arXiv subjects

Guoquan Wu

Publications and source records attributed to Guoquan Wu.

10 recordsLinked to original sources

arXiv2026

HitMem: Hierarchical Temporal 3D Memory with Multi-Modal Context-Aware Retrieval for Dynamic Environments

Executing long-term tasks in dynamic environments requires embodied agents to maintain robust and adaptive 3D scene representations. However, most existing 3D memory frameworks rely on static world assumptions. When objects are displaced by human activities or unobserved events, agents encounter memory-observation conflicts and often require costly geometric recomputations or inefficient global re-exploration. To address this, we propose HitMem, a hierarchical temporal 3D memory framework with a multi-modal context-aware retrieval mechanism. Through continuous perception, HitMem unifies semantic and spatial information into a lightweight topological graph that captures support relationships, while a temporal decay mechanism dynamically regulates memory activeness to mitigate the impact of stale representations. In addition, the multi-modal context-aware retrieval mechanism defaults to filtering candidates using integrated semantic, spatial, and temporal memory features, and activates a specialized two-stage retrieval process when object displacement is detected. This process combines spatial constraints inferred from external agent trajectories with semantic common sense grounded in class affinities, efficiently identifying high-probability candidate regions. Extensive evaluations on our constructed Dyna-THOR benchmark demonstrate that HitMem significantly improves object relocation accuracy, reduces exploration costs, and enhances task execution performance in dynamic environments.

cs.RO↗

arXiv2026

Prox: Training-Free FFN Activation Sparsity via Approximate Intermediate-Channel Salience in LLMs

Feed-forward networks (FFNs) dominate memory traffic and computation in large language model (LLM) inference, making them a primary target for activation sparsification. However, existing training-free methods suffer substantial model-quality degradation at high sparsity due to limitations in their channel-selection strategies. We observe that the SwiGLU intermediate state provides a highly effective channel-selection signal, but obtaining it requires costly dense computation. To address this, we present \emph{Prox}, a two-stage training-free framework for sparse SwiGLU FFNs. Prox hinges on the key insight: sparse execution requires only the channel mask induced by the intermediate state, which can be constructed from the magnitude ranking of its entries rather than their exact values. Specifically, Stage 1 uses input sparsity and quantized proxy weights to construct a shared mask; Stage 2 computes the selected channels exactly, enabling sparse execution of all three projections. Across ten LLMs from six model families, Prox outperforms training-free baselines at all sparsity levels, achieves up to a $1.99\times$ end-to-end decoding speedup at 70\% FFN sparsity, and is compatible with quantization and sparse attention.

cs.LG↗

arXiv2026

Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study

Deep learning compilers (DLCs) are designed to translate deep learning programs into optimized, hardware-specific code. Typically, DLC frontends translate programs into graph-based intermediate representations (IRs) to enable optimizations. Defects introduced during this stage (termed \emph{fBug}s) are severe yet understudied, as prior work predominantly focuses on low-level APIs and operators or treats DLCs as monolithic entities. To bridge this gap, we conduct the first systematic empirical study of \emph{fBug}s in TorchDynamo, the default DLC frontend for PyTorch 2, the most popular DL framework. Leveraging a domain-knowledge-enhanced LLM-aided methodology, we analyze 123 \emph{fBug}s and construct a taxonomy comprising 7 root cause categories and 15 subcategories. Our findings provide actionable insights for DLC development and testing. Furthermore, we leverage the LLM to generate targeted, root cause-aware test cases to detect new bugs. We uncovered 23 previously unknown \emph{fBug}s in recent releases (15 confirmed) across eight (sub)categories, demonstrating the efficacy of our methodology in testing and hardening DLC frontends.

