OpenRaiser/Intern-Atlas huggingface dataset:
Automated Unit Test Case Generation: A Systematic Literature Review
Venue: arXiv cs.SE 2025
Year: 2025

Software is omnipresent within all factors of society. It is thus important to ensure that software are well tested to mitigate bad user experiences as well as the potential for severe financial and human losses. Software testing is however expensive and absorbs valuable time and resources. As a result, the field of automated software testing has grown of interest to researchers in past decades. In our review of present and past research papers, we have identified an information gap in the areas of improvement for the Genetic Algorithm and Particle Swarm Optimisation. A gap in knowledge in the current challenges that face automated testing has also been identified. We therefore present this systematic literature review in an effort to consolidate existing knowledge in regards to the evolutionary approaches as well as their improvements and resulting limitations. These improvements include hybrid algorithm combinations as well as interoperability with mutation testing and neural networks. We will also explore the main test criterion that are used in these algorithms alongside the challenges currently faced in the field related to readability, mocking and more.
Thoughtful, Confused, or Untrustworthy: How Text Presentation Influences Perceptions of AI Writing Tools
Venue: arXiv cs.HC 2025
Year: 2025

AI writing tools have been shown to dramatically change the way people write, yet the effects of AI text presentation are not well understood nor always intentionally designed. Although text presentation in existing large language model interfaces is linked to the speed of the underlying model, text presentation speed can impact perceptions of AI systems, potentially influencing whether AI suggestions are accepted or rejected. In this paper, we analyze the effects of varying text generation speed in creative and professional writing scenarios on an online platform (n=297). We find that speed is correlated with perceived humanness and trustworthiness of the AI tool, as well as the perceived quality of the generated text. We discuss its implications on creative and writing processes, along with future steps in the intentional design of AI writing tool interfaces.
GSFeatLoc: Visual Localization Using Feature Correspondence on 3D Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we present a method for localizing a query image with respect to a precomputed 3D Gaussian Splatting (3DGS) scene representation. First, the method uses 3DGS to render a synthetic RGBD image at some initial pose estimate. Second, it establishes 2D-2D correspondences between the query image and this synthetic image. Third, it uses the depth map to lift the 2D-2D correspondences to 2D-3D correspondences and solves a perspective-n-point (PnP) problem to produce a final pose estimate. Results from evaluation across three existing datasets with 38 scenes and over 2,700 test images show that our method significantly reduces both inference time (by over two orders of magnitude, from more than 10 seconds to as fast as 0.1 seconds) and estimation error compared to baseline methods that use photometric loss minimization. Results also show that our method tolerates large errors in the initial pose estimate of up to 55° in rotation and 1.1 units in translation (normalized by scene scale), achieving final pose errors of less than 5° in rotation and 0.05 units in translation on 90% of images from the Synthetic NeRF and Mip-NeRF360 datasets and on 42% of images from the more challenging Tanks and Temples dataset.
An Empirical Study on Common Defects in Modern Web Browsers Using Knowledge Embedding in GPT-4o
Venue: arXiv cs.SE 2025
Year: 2025

Technology is advancing at an unprecedented pace. With the advent of cutting-edge technologies, keeping up with rapid changes are becoming increasingly challenging. In addition to that, increasing dependencies on the cloud technologies have imposed enormous pressure on modern web browsers leading to adapting new technologies faster and making them more susceptible to defects/bugs. Although, many studies have explored browser bugs, a comparative study among the modern browsers generalizing the bug categories and their nature was still lacking. To fill this gap, we undertook an empirical investigation aimed at gaining insights into the prevalent bugs in Google Chromium and Mozilla Firefox as the representatives of modern web browsers. We used GPT-4.o to identify the defect (bugs) categories and analyze the clusters of the most commonly appeared bugs in the two prominent web browsers. Additionally, we compared our LLM based bug categorization with the traditional NLP based approach using TF-IDF and K-Means clustering. We found that although Google Chromium and Firefox have evolved together since almost around the same time (2006-2008), Firefox suffers from high number of bugs having extremely high defect-prone components compared to Chromium. This exploratory study offers valuable insights on the browser bugs and defect-prone components to the developers, enabling them to craft web browsers and web-applications with enhanced resilience and reduced errors.
Weighted GKAT: Completeness and Complexity
Venue: arXiv cs.LO 2025
Year: 2025

