OpenRaiser/Intern-Atlas huggingface dataset:
A Multi-Language Perspective on the Robustness of LLM Code Generation
Venue: arXiv cs.SE 2025
Year: 2025

Large language models have gained significant traction and popularity in recent times, extending their usage to code-generation tasks. While this field has garnered considerable attention, the exploration of testing and evaluating the robustness of code generation models remains an ongoing endeavor. Previous studies have primarily focused on code generation models specifically for the Python language, overlooking other widely-used programming languages. In this work, we conduct a comprehensive comparative analysis to assess the robustness performance of several prominent code generation models and investigate whether robustness can be improved by repairing perturbed docstrings using an LLM. Furthermore, we investigate how their performance varies across different programming languages. To accomplish this, we introduce perturbations in four key areas of the prompt: DocString, functionname, syntax, and format. We have compiled and released a dedicated dataset for this purpose. This work presents our experimental findings, shedding light on the performance of code generation models in various scenarios.
Newton-Puiseux Analysis for Interpretability and Calibration of Complex-Valued Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Complex-valued neural networks (CVNNs) are particularly suitable for handling phase-sensitive signals, including electrocardiography (ECG), radar/sonar, and wireless in-phase/quadrature (I/Q) streams. Nevertheless, their \emph{interpretability} and \emph{probability calibration} remain insufficiently investigated. In this work, we present a Newton--Puiseux framework that examines the \emph{local decision geometry} of a trained CVNN by (i) fitting a small, kink-aware polynomial surrogate to the \emph{logit difference} in the vicinity of uncertain inputs, and (ii) factorizing this surrogate using Newton--Puiseux expansions to derive analytic branch descriptors, including exponents, multiplicities, and orientations. These descriptors provide phase-aligned directions that induce class flips in the original network and allow for a straightforward, \emph{multiplicity-guided} temperature adjustment for improved calibration. We outline assumptions and diagnostic measures under which the surrogate proves informative and characterize potential failure modes arising from piecewise-holomorphic activations (e.g., modReLU). Our phase-aware analysis identifies sensitive directions and enhances Expected Calibration Error in two case studies beyond a controlled $\C^2$ synthetic benchmark -- namely, the MIT--BIH arrhythmia (ECG) dataset and RadioML 2016.10a (wireless modulation) -- when compared to uncalibrated softmax and standard post-hoc baselines. We also present confidence intervals, non-parametric tests, and quantify sensitivity to inaccuracies in estimating branch multiplicity. Crucially, this method requires no modifications to the architecture and applies to any CVNN with complex logits transformed to real moduli.
NANO-SLAM : Natural Gradient Gaussian Approximation for Vehicle SLAM
Venue: arXiv cs.RO 2025
Year: 2025

Accurate localization is a challenging task for autonomous vehicles, particularly in GPS-denied environments such as urban canyons and tunnels. In these scenarios, simultaneous localization and mapping (SLAM) offers a more robust alternative to GPS-based positioning, enabling vehicles to determine their position using onboard sensors and surrounding environment's landmarks. Among various vehicle SLAM approaches, Rao-Blackwellized particle filter (RBPF) stands out as one of the most widely adopted methods due to its efficient solution with logarithmic complexity relative to the map size. RBPF approximates the posterior distribution of the vehicle pose using a set of Monte Carlo particles through two main steps: sampling and importance weighting. The key to effective sampling lies in solving a distribution that closely approximates the posterior, known as the sampling distribution, to accelerate convergence. Existing methods typically derive this distribution via linearization, which introduces significant approximation errors due to the inherent nonlinearity of the system. To address this limitation, we propose a novel vehicle SLAM method called \textit{N}atural Gr\textit{a}dient Gaussia\textit{n} Appr\textit{o}ximation (NANO)-SLAM, which avoids linearization errors by modeling the sampling distribution as the solution to an optimization problem over Gaussian parameters and solving it using natural gradient descent. This approach improves the accuracy of the sampling distribution and consequently enhances localization performance. Experimental results on the long-distance Sydney Victoria Park vehicle SLAM dataset show that NANO-SLAM achieves over 50\% improvement in localization accuracy compared to the most widely used vehicle SLAM algorithms, with minimal additional computational cost.
Dynamic Embedded Topic Models: properties and recommendations based on diverse corpora
Venue: arXiv cs.CL 2025
Year: 2025

