Searcharxiv⌕ Search

arXiv subjects

Sinan Wang

Publications and source records attributed to Sinan Wang.

32 records · Page 2Linked to original sources
arXiv2025

Multi-agent Assisted Automatic Test Generation for Java JSON Libraries

JSON is a widely used format for data exchange between applications. In the Java ecosystem, JSON libraries serve as fundamental toolkits for processing JSON data, powering real-world applications such as web services, Android apps, or data management systems. However, without effective quality assurance methods such as automatic test generation (ATG), developers risk introducing subtle data inconsistency bugs, compatibility issues, and even security vulnerabilities. These flaws can affect billions of end users and potentially cause severe financial losses. Recently, large language models (LLMs) have shown strong potential in enhancing ATG. However, existing LLM-based methods like TitanFuzz and YanHui lack specialization in the JSON domain. For Java JSON libraries (JJLs), effective test cases should capture the constraints between formatted data and application programs, leaving critical aspects of quality assurance unaddressed. To fill this gap, we propose JsonATG, a multi-agent ATG system that generates diverse bug-triggering tests for JJLs. With historical bug information as seeds, JsonATG introduces a code summarization agent and a test validation agent into the generation pipeline to produce new and valid test cases. It applies agent-generated program mutation rules tailored specifically for the structural and semantic characteristics of JJLs, such as data streaming operations, serialization formats, and data-binding patterns. The generated tests are further refined through post-processing to ensure syntactic and semantic correctness. Our experiments show that JsonATG achieves higher coverage than two state-of-the-art LLM-based test generation methods on the widely used JSON library. With a \$25 budget, we reported 59 bugs (including non-crashing functional bugs) in fastjson, of which 47 were confirmed and 28 have already been fixed.

cs.SE↗
arXiv2025

Reinforced Visual Perception with Tools

Visual reasoning, a cornerstone of human intelligence, encompasses complex perceptual and logical processes essential for solving diverse visual problems. While advances in computer vision have produced powerful models for various perceptual tasks, leveraging these for general visual reasoning remains challenging. Prior work demonstrates that augmenting LLMs with vision models via supervised finetuning improves performance, but faces key limitations such as expensive data generation, reliance on careful data filtering, and poor generalization. To address these issues, we propose ReVPT to enhance multi-modal LLMs' abilities to reason about and use visual tools through reinforcement learning. We introduce a novel RL algorithm based on GRPO, designed to train models to reason with a suite of four visual tools. Through extensive experiments, we show that our method achieves state-of-the-art performance on several perception-heavy benchmarks, including SAT, CV-Bench, BLINK and MMStar, significantly outperforming the supervised and text-based RL finetuning baselines. Notably, Our ReVPT-3B and ReVPT-7B outperform the instruct models by 9.03% and 9.44% on CV-Bench. Finally, we bring to the community new insights on RL-based visual tool-usage through extensive ablations. Our code is available at https://github.com/ls-kelvin/REVPT.

cs.CV↗
arXiv2025

MultiRef: Controllable Image Generation with Multiple Visual References

Visual designers naturally draw inspiration from multiple visual references, combining diverse elements and aesthetic principles to create artwork. However, current image generative frameworks predominantly rely on single-source inputs -- either text prompts or individual reference images. In this paper, we focus on the task of controllable image generation using multiple visual references. We introduce MultiRef-bench, a rigorous evaluation framework comprising 990 synthetic and 1,000 real-world samples that require incorporating visual content from multiple reference images. The synthetic samples are synthetically generated through our data engine RefBlend, with 10 reference types and 33 reference combinations. Based on RefBlend, we further construct a dataset MultiRef containing 38k high-quality images to facilitate further research. Our experiments across three interleaved image-text models (i.e., OmniGen, ACE, and Show-o) and six agentic frameworks (e.g., ChatDiT and LLM + SD) reveal that even state-of-the-art systems struggle with multi-reference conditioning, with the best model OmniGen achieving only 66.6% in synthetic samples and 79.0% in real-world cases on average compared to the golden answer. These findings provide valuable directions for developing more flexible and human-like creative tools that can effectively integrate multiple sources of visual inspiration. The dataset is publicly available at: https://multiref.github.io/.