We propose Weighted Guarded Kleene Algebra with Tests (wGKAT), an uninterpreted weighted programming language equipped with branching, conditionals, and loops. We provide an operational semantics for wGKAT using a variant of weighted automata and introduce a sound and complete axiomatization. We also provide a polynomial time decision procedure for bisimulation equivalence.
CloudQC: A Network-aware Framework for Multi-tenant Distributed Quantum Computing
Venue: arXiv cs.DC 2025
Year: 2025

Distributed quantum computing (DQC) that allows a large quantum circuit to be executed simultaneously on multiple quantum processing units (QPUs) becomes a promising approach to increase the scalability of quantum computing. It is natural to envision the near-future DQC platform as a multi-tenant cluster of QPUs, called a Quantum Cloud. However, no existing DQC work has addressed the two key problems of running DQC in a multi-tenant quantum cloud: placing multiple quantum circuits to QPUs and scheduling network resources to complete these jobs. This work is the first attempt to design a circuit placement and resource scheduling framework for a multi-tenant environment. The proposed framework is called CloudQC, which includes two main functional components, circuit placement and network scheduler, with the objectives of optimizing both quantum network cost and quantum computing time. Experimental results with real quantum circuit workloads show that CloudQC significantly reduces the average job completion time compared to existing DQC placement algorithms for both single-circuit and multi-circuit DQC. We envision this work will motivate more future work on network-aware quantum cloud.
CrashFixer: A crash resolution agent for the Linux kernel
Venue: arXiv cs.SE 2025
Year: 2025

Code large language models (LLMs) have shown impressive capabilities on a multitude of software engineering tasks. In particular, they have demonstrated remarkable utility in the task of code repair. However, common benchmarks used to evaluate the performance of code LLMs are often limited to small-scale settings. In this work, we build upon kGym, which shares a benchmark for system-level Linux kernel bugs and a platform to run experiments on the Linux kernel. This paper introduces CrashFixer, the first LLM-based software repair agent that is applicable to Linux kernel bugs. Inspired by the typical workflow of a kernel developer, we identify the key capabilities an expert developer leverages to resolve a kernel crash. Using this as our guide, we revisit the kGym platform and identify key system improvements needed to practically run LLM-based agents at the scale of the Linux kernel (50K files and 20M lines of code). We implement these changes by extending kGym to create an improved platform - called kGymSuite, which will be open-sourced. Finally, the paper presents an evaluation of various repair strategies for such complex kernel bugs and showcases the value of explicitly generating a hypothesis before attempting to fix bugs in complex systems such as the Linux kernel. We also evaluated CrashFixer's capabilities on still open bugs, and found at least two patch suggestions considered plausible to resolve the reported bug.
Plant Disease Detection through Multimodal Large Language Models and Convolutional Neural Networks
Venue: arXiv cs.CV 2025
Year: 2025