We measure the effects of several implementation choices for the Dynamic Embedded Topic Model, as applied to five distinct diachronic corpora, with the goal of isolating important decisions for its use and further development. We identify priorities that will maximize utility in applied scholarship, including the practical scalability of vocabulary size to best exploit the strengths of embedded representations, and more flexible modeling of intervals to accommodate the uneven temporal distributions of historical writing. Of similar importance, we find performance is not significantly or consistently affected by several aspects that otherwise limit the model's application or might consume the resources of a grid search.
Deep Reinforcement Learning for Automated Web GUI Testing
Venue: arXiv cs.SE 2025
Year: 2025

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.
Algorithmic Detection of Jacobi Stability for Systems of Second Order Differential Equations
Venue: arXiv cs.SC 2025
Year: 2025

This paper introduces an algorithmic approach to the analysis of Jacobi stability of systems of second order ordinary differential equations (ODEs) via the Kosambi--Cartan--Chern (KCC) theory. We develop an efficient symbolic program using Maple for computing the second KCC invariant for systems of second order ODEs in arbitrary dimension. The program allows us to systematically analyze Jacobi stability of a system of second order ODEs by means of real solving and solution classification using symbolic computation. The effectiveness of the proposed approach is illustrated by a model of wound strings, a two-dimensional airfoil model with cubic nonlinearity in supersonic flow and a 3-DOF tractor seat-operator model. The computational results on Jacobi stability of these models are further verified by numerical simulations. Moreover, our algorithmic approach allows us to detect hand-guided computation errors in published papers.
Efficient COLREGs-Compliant Collision Avoidance using Turning Circle-based Control Barrier Function
Venue: arXiv cs.RO 2025
Year: 2025

This paper proposes a computationally efficient collision avoidance algorithm using turning circle-based control barrier functions (CBFs) that comply with international regulations for preventing collisions at sea (COLREGs). Conventional CBFs often lack explicit consideration of turning capabilities and avoidance direction, which are key elements in developing a COLREGs-compliant collision avoidance algorithm. To overcome these limitations, we introduce two CBFs derived from left and right turning circles. These functions establish safety conditions based on the proximity between the traffic ships and the centers of the turning circles, effectively determining both avoidance directions and turning capabilities. The proposed method formulates a quadratic programming problem with the CBFs as constraints, ensuring safe navigation without relying on computationally intensive trajectory optimization. This approach significantly reduces computational effort while maintaining performance comparable to model predictive control-based methods. Simulation results validate the effectiveness of the proposed algorithm in enabling COLREGs-compliant, safe navigation, demonstrating its potential for reliable and efficient operation in complex maritime environments.
ODExAI: A Comprehensive Object Detection Explainable AI Evaluation
Venue: arXiv cs.CV 2025
Year: 2025

Explainable Artificial Intelligence (XAI) techniques for interpreting object detection models remain in an early stage, with no established standards for systematic evaluation. This absence of consensus hinders both the comparative analysis of methods and the informed selection of suitable approaches. To address this gap, we introduce the Object Detection Explainable AI Evaluation (ODExAI), a comprehensive framework designed to assess XAI methods in object detection based on three core dimensions: localization accuracy, faithfulness to model behavior, and computational complexity. We benchmark a set of XAI methods across two widely used object detectors (YOLOX and Faster R-CNN) and standard datasets (MS-COCO and PASCAL VOC). Empirical results demonstrate that region-based methods (e.g., D-CLOSE) achieve strong localization (PG = 88.49%) and high model faithfulness (OA = 0.863), though with substantial computational overhead (Time = 71.42s). On the other hand, CAM-based methods (e.g., G-CAME) achieve superior localization (PG = 96.13%) and significantly lower runtime (Time = 0.54s), but at the expense of reduced faithfulness (OA = 0.549). These findings demonstrate critical trade-offs among existing XAI approaches and reinforce the need for task-specific evaluation when deploying them in object detection pipelines. Our implementation and evaluation benchmarks are publicly available at: https://github.com/Analytics-Everywhere-Lab/odexai.
Quantitative evaluation of brain-inspired vision sensors in high-speed robotic perception
Venue: arXiv cs.RO 2025
Year: 2025

