdict. If the program is genuinely vulnerable but the query stays silent, the query has an FN weakness; if the program is safe but the query fires anyway, it has an FP weakness. ARQ then runs an LLM-based refinement loop that repairs the query using these disagreements as ground truth. Unlike previous query refining methods, ARQ requires no labeled datasets, no commit history, and no vulnerability-specific templates. We demonstrate the effectiveness of ARQ by refining 12 official CodeQL queries using three commercial LLMs (GPT-5.4, Claude-Sonnet-4.6, and Gemini-3.5-flash). We compare both ARQ-refined and original CodeQL queries on the Juliet v1.3 and FormAI v2 datasets and show that ARQ-refined queries detect substantially more true positives, by up to 119.8\%, with a Precision of at least 98.0\% throughout. ARQ successfully fixed three unresolved GitHub issues raised in the official CodeQL query repository that had remained open for as long as \textit{27 months}. The refined queries also exposed two previously undiscovered bugs in the real-world libraries libpng and zlib.

cs.CR↗

arXiv2022

Thin Spectra and Singular Continuous Spectral Measures for Limit-Periodic Jacobi Matrices

This paper investigates the spectral properties of Jacobi matrices with limit-periodic coefficients. We show that for a residual set of such matrices, the spectrum is a Cantor set of zero Lebesgue measure, and the spectral measures are purely singular continuous. For a dense set of limit-periodic Jacobi matrices we can strengthen the result and show that the spectrum is a Cantor set of zero lower box counting dimension, and hence in particular of zero Hausdorff dimension, while still retaining the singular continuity of the spectral type. We also show how results of this nature can be established by fixing the off-diagonal coefficients and varying only the diagonal coefficients, and, in a more restricted version, by fixing the diagonal coefficients to be zero and varying only the off-diagonal coefficients. We apply these results to produce examples of weighted Laplacians on the multidimensional integer lattice having purely singular continuous spectral type and zero-dimensional spectrum.

math.SP↗

arXiv2020

Speech Emotion Recognition Based on Multi-feature and Multi-lingual Fusion

A speech emotion recognition algorithm based on multi-feature and Multi-lingual fusion is proposed in order to resolve low recognition accuracy caused by lack of large speech dataset and low robustness of acoustic features in the recognition of speech emotion. First, handcrafted and deep automatic features are extracted from existing data in Chinese and English speech emotions. Then, the various features are fused respectively. Finally, the fused features of different languages are fused again and trained in a classification model. Distinguishing the fused features with the unfused ones, the results manifest that the fused features significantly enhance the accuracy of speech emotion recognition algorithm. The proposed solution is evaluated on the two Chinese corpus and two English corpus, and is shown to provide more accurate predictions compared to original solution. As a result of this study, the multi-feature and Multi-lingual fusion algorithm can significantly improve the speech emotion recognition accuracy when the dataset is small.

cs.CL↗

arXiv2013

MCMC methods for Gaussian process models using fast approximations for the likelihood

Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $π$, for covariance function parameters requires computation of the covariance matrix, C, a $pn^2$ operation, where p is the number of covariates and n is the number of training cases, and then inversion of C, an $n^3$ operation. We introduce MCMC methods based on the "temporary mapping and caching" framework, using a fast approximation, $π^*$, as the distribution needed to construct the temporary space. We propose two implementations under this scheme: "mapping to a discretizing chain", and "mapping with tempered transitions", both of which are exactly correct MCMC methods for sampling $π$, even though their transitions are constructed using an approximation. These methods are equivalent when their tuning parameters are set at the simplest values, but differ in general. We compare how well these methods work when using several approximations, finding on synthetic datasets that a $π^*$ based on the "Subset of Data" (SOD) method is almost always more efficient than standard MCMC using only $π$. On some datasets, a more sophisticated $π^*$ based on the "Nyström-Cholesky" method works better than SOD.

stat.CO↗

arXiv2012

Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals

Gaussian Process (GP) regression models typically assume that residuals are Gaussian and have the same variance for all observations. However, applications with input-dependent noise (heteroscedastic residuals) frequently arise in practice, as do applications in which the residuals do not have a Gaussian distribution. In this paper, we propose a GP Regression model with a latent variable that serves as an additional unobserved covariate for the regression. This model (which we call GPLC) allows for heteroscedasticity since it allows the function to have a changing partial derivative with respect to this unobserved covariate. With a suitable covariance function, our GPLC model can handle (a) Gaussian residuals with input-dependent variance, or (b) non-Gaussian residuals with input-dependent variance, or (c) Gaussian residuals with constant variance. We compare our model, using synthetic datasets, with a model proposed by Goldberg, Williams and Bishop (1998), which we refer to as GPLV, which only deals with case (a), as well as a standard GP model which can handle only case (c). Markov Chain Monte Carlo methods are developed for both modelsl. Experiments show that when the data is heteroscedastic, both GPLC and GPLV give better results (smaller mean squared error and negative log-probability density) than standard GP regression. In addition, when the residual are Gaussian, our GPLC model is generally nearly as good as GPLV, while when the residuals are non-Gaussian, our GPLC model is better than GPLV.

stat.ML↗