Automation in agriculture plays a vital role in addressing challenges related to crop monitoring and disease management, particularly through early detection systems. This study investigates the effectiveness of combining multimodal Large Language Models (LLMs), specifically GPT-4o, with Convolutional Neural Networks (CNNs) for automated plant disease classification using leaf imagery. Leveraging the PlantVillage dataset, we systematically evaluate model performance across zero-shot, few-shot, and progressive fine-tuning scenarios. A comparative analysis between GPT-4o and the widely used ResNet-50 model was conducted across three resolutions (100, 150, and 256 pixels) and two plant species (apple and corn). Results indicate that fine-tuned GPT-4o models achieved slightly better performance compared to the performance of ResNet-50, achieving up to 98.12% classification accuracy on apple leaf images, compared to 96.88% achieved by ResNet-50, with improved generalization and near-zero training loss. However, zero-shot performance of GPT-4o was significantly lower, underscoring the need for minimal training. Additional evaluations on cross-resolution and cross-plant generalization revealed the models' adaptability and limitations when applied to new domains. The findings highlight the promise of integrating multimodal LLMs into automated disease detection pipelines, enhancing the scalability and intelligence of precision agriculture systems while reducing the dependence on large, labeled datasets and high-resolution sensor infrastructure. Large Language Models, Vision Language Models, LLMs and CNNs, Disease Detection with Vision Language Models, VLMs
A Geography-Inspired and Self-Adaptive Clustering Algorithm: A Study in Channel Measurement
Venue: arXiv cs.IT 2025
Year: 2025

The phenomenon that multi-path components (MPCs) arrive in clusters has been verified by channel measurements, and is widely adopted by cluster-based channel models. As a crucial intermediate processing step, MPC clustering bridges raw data in channel measurement and cluster characteristics for channel modeling. In this paper, a physical-interpretable and self-adaptive MPC clustering algorithm is proposed, which can locate both single-point and wide-spread scatterers without prior knowledge. Inspired by the concept in geography, a novel metaphor that interprets features of MPC attributes in the power-delay-angle profile (PDAP) as topographic concepts is developed. In light of the interpretation, the proposed algorithm disassembles the PDAP by constructing contour lines and identifying characteristic points that indicate the skeleton of MPC clusters, which are fitted by analytical models that associate MPCs with physical scatterer locations. Besides, a new clustering performance index, the power gradient consistency index, is proposed. Calculated as the weighted Spearman correlation coefficient between the power and the distance to the center, the index captures the intrinsic property of MPC clusters that the dominant high-power path is surrounded by lower-power paths. The performance of the proposed algorithm is analyzed and compared with the counterparts of conventional clustering algorithms based on the channel measurement conducted in an outdoor scenario. The proposed algorithm performs better in average Silhouette index and weighted Spearman correlation coefficient, and the average root mean square error (RMSE) of the estimated scatterer location is 0.1 m.
On Psychology of AI -- Does Primacy Effect Affect ChatGPT and Other LLMs?
Venue: arXiv cs.CL 2025
Year: 2025

We study the primacy effect in three commercial LLMs: ChatGPT, Gemini and Claude. We do this by repurposing the famous experiment Asch (1946) conducted using human subjects. The experiment is simple, given two candidates with equal descriptions which one is preferred if one description has positive adjectives first before negative ones and another description has negative adjectives followed by positive ones. We test this in two experiments. In one experiment, LLMs are given both candidates simultaneously in the same prompt, and in another experiment, LLMs are given both candidates separately. We test all the models with 200 candidate pairs. We found that, in the first experiment, ChatGPT preferred the candidate with positive adjectives listed first, while Gemini preferred both equally often. Claude refused to make a choice. In the second experiment, ChatGPT and Claude were most likely to rank both candidates equally. In the case where they did not give an equal rating, both showed a clear preference to a candidate that had negative adjectives listed first. Gemini was most likely to prefer a candidate with negative adjectives listed first.
A Survey on GUI Agents with Foundation Models Enhanced by Reinforcement Learning
Venue: arXiv cs.AI 2025
Year: 2025