Perception systems in robotics encounter significant challenges in high-speed and dynamic conditions when relying on traditional cameras, where motion blur can compromise spatial feature integrity and task performance. Brain-inspired vision sensors (BVS) have recently gained attention as an alternative, offering high temporal resolution with reduced bandwidth and power requirements. Here, we present the first quantitative evaluation framework for two representative classes of BVSs in variable-speed robotic sensing, including event-based vision sensors (EVS) that detect asynchronous temporal contrasts, and the primitive-based sensor Tianmouc that employs a complementary mechanism to encode both spatiotemporal changes and intensity. A unified testing protocol is established, including crosssensor calibrations, standardized testing platforms, and quality metrics to address differences in data modality. From an imaging standpoint, we evaluate the effects of sensor non-idealities, such as motion-induced distortion, on the capture of structural information. For functional benchmarking, we examine task performance in corner detection and motion estimation under different rotational speeds. Results indicate that EVS performs well in highspeed, sparse scenarios and in modestly fast, complex scenes, but exhibits performance limitations in high-speed, cluttered settings due to pixel-level bandwidth variations and event rate saturation. In comparison, Tianmouc demonstrates consistent performance across sparse and complex scenarios at various speeds, supported by its global, precise, high-speed spatiotemporal gradient samplings. These findings offer valuable insights into the applicationdependent suitability of BVS technologies and support further advancement in this area.
OPAL: Visibility-aware LiDAR-to-OpenStreetMap Place Recognition via Adaptive Radial Fusion
Venue: arXiv cs.CV 2025
Year: 2025

LiDAR place recognition is a critical capability for autonomous navigation and cross-modal localization in large-scale outdoor environments. Existing approaches predominantly depend on pre-built 3D dense maps or aerial imagery, which impose significant storage overhead and lack real-time adaptability. In this paper, we propose OPAL, a novel framework for LiDAR place recognition that leverages OpenStreetMap (OSM) as a lightweight and up-to-date prior. Our key innovation lies in bridging the domain disparity between sparse LiDAR scans and structured OSM data through two carefully designed components. First, a cross-modal visibility mask that identifies observable regions from both modalities to guide feature alignment. Second, an adaptive radial fusion module that dynamically consolidates radial features into discriminative global descriptors. Extensive experiments on KITTI and KITTI-360 datasets demonstrate OPAL's superiority, achieving 15.98% higher recall at 1m threshold for top-1 retrieved matches, along with 12x faster inference speed compared to the state-of-the-art approach. Code and data are publicly available at: https://github.com/kang-1-2-3/OPAL.
VI3NR: Variance Informed Initialization for Implicit Neural Representations
Venue: arXiv cs.CV 2025
Year: 2025

Implicit Neural Representations (INRs) are a versatile and powerful tool for encoding various forms of data, including images, videos, sound, and 3D shapes. A critical factor in the success of INRs is the initialization of the network, which can significantly impact the convergence and accuracy of the learned model. Unfortunately, commonly used neural network initializations are not widely applicable for many activation functions, especially those used by INRs. In this paper, we improve upon previous initialization methods by deriving an initialization that has stable variance across layers, and applies to any activation function. We show that this generalizes many previous initialization methods, and has even better stability for well studied activations. We also show that our initialization leads to improved results with INR activation functions in multiple signal modalities. Our approach is particularly effective for Gaussian INRs, where we demonstrate that the theory of our initialization matches with task performance in multiple experiments, allowing us to achieve improvements in image, audio, and 3D surface reconstruction.
Efficient Serverless Cold Start: Reducing Library Loading Overhead by Profile-guided Optimization
Venue: arXiv cs.DC 2025
Year: 2025

Serverless computing abstracts away server management, enabling automatic scaling, efficient resource utilization, and cost-effective pricing models. However, despite these advantages, it faces the significant challenge of cold-start latency, adversely impacting end-to-end performance. Our study shows that many serverless functions initialize libraries that are rarely or never used under typical workloads, thus introducing unnecessary overhead. Although existing static analysis techniques can identify unreachable libraries, they fail to address workload-dependent inefficiencies, resulting in limited performance improvements. To overcome these limitations, we present SLIMSTART, a profile-guided optimization tool designed to identify and mitigate inefficient library usage patterns in serverless applications. By leveraging statistical sampling and call-path profiling, SLIMSTART collects runtime library usage data, generates detailed optimization reports, and applies automated code transformations to reduce cold-start overhead. Furthermore, SLIMSTART integrates seamlessly into CI/CD pipelines, enabling adaptive monitoring and continuous optimizations tailored to evolving workloads. Through extensive evaluation across three benchmark suites and four real-world serverless applications, SLIMSTART achieves up to a 2.30X speedup in initialization latency, a 2.26X improvement in end-to-end latency, and a 1.51X reduction in memory usage, demonstrating its effectiveness in addressing cold-start inefficiencies and optimizing resource utilization.
Sojourner under Sabotage: A Serious Testing and Debugging Game
Venue: arXiv cs.SE 2025
Year: 2025

