arXiv subjects

Yuqing Wang

Publications and source records attributed to Yuqing Wang.

3 recordsLinked to original sources

arXiv2026

A Theoretical Analysis of Generalization Dynamics in Neural Networks under Gradient Descent with Weight Decay

Understanding generalization remains a central challenge in machine learning because it requires jointly considering data, architecture, and training dynamics. In this paper, we develop a theoretical framework that characterizes how these factors jointly shape generalization performance throughout training. More precisely, we study a broad class of neural networks trained under the $\ell^2$ loss by gradient descent (GD) with weight decay, and prove the convergence of GD to a neighbourhood of the global minimizers of the empirical loss. By partitioning the space based on the input data, we then decompose the population error into data error, optimization error, and prediction variation error, and bound them separately. In particular, for the prediction variation error, which measures the oscillations of the learned function, we propose (local) approximate homogeneity and derive explicit cellwise and layerwise bounds for its evolution along the training trajectory. These bounds yield two important implications: a necessary condition of improved generalization explains differences in layerwise generalization behavior; a sufficient condition describes delayed generalization and provides a theoretical characterization of grokking.

cs.LG↗

arXiv2026

SARTM: Segment Any RGB Thermal Model with Language aided Distillation

The recent Segment Anything Model (SAM) demonstrates strong instance segmentation performance across various downstream tasks. However, SAM is trained solely on RGB data, limiting its direct applicability to RGB-thermal (RGB-T) semantic segmentation. Given that RGB-T provides a robust solution for scene understanding in adverse weather and lighting conditions, such as low light and overexposure, we propose a novel framework, SARTM, which customizes the powerful SAM for RGB-T semantic segmentation. Our key idea is to unleash the potential of SAM while introduce semantic understanding modules for RGB-T data pairs. Specifically, our framework first involves fine-tuning the original SAM by adding extra LoRA layers, aiming at preserving SAM's strong generalization and segmentation capabilities for downstream tasks. Secondly, we introduce language information as guidance for training our SARTM. To address cross-modal inconsistencies, we introduce a Cross-Modal Knowledge Distillation(CMKD) module that effectively achieves modality adaptation while maintaining its generalization capabilities. This semantic module enables the minimization of modality gaps and alleviates semantic ambiguity, facilitating the combination of any modality under any visual conditions. Furthermore, we enhance the segmentation performance by adjusting the segmentation head of SAM and incorporating an auxiliary semantic segmentation head, which integrates multi-scale features for effective fusion. Extensive experiments are conducted across three multi-modal RGBT semantic segmentation benchmarks: MFNET, PST900, and FMB. Both quantitative and qualitative results consistently demonstrate that the proposed SARTM significantly outperforms state-of-the-art approaches across a variety of conditions. Code and pre-trained weights can be found at https://github.com/wahaha-debug/SARTM.

cs.CV↗

arXiv2026

Large Language Models for Fuzz Testing in Microservices: A Systematic Literature Review

Microservice systems (MSS) increasingly rely on heterogeneous APIs whose combinatorial input space and stateful dependencies challenge traditional fuzz testing. Meanwhile, Large Language Models (LLMs) have recently been introduced to enhance fuzzing with semantic reasoning over specifications, inputs, and runtime feedback. This paper presents a systematic literature review (SLR) of LLM-assisted fuzz testing for microservices to synthesise how LLMs are applied, evaluated, and what challenges remain. Following established SLR guidelines, we analyze 20 primary studies published between 2024 and 2026. Results show LLMs are mainly used as semantic input generators in black-box fuzzing, with a growing shift towards agent-based and retrieval-augmented architectures, improving valid input generation and modestly increasing coverage and vulnerability detection. However, evaluation remains heterogeneous, with limited benchmark standardization, scarce cost reporting, and a bias toward single-service experiments, highlighting a gap with real-world multi-service systems. This review provides a taxonomy of LLM roles and integrations, a consolidated view of evaluation practices, and a mapping of open challenges to research directions, supporting the design and deployment of LLM-driven fuzzing in microservices.

cs.SE↗