cs.CV↗
arXiv2025

Fluid Simulation on Vortex Particle Flow Maps

We propose the Vortex Particle Flow Map (VPFM) method to simulate incompressible flow with complex vortical evolution in the presence of dynamic solid boundaries. The core insight of our approach is that vorticity is an ideal quantity for evolution on particle flow maps, enabling significantly longer flow map distances compared to other fluid quantities like velocity or impulse. To achieve this goal, we developed a hybrid Eulerian-Lagrangian representation that evolves vorticity and flow map quantities on vortex particles, while reconstructing velocity on a background grid. The method integrates three key components: (1) a vorticity-based particle flow map framework, (2) an accurate Hessian evolution scheme on particles, and (3) a solid boundary treatment for no-through and no-slip conditions in VPFM. These components collectively allow a substantially longer flow map length (3-12 times longer) than the state-of-the-art, enhancing vorticity preservation over extended spatiotemporal domains. We validated the performance of VPFM through diverse simulations, demonstrating its effectiveness in capturing complex vortex dynamics and turbulence phenomena.

cs.GR↗
arXiv2024

Leveraging Large Vision Language Model For Better Automatic Web GUI Testing

With the rapid development of web technology, more and more software applications have become web-based in the past decades. To ensure software quality and user experience, various techniques have been proposed to automatically test web applications by interacting with their GUIs. To achieve high functional coverage, web GUI testing tools often need to generate high-quality text inputs and interact with the associated GUI elements (e.g., click submit buttons). However, developing a holistic approach that solves both subtasks is challenging because the web GUI context can be complicated and highly dynamic, which makes it hard to process programmatically. The recent development of large vision-language models (LVLM) provides new opportunities to handle these longstanding problems. This paper proposes VETL, the first LVLM-driven end-to-end web testing technique. With LVLM's scene understanding capabilities, VETL can generate valid and meaningful text inputs focusing on the local context, while avoiding the need to extract precise textual attributes. The selection of associated GUI elements is formulated as a visual question-answering problem, allowing LVLM to capture the logical connection between the input box and the relevant element based on visual instructions. Further, the GUI exploration is guided by a multi-armed bandit module employing a curiosity-oriented strategy. Experiments show that VETL effectively explores web state/action spaces and detects bugs. Compared with WebExplor, the state-of-the-art web testing technique, VETL can discover 25% more unique web actions on benchmark websites. Moreover, it can expose functional bugs in top-ranking commercial websites, which the website maintainers have confirmed. Our work makes the first attempt at leveraging LVLM in end-to-end GUI testing, demonstrating promising results in this research direction.

cs.SE↗
arXiv2024

An Eulerian Vortex Method on Flow Maps

We present an Eulerian vortex method based on the theory of flow maps to simulate the complex vortical motions of incompressible fluids. Central to our method is the novel incorporation of the flow-map transport equations for line elements, which, in combination with a bi-directional marching scheme for flow maps, enables the high-fidelity Eulerian advection of vorticity variables. The fundamental motivation is that, compared to impulse $\mathbf{m}$, which has been recently bridged with flow maps to encouraging results, vorticity $\boldsymbolω$ promises to be preferable for its numerical stability and physical interpretability. To realize the full potential of this novel formulation, we develop a new Poisson solving scheme for vorticity-to-velocity reconstruction that is both efficient and able to accurately handle the coupling near solid boundaries. We demonstrate the efficacy of our approach with a range of vortex simulation examples, including leapfrog vortices, vortex collisions, cavity flow, and the formation of complex vortical structures due to solid-fluid interactions.