cs.PL↗

arXiv2025

Temac: Multi-Agent Collaboration for Automated Web GUI Testing

Quality assurance of web applications is critical, as web applications play an essential role in people's daily lives. To reduce labor costs, automated web GUI testing (AWGT) is widely adopted, exploring web applications via GUI actions such as clicks and text inputs. However, these approaches face limitations in generating continuous and meaningful action sequences capable of covering complex functionalities. Recent work incorporates large language models (LLMs) for GUI testing. However, these approaches face various challenges, including low efficiency of LLMs, high complexity of rich web application contexts, and a low success rate of LLMs in executing GUI tasks. To address these challenges, in this paper, we propose Temac, an approach that enhances AWGT using LLM-based multi-agent collaboration to increase code coverage. Temac is motivated by our insight that LLMs can enhance AWGT in executing complex functionalities, while the information discovered during AWGT can, in turn, be provided as the domain knowledge to improve the LLM-based task execution. Specifically, given a web application, Temac initially runs an existing approach to broadly explore application states. When the testing coverage stagnates, Temac then employs LLM-based agents to summarize the collected information to form a knowledge base and to infer not-covered functionalities. Guided by this knowledge base, Temac finally uses specialized LLM-based agents to target and execute the not-covered functionalities, reaching deeper states beyond those explored by the existing approach. Our evaluation results show that Temac exceeds state-of-the-art approaches from 12.5% to 60.3% on average code coverage on six complex open-source web applications, while revealing 445 unique failures in the top 20 real-world web applications. These results strongly demonstrate the effectiveness and the general applicability of Temac.

cs.SE↗

arXiv2025

Deep Reinforcement Learning for Automated Web GUI Testing

Automated GUI testing of web applications has always been considered a challenging task considering their large state space and complex interaction logic. Deep Reinforcement Learning (DRL) is a recent extension of Reinforcement Learning (RL), which takes advantage of the powerful learning capabilities of neural networks, making it suitable for complex exploration space. In this paper, leveraging the capability of deep reinforcement learning, we propose WebRLED, an effective approach for automated GUI testing of complex web applications. WebRLED has the following characteristics: (1) a grid-based action value learning technique, which can improve the efficiency of state space exploration; (2) a novel action discriminator which can be trained during the exploration to identify more actions; (3) an adaptive, curiosity-driven reward model, which considers the novelty of an explored state within an episode and global history, and can guide exploration continuously. We conduct a comprehensive evaluation of WebRLED on 12 open-source web applications and a field study of the top 50 most popular web applications in the world. The experimental results show that WebRLED achieves higher code/state coverage and failure detection rate compared to existing state-of-the-art (SOTA) techniques. Furthermore, WebRLED finds 695 unique failures in 50 real-world applications.

cs.SE↗

arXiv2025

Testing the Fault-Tolerance of Multi-Sensor Fusion Perception in Autonomous Driving Systems

High-level Autonomous Driving Systems (ADSs), such as Google Waymo and Baidu Apollo, typically rely on multi-sensor fusion (MSF) based approaches to perceive their surroundings. This strategy increases perception robustness by combining the respective strengths of the camera and LiDAR and directly affects the safety-critical driving decisions of autonomous vehicles (AVs). However, in real-world autonomous driving scenarios, cameras and LiDAR are subject to various faults, which can probably significantly impact the decision-making and behaviors of ADSs. Existing MSF testing approaches only discovered corner cases that the MSF-based perception cannot accurately detected by MSF-based perception, while lacking research on how sensor faults affect the system-level behaviors of ADSs. To address this gap, we conduct the first exploration of the fault tolerance of MSF perception-based ADS for sensor faults. In this paper, we systematically and comprehensively build fault models for cameras and LiDAR in AVs and inject them into the MSF perception-based ADS to test its behaviors in test scenarios. To effectively and efficiently explore the parameter spaces of sensor fault models, we design a feedback-guided differential fuzzer to discover the safety violations of MSF perception-based ADS caused by the injected sensor faults. We evaluate FADE on the representative and practical industrial ADS, Baidu Apollo. Our evaluation results demonstrate the effectiveness and efficiency of FADE, and we conclude some useful findings from the experimental results. To validate the findings in the physical world, we use a real Baidu Apollo 6.0 EDU autonomous vehicle to conduct the physical experiments, and the results show the practical significance of our findings.

cs.RO↗

arXiv2025

An LLM-Empowered Adaptive Evolutionary Algorithm For Multi-Component Deep Learning Systems