Teaching software testing and debugging is a critical yet challenging task in computer science education, often hindered by low student engagement and the perceived monotony of these activities. Sojourner under Sabotage, a browser-based serious game, reimagines this learning experience by blending education with an immersive and interactive storyline. Players take on the role of a spaceship crew member, using unit testing and debugging techniques to identify and repair sabotaged components across seven progressively challenging levels. A study with 79 students demonstrates that the game is a powerful tool for enhancing motivation, engagement, and skill development. These findings underscore the transformative potential of serious games in making essential software engineering practices accessible and enjoyable.
Explanatory Summarization with Discourse-Driven Planning
Venue: arXiv cs.CL 2025
Year: 2025

Lay summaries for scientific documents typically include explanations to help readers grasp sophisticated concepts or arguments. However, current automatic summarization methods do not explicitly model explanations, which makes it difficult to align the proportion of explanatory content with human-written summaries. In this paper, we present a plan-based approach that leverages discourse frameworks to organize summary generation and guide explanatory sentences by prompting responses to the plan. Specifically, we propose two discourse-driven planning strategies, where the plan is conditioned as part of the input or part of the output prefix, respectively. Empirical experiments on three lay summarization datasets show that our approach outperforms existing state-of-the-art methods in terms of summary quality, and it enhances model robustness, controllability, and mitigates hallucination.
Optimal Static Fully Indexable Dictionaries
Venue: arXiv cs.DS 2025
Year: 2025

Fully indexable dictionaries (FID) store sets of integer keys while supporting rank/select queries. They serve as basic building blocks in many succinct data structures. Despite the great importance of FIDs, no known FID is succinct with efficient query time when the universe size $U$ is a large polynomial in the number of keys $n$, which is the conventional parameter regime for dictionary problems. In this paper, we design an FID that uses $\log \binom{U}{n} + \frac{n}{(\log U / t)^{Ω(t)}}$ bits of space, and answers rank/select queries in $O(t + \log \log n)$ time in the worst case, for any parameter $1 \le t \le \log n / \log \log n$, provided $U = n^{1 + Θ(1)}$. This time-space trade-off matches known lower bounds for FIDs [Pǎtraşcu & Thorup STOC 2006; Pǎtraşcu & Viola SODA 2010] when $t \le \log^{0.99} n$. Our techniques also lead to efficient succinct data structures for the fundamental problem of maintaining $n$ integers each of $\ell = Θ(\log n)$ bits and supporting partial-sum queries, with a trade-off between $O(t)$ query time and $n\ell + n / (\log n / t)^{Ω(t)}$ bits of space. Prior to this work, no known data structure for the partial-sum problem achieves constant query time with $n \ell + o(n)$ bits of space usage.
Symmetric Policy Design for Multi-Agent Dispatch Coordination in Supply Chains
Venue: arXiv cs.MA 2025
Year: 2025

We study a decentralized dispatch coordination problem in a multi-agent supply chain setting with shared logistics capacity. We propose symmetric (identical) dispatch strategies for all agents, enabling efficient coordination without centralized control. Using a common information approach, we derive a dynamic programming solution that computes optimal symmetric dispatch strategies by transforming the multi-agent problem into a tractable dynamic program on the agents common information state. Simulation results demonstrate that our method significantly reduces coordination cost compared to baseline heuristics, including belief-based strategies and an always-dispatch policy. These findings highlight the benefits of combining symmetric strategy design with a common information-based dynamic programming framework for improving multi-agent coordination performance.
Dynamic Arthroscopic Navigation System for Anterior Cruciate Ligament Reconstruction Based on Multi-level Memory Architecture
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a dynamic arthroscopic navigation system based on multi-level memory architecture for anterior cruciate ligament (ACL) reconstruction surgery. The system extends our previously proposed markerless navigation method from static image matching to dynamic video sequence tracking. By integrating the Atkinson-Shiffrin memory model's three-level architecture (sensory memory, working memory, and long-term memory), our system maintains continuous tracking of the femoral condyle throughout the surgical procedure, providing stable navigation support even in complex situations involving viewpoint changes, instrument occlusion, and tissue deformation. Unlike existing methods, our system operates in real-time on standard arthroscopic equipment without requiring additional tracking hardware, achieving 25.3 FPS with a latency of only 39.5 ms, representing a 3.5-fold improvement over our previous static system. For extended sequences (1000 frames), the dynamic system maintained an error of 5.3 plus-minus 1.5 pixels, compared to the static system's 12.6 plus-minus 3.7 pixels - an improvement of approximately 45 percent. For medium-length sequences (500 frames) and short sequences (100 frames), the system achieved approximately 35 percent and 19 percent accuracy improvements, respectively. Experimental results demonstrate the system overcomes limitations of traditional static matching methods, providing new technical support for improving surgical precision in ACL reconstruction.