cs.GR↗
arXiv2024

SENC: Handling Self-collision in Neural Cloth Simulation

We present SENC, a novel self-supervised neural cloth simulator that addresses the challenge of cloth self-collision. This problem has remained unresolved due to the gap in simulation setup between recent collision detection and response approaches and self-supervised neural simulators. The former requires collision-free initial setups, while the latter necessitates random cloth instantiation during training. To tackle this issue, we propose a novel loss based on Global Intersection Analysis (GIA). This loss extracts the volume surrounded by the cloth region that forms the penetration. By constructing an energy based on this volume, our self-supervised neural simulator can effectively address cloth self-collisions. Moreover, we develop a self-collision-aware graph neural network capable of learning to handle self-collisions, even for parts that are topologically distant from one another. Additionally, we introduce an effective external force scheme that enables the simulation to learn the cloth's behavior in response to random external forces. We validate the efficacy of SENC through extensive quantitative and qualitative experiments, demonstrating that it effectively reduces cloth self-collision while maintaining high-quality animation results.

cs.GR↗
arXiv2024

Eulerian-Lagrangian Fluid Simulation on Particle Flow Maps

We propose a novel Particle Flow Map (PFM) method to enable accurate long-range advection for incompressible fluid simulation. The foundation of our method is the observation that a particle trajectory generated in a forward simulation naturally embodies a perfect flow map. Centered on this concept, we have developed an Eulerian-Lagrangian framework comprising four essential components: Lagrangian particles for a natural and precise representation of bidirectional flow maps; a dual-scale map representation to accommodate the mapping of various flow quantities; a particle-to-grid interpolation scheme for accurate quantity transfer from particles to grid nodes; and a hybrid impulse-based solver to enforce incompressibility on the grid. The efficacy of PFM has been demonstrated through various simulation scenarios, highlighting the evolution of complex vortical structures and the details of turbulent flows. Notably, compared to NFM, PFM reduces computing time by up to 49 times and memory consumption by up to 41%, while enhancing vorticity preservation as evidenced in various tests like leapfrog, vortex tube, and turbulent flow.

cs.GR↗
arXiv2023

On the continuity of the solutions to the $L_{p}$ torsional Minkowski problem

In this paper, we derive the continuity of solutions to the $L_{p}$ torsional Minkowski problem for $p>1$. It is shown that the weak convergence of the $L_{p}$ torsional measure implies the convergence of the sequence of the corresponding convex bodies in the Hausdorff metric. Furthermore, continuity of the solution to the $L_{p}$ torsional Minkowski problem with regard to $p$ is also obtained.

math.MG↗
arXiv2023

MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation

Large-scale e-commercial platforms in the real-world usually contain various recommendation scenarios (domains) to meet demands of diverse customer groups. Multi-Domain Recommendation (MDR), which aims to jointly improve recommendations on all domains and easily scales to thousands of domains, has attracted increasing attention from practitioners and researchers. Existing MDR methods usually employ a shared structure and several specific components to respectively leverage reusable features and domain-specific information. However, data distribution differs across domains, making it challenging to develop a general model that can be applied to all circumstances. Additionally, during training, shared parameters often suffer from the domain conflict while specific parameters are inclined to overfitting on data sparsity domains. we first present a scalable MDR platform served in Taobao that enables to provide services for thousands of domains without specialists involved. To address the problems of MDR methods, we propose a novel model agnostic learning framework, namely MAMDR, for the multi-domain recommendation. Specifically, we first propose a Domain Negotiation (DN) strategy to alleviate the conflict between domains. Then, we develop a Domain Regularization (DR) to improve the generalizability of specific parameters by learning from other domains. We integrate these components into a unified framework and present MAMDR, which can be applied to any model structure to perform multi-domain recommendation. Finally, we present a large-scale implementation of MAMDR in the Taobao application and construct various public MDR benchmark datasets which can be used for following studies. Extensive experiments on both benchmark datasets and industry datasets demonstrate the effectiveness and generalizability of MAMDR.