Multi-objective evolutionary algorithms (MOEAs) are widely used for searching optimal solutions in complex multi-component applications. Traditional MOEAs for multi-component deep learning (MCDL) systems face challenges in enhancing the search efficiency while maintaining the diversity. To combat these, this paper proposes $\mu$MOEA, the first LLM-empowered adaptive evolutionary search algorithm to detect safety violations in MCDL systems. Inspired by the context-understanding ability of Large Language Models (LLMs), $\mu$MOEA promotes the LLM to comprehend the optimization problem and generate an initial population tailed to evolutionary objectives. Subsequently, it employs adaptive selection and variation to iteratively produce offspring, balancing the evolutionary efficiency and diversity. During the evolutionary process, to navigate away from the local optima, $\mu$MOEA integrates the evolutionary experience back into the LLM. This utilization harnesses the LLM's quantitative reasoning prowess to generate differential seeds, breaking away from current optimal solutions. We evaluate $\mu$MOEA in finding safety violations of MCDL systems, and compare its performance with state-of-the-art MOEA methods. Experimental results show that $\mu$MOEA can significantly improve the efficiency and diversity of the evolutionary search.

cs.NE↗

arXiv2024

Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition

Radio-Frequency (RF)-based Human Activity Recognition (HAR) rises as a promising solution for applications unamenable to techniques requiring computer visions. However, the scarcity of labeled RF data due to their non-interpretable nature poses a significant obstacle. Thanks to the recent breakthrough of foundation models (FMs), extracting deep semantic insights from unlabeled visual data become viable, yet these vision-based FMs fall short when applied to small RF datasets. To bridge this gap, we introduce FM-Fi, an innovative cross-modal framework engineered to translate the knowledge of vision-based FMs for enhancing RF-based HAR systems. FM-Fi involves a novel cross-modal contrastive knowledge distillation mechanism, enabling an RF encoder to inherit the interpretative power of FMs for achieving zero-shot learning. It also employs the intrinsic capabilities of FM and RF to remove extraneous features for better alignment between the two modalities. The framework is further refined through metric-based few-shot learning techniques, aiming to boost the performance for predefined HAR tasks. Comprehensive evaluations evidently indicate that FM-Fi rivals the effectiveness of vision-based methodologies, and the evaluation results provide empirical validation of FM-Fi's generalizability across various environments.

cs.CV↗

arXiv2024

LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos

Safety testing serves as the fundamental pillar for the development of autonomous driving systems (ADSs). To ensure the safety of ADSs, it is paramount to generate a diverse range of safety-critical test scenarios. While existing ADS practitioners primarily focus on reproducing real-world traffic accidents in simulation environments to create test scenarios, it's essential to highlight that many of these accidents do not directly result in safety violations for ADSs due to the differences between human driving and autonomous driving. More importantly, we observe that some accident-free real-world scenarios can not only lead to misbehaviors in ADSs but also be leveraged for the generation of ADS violations during simulation testing. Therefore, it is of significant importance to discover safety violations of ADSs from routine traffic scenarios (i.e., non-crash scenarios). We introduce LEADE, a novel methodology to achieve the above goal. It automatically generates abstract and concrete scenarios from real-traffic videos. Then it optimizes these scenarios to search for safety violations of the ADS in semantically consistent scenarios where human-driving worked safely. Specifically, LEADE enhances the ability of Large Multimodal Models (LMMs) to accurately construct abstract scenarios from traffic videos and generate concrete scenarios by multi-modal few-shot Chain of Thought (CoT). Based on them, LEADE assesses and increases the behavior differences between the ego vehicle and human-driving in semantic equivalent scenarios (here equivalent semantics means that each participant in test scenarios has the same behaviors as those observed in the original real traffic scenarios). We implement and evaluate LEADE on the industrial-grade Level-4 ADS, Apollo.

cs.SE↗

arXiv2019

Feedback-based, Automated Failure Testing of Microservice-based Applications

Modern distributed applications are moving toward a microservice architecture, in which each service is developed and managed independently, and new features and updates are delivered continuously. A guiding principle of microservice architecture is that it must be built to anticipate and mitigate a variety of hardware and software failures. In order to test the fault handling capabilities of microservces, this paper presents IntelliFT, a feedback-based, automated failure testing technique for microservice based applications, which aims to expose the defects in the fault-handling logic quickly. The initial experimental result on a medium-size microservice benchmark system shows that the proposed approach is effective.

cs.SE↗