Kaibo Liu, Zhenpeng Chen, Yiyang Liu, Jie M. Zhang, Mark Harman, Yudong Han, Yun Ma, Yihong Dong, Ge Li, Gang Huang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Approximate nearest neighbor search (ANNS) has become vital to modern AI infrastructure, particularly in retrieval-augmented generation (RAG) applications. Numerous in-browser ANNS engines have emerged to seamlessly integrate with popular LLM-based web applications, while addressing privacy protection and challenges of heterogeneous device deployments. However, web browsers present unique challenges for ANNS, including computational limitations, external storage access issues, and memory utilization constraints, which state-of-the-art (SOTA) solutions fail to address comprehensively.

WebGL has long been the prevalent API for GPU-accelerated graphics in web browsers, boosting 2D/3D graphical web applications. Despite widespread adoption, WebGL's programming model hinders its rendering performance on modern GPU hardware. To this end, WebGPU has been proposed as the next-generation API of GPU-accelerated processing in web browsers, exhibiting higher performance than WebGL. However, considering the complex logic of WebGL applications and the still-evolving WebGPU specification, statically migrating existing WebGL applications to WebGPU from source code is labor-intensive. To address this issue, we propose GL2GPU, an intermediate layer that dynamically translates WebGL to WebGPU at JavaScript runtime to improve rendering performance. GL2GPU addresses the inconsistencies between the WebGL and WebGPU programming models by emulating WebGL rendering states and leverages performance optimization mechanisms introduced by WebGPU to reduce the overhead of dynamic translation. Evaluation of three representative WebGL benchmarks shows that GL2GPU significantly enhances end-to-end rendering performance while maintaining visual consistency, achieving an average frame time reduction of 45.05% across different devices and operating systems.

Federated learning (FL) is an emerging promising paradigm of privacy-preserving machine learning (ML). An important type of FL is cross-silo FL, which enables a moderate number of organizations to cooperatively train a shared model by keeping confidential data locally and aggregating gradients on a central parameter server. However, the central server may be vulnerable to malicious attacks or software failures in practice. To address this issue, in this paper, we propose$\mathtt{DegaFL} $, a novel decentralized gradient aggregation approach for cross-silo FL.$\mathtt{DegaFL} $eliminates the central server by aggregating gradients on each participant, and maintains and synchronizes gradients of only the current training round. Besides, we propose$\mathtt{AdaAgg} $to adaptively aggregate correct gradients from honest nodes and use HotStuff to ensure the consistency of the training round number and gradients among all nodes. Experimental results show that$\mathtt{DegaFL} $defends against common threat models with minimal accuracy loss, and achieves up to$50\times$reduction in storage overhead and up to$13\times$reduction in network overhead, compared to state-of-the-art decentralized FL approaches.

Federated learning (FL) is a privacy-preserving deep learning paradigm. An important type of FL is cross-silo FL, which enables a moderate number of organizations to cooperatively train a shared model while keeping private data locally and aggregating parameters on a central parameter server. However, the central server may be vulnerable to malicious attacks or software failures. To address this problem, Swarm Learning (SL) has emerged to perform FL in a decentralized manner by introducing a blockchain to securely onboard members and dynamically elect the leader for parameter aggregation. Despite tremendous attention to SL recently, few measurement studies provide comprehensive knowledge of best practices and precautions for deploying SL in real-world scenarios. To this end, we conduct the first empirical study of SL, to fill the knowledge gap between SL research and real-world deployment. We conduct various experiments on 3 public datasets for 4 research questions, present interesting findings, quantitatively analyze the reasons behind these findings, and provide developers and researchers with practical suggestions.

We call a program that passes existing tests but still contains bugs as a buggy plausible program. Bugs in such a program can bypass the testing environment and enter the production environment, causing unpredictable consequences. Therefore, discovering and fixing such bugs is a fundamental and critical problem. However, no existing bug dataset is purposed to collect this kind of bug, posing significant obstacles to relevant research. To address this gap, we introduce TrickyBugs, a bug dataset with 3,043 buggy plausible programs sourced from human-written submissions of 324 real-world competition coding tasks. We identified the buggy plausible programs from approximately 400,000 submissions, and all the bugs in TrickyBugs were not previously detected. We hope that TrickyBugs can effectively facilitate research in the fields of automated program repair, fault localization, test generation, and test adequacy.

Graphics rendering on web browsers serves as the foundation for numerous web applications. Compared with the widely employed WebGL, the next-generation web graphics API, WebGPU, demonstrates an enhanced capacity to adapt to modern GPU features, boasting more significant potential. However, our experiment shows that the performance of current graphics rendering frameworks based on WebGPU lags behind those built on WebGL. Such discrepancy primarily arises from an incomplete alignment with WebGPU's distinctive features. The individual rendering of each graphic leads to redundant communication between the CPU and GPU. To enhance the graphics performance on the web, we introduce the FusionRender to harness the power of WebGPU. To mitigate redundant communication, FusionRender assigns a unique signature to each object and employs these signatures for grouping, enabling the consolidation of graphics rendering whenever possible. In simulated experiments involving the rendering of multiple objects, FusionRender improves the rendering performance by 29.3%-122.1% compared with the existing optimal baseline. In real cases with more complex features, performance improvement ranges from 9.4% to 39.7%. Additionally, FusionRender exhibits robust performance enhancement across various devices and browsers.

Online Judge platforms play a pivotal role in education, competitive programming, recruitment, career training, and large language model training. They rely on predefined test suites to judge the correctness of submitted solutions. It is therefore important that the solution judgement is reliable and free from potentially misleading false positives (i.e., incorrect solutions that are judged as correct). In this paper, we conduct an empirical study of 939 coding problems with 541,552 solutions, all of which are judged to be correct according to the test suites used by the platform, finding that 43.4% of the problems include false positive solutions (3,440 bugs are revealed in total). We also find that test suites are, nevertheless, of high quality according to widely-studied test effectiveness measurements: 88.2% of false positives have perfect (100%) line coverage, 78.9% have perfect branch coverage, and 32.5% have a perfect mutation score. Our findings indicate that more work is required to weed out false positive solutions and to further improve test suite effectiveness. We have released the detected false positive solutions and the generated test inputs to facilitate future research.