cs.IR↗
arXiv2022

Recommender Transformers with Behavior Pathways

Sequential recommendation requires the recommender to capture the evolving behavior characteristics from logged user behavior data for accurate recommendations. However, user behavior sequences are viewed as a script with multiple ongoing threads intertwined. We find that only a small set of pivotal behaviors can be evolved into the user's future action. As a result, the future behavior of the user is hard to predict. We conclude this characteristic for sequential behaviors of each user as the Behavior Pathway. Different users have their unique behavior pathways. Among existing sequential models, transformers have shown great capacity in capturing global-dependent characteristics. However, these models mainly provide a dense distribution over all previous behaviors using the self-attention mechanism, making the final predictions overwhelmed by the trivial behaviors not adjusted to each user. In this paper, we build the Recommender Transformer (RETR) with a novel Pathway Attention mechanism. RETR can dynamically plan the behavior pathway specified for each user, and sparingly activate the network through this behavior pathway to effectively capture evolving patterns useful for recommendation. The key design is a learned binary route to prevent the behavior pathway from being overwhelmed by trivial behaviors. We empirically verify the effectiveness of RETR on seven real-world datasets and RETR yields state-of-the-art performance.

cs.IR↗
arXiv2022

Aper: Evolution-Aware Runtime Permission Misuse Detection for Android Apps

The Android platform introduces the runtime permission model in version 6.0. The new model greatly improves data privacy and user experience, but brings new challenges for app developers. First, it allows users to freely revoke granted permissions. Hence, developers cannot assume that the permissions granted to an app would keep being granted. Instead, they should make their apps carefully check the permission status before invoking dangerous APIs. Second, the permission specification keeps evolving, bringing new types of compatibility issues into the ecosystem. To understand the impact of the challenges, we conducted an empirical study on 13,352 popular Google Play apps. We found that 86.0% apps used dangerous APIs asynchronously after permission management and 61.2% apps used evolving dangerous APIs. If an app does not properly handle permission revocations or platform differences, unexpected runtime issues may happen and even cause app crashes. We call such Android Runtime Permission issues as ARP bugs. Unfortunately, existing runtime permission issue detection tools cannot effectively deal with the ARP bugs induced by asynchronous permission management and permission specification evolution. To fill the gap, we designed a static analyzer, Aper, that performs reaching definition and dominator analysis on Android apps to detect the two types of ARP bugs. To compare Aper with existing tools, we built a benchmark, ARPfix, from 60 real ARP bugs. Our experiment results show that Aper significantly outperforms two academic tools, ARPDroid and RevDroid, and an industrial tool, Lint, on ARPfix, with an average improvement of 46.3% on F1-score. In addition, Aper successfully found 34 ARP bugs in 214 opensource Android apps, most of which can result in abnormal app behaviors (such as app crashes) according to our manual validation.

cs.SE↗
arXiv2021

Runtime Permission Issues in Android Apps: Taxonomy, Practices, and Ways Forward

Android introduces a new permission model that allows apps to request permissions at runtime rather than at the installation time since 6.0 (Marshmallow, API level 23). While this runtime permission model provides users with greater flexibility in controlling an app's access to sensitive data and system features, it brings new challenges to app development. First, as users may grant or revoke permissions at any time while they are using an app, developers need to ensure that the app properly checks and requests required permissions before invoking any permission-protected APIs. Second, Android's permission mechanism keeps evolving and getting customized by device manufacturers. Developers are expected to comprehensively test their apps on different Android versions and device models to make sure permissions are properly requested in all situations. Unfortunately, these requirements are often impractical for developers. In practice, many Android apps suffer from various runtime permission issues (ARP issues). While existing studies have explored ARP issues, the understanding of such issues is still preliminary. To better characterize ARP issues, we performed an empirical study using 135 Stack Overflow posts that discuss ARP issues and 199 real ARP issues archived in popular open-source Android projects on GitHub. Via analyzing the data, we observed 11 types of ARP issues that commonly occur in Android apps. Furthermore, we conducted a field survey and in-depth interviews among practitioners, to gain insights from industrial practices and learn practitioners' requirements of tools that can help combat ARP issues. We hope that our findings can shed light on future research and provide useful guidance to practitioners.

cs.SE↗