Graphical User Interface (GUI) agents, driven by Multi-modal Large Language Models (MLLMs), have emerged as a promising paradigm for enabling intelligent interaction with digital systems. This paper provides a structured survey of recent advances in GUI agents, focusing on architectures enhanced by Reinforcement Learning (RL). We first formalize GUI agent tasks as Markov Decision Processes and discuss typical execution environments and evaluation metrics. We then review the modular architecture of (M)LLM-based GUI agents, covering Perception, Planning, and Acting modules, and trace their evolution through representative works. Furthermore, we categorize GUI agent training methodologies into Prompt-based, Supervised Fine-Tuning (SFT)-based, and RL-based approaches, highlighting the progression from simple prompt engineering to dynamic policy learning via RL. Our summary illustrates how recent innovations in multimodal perception, decision reasoning, and adaptive action generation have significantly improved the generalization and robustness of GUI agents in complex real-world environments. We conclude by identifying key challenges and future directions for building more capable and reliable GUI agents.
Large-scale visual SLAM for in-the-wild videos
Venue: arXiv cs.CV 2025
Year: 2025

Accurate and robust 3D scene reconstruction from casual, in-the-wild videos can significantly simplify robot deployment to new environments. However, reliable camera pose estimation and scene reconstruction from such unconstrained videos remains an open challenge. Existing visual-only SLAM methods perform well on benchmark datasets but struggle with real-world footage which often exhibits uncontrolled motion including rapid rotations and pure forward movements, textureless regions, and dynamic objects. We analyze the limitations of current methods and introduce a robust pipeline designed to improve 3D reconstruction from casual videos. We build upon recent deep visual odometry methods but increase robustness in several ways. Camera intrinsics are automatically recovered from the first few frames using structure-from-motion. Dynamic objects and less-constrained areas are masked with a predictive model. Additionally, we leverage monocular depth estimates to regularize bundle adjustment, mitigating errors in low-parallax situations. Finally, we integrate place recognition and loop closure to reduce long-term drift and refine both intrinsics and pose estimates through global bundle adjustment. We demonstrate large-scale contiguous 3D models from several online videos in various environments. In contrast, baseline methods typically produce locally inconsistent results at several points, producing separate segments or distorted maps. In lieu of ground-truth pose data, we evaluate map consistency, execution time and visual accuracy of re-rendered NeRF models. Our proposed system establishes a new baseline for visual reconstruction from casual uncontrolled videos found online, demonstrating more consistent reconstructions over longer sequences of in-the-wild videos than previously achieved.
Style-Adaptive Detection Transformer for Single-Source Domain Generalized Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

Single-source domain generalization (SDG) in object detection aims to develop a detector using only source domain data that generalizes well to unseen target domains. Existing methods are primarily CNN-based and improve robustness through data augmentation combined with feature alignment. However, these methods are limited, as augmentation is only effective when the synthetic distribution approximates that of unseen domains, thus failing to ensure generalization across diverse scenarios. While DEtection TRansformer (DETR) has shown strong generalization in domain adaptation due to global context modeling, its potential for SDG remains underexplored. To this end, we propose Style-Adaptive DEtection TRansformer (SA-DETR), a DETR-based detector tailored for SDG. SA-DETR introduces an online domain style adapter that projects the style representation of unseen domains into the source domain via a dynamic memory bank. This bank self-organizes into diverse style prototypes and is continuously updated under a test-time adaptation framework, enabling effective style rectification. Additionally, we design an object-aware contrastive learning module to promote extraction of domain-invariant features. By applying gating masks that constrain contrastive learning in both spatial and semantic dimensions, this module facilitates instance-level cross-domain contrast and enhances generalization. Extensive experiments across five distinct weather scenarios demonstrate that SA-DETR consistently outperforms existing methods in both detection accuracy and domain generalization capability.
Taxonomic Trace Links: Rethinking Traceability and its Benefits
Venue: arXiv cs.SE 2025
Year: 2025

