OpenRaiser/Intern-Atlas huggingface dataset:
DocFetch - Towards Generating Software Documentation from Multiple Software Artifacts
Venue: arXiv cs.SE 2025
Year: 2025

Software Documentation plays a major role in the usage and development of a project. Widespread adoption of open source software projects contributes to larger and faster development of the projects, making it difficult to maintain the associated documentation. Existing automated approaches to generate documentation largely focus on source code. However, information useful for documentation is observed to be scattered across various artifacts that co-evolve with the source code. Leveraging this information across multiple artifacts can reduce the effort involved in maintaining documentation. Hence, we