Traceability greatly supports knowledge-intensive tasks, e.g., coverage check and impact analysis. Despite its clear benefits, the \emph{practical} implementation of traceability poses significant challenges, leading to a reduced focus on the creation and maintenance of trace links. We propose a new approach -- Taxonomic Trace Links (TTL) -- which rethinks traceability and its benefits. With TTL, trace links are created indirectly through a domain-specific taxonomy, a simplified version of a domain model. TTL has the potential to address key traceability challenges, such as the granularity of trace links, the lack of a common data structure among software development artifacts, and unclear responsibility for traceability. We explain how TTL addresses these challenges and perform an initial validation with practitioners. We identified six challenges associated with TTL implementation that need to be addressed. Finally, we propose a research roadmap to further develop and evaluate the technical solution of TTL. TTL appears to be particularly feasible in practice where a domain taxonomy is already established
Wavelet-Filtering of Symbolic Music Representations for Folk Tune Segmentation and Classification
Venue: arXiv cs.LG 2025
Year: 2025

The aim of this study is to evaluate a machine-learning method in which symbolic representations of folk songs are segmented and classified into tune families with Haar-wavelet filtering. The method is compared with previously proposed Gestalt-based method. Melodies are represented as discrete symbolic pitch-time signals. We apply the continuous wavelet transform (CWT) with the Haar wavelet at specific scales, obtaining filtered versions of melodies emphasizing their information at particular time-scales. We use the filtered signal for representation and segmentation, using the wavelet coefficients' local maxima to indicate local boundaries and classify segments by means of k-nearest neighbours based on standard vector-metrics (Euclidean, cityblock), and compare the results to a Gestalt-based segmentation method and metrics applied directly to the pitch signal. We found that the wavelet based segmentation and wavelet-filtering of the pitch signal lead to better classification accuracy in cross-validated evaluation when the time-scale and other parameters are optimized.
Starfish: Rebalancing Multi-Party Off-Chain Payment Channels
Venue: arXiv cs.CR 2025
Year: 2025

Blockchain technology has revolutionized the way transactions are executed, but scalability remains a major challenge. Payment Channel Network (PCN), as a Layer-2 scaling solution, has been proposed to address this issue. However, skewed payments can deplete the balance of one party within a channel, restricting the ability of PCNs to transact through a path and subsequently reducing the transaction success rate. To address this issue, the technology of rebalancing has been proposed. However, existing rebalancing strategies in PCNs are limited in their capacity and efficiency. Cycle-based approaches only address rebalancing within groups of nodes that form a cycle network, while non-cycle-based approaches face high complexity of on-chain operations and limitations on rebalancing capacity. In this study, we propose Starfish, a rebalancing approach that captures the star-shaped network structure to provide high rebalancing efficiency and large channel capacity. Starfish requires only $N$-time on-chain operations to connect independent channels and aggregate the total budget of all channels. To demonstrate the correctness and advantages of our method, we provide a formal security proof of the Starfish protocol and conduct comparative experiments with existing rebalancing techniques.
WakeLoc: An Ultra-Low Power, Accurate and Scalable On-Demand RTLS using Wake-Up Radios
Venue: arXiv cs.NI 2025
Year: 2025

For future large scale robotic moon missions, the availability of infrastructure-less, cheap and low power real-time locating systems (RTLSs) is critical. Traditional RTLS face significant trade-offs between power consumption and localization latency, often requiring anchors to be connected to the power grid or sacrificing speed for energy efficiency. This paper proposes WakeLoc, an on-demand RTLS based on ultra-wideband (UWB), enabling both low-latency and ultra-low power consumption by leveraging UWB wake-up radios (WuRs). In WakeLoc, tags independently start a localization procedure by sending a wake-up call (WuC) to anchors, before performing the actual localization. Distributed tags equipped with WuRs listen to the WuC and use passive listening of the UWB messages to determine their own position. Experimental measurements demonstrate that the localization accuracy in a 2D setup achieves less than 12.9cm error, both for the active and the passive tag. Additional power simulations based on real-world measurements were performed in a realistic environment, showing that anchors can achieve a power consumption as low as 15.53μW while the RTLS performs one on-demand localization per minute for 5 tags, thus operate up to 5.01 years on a single coin cell battery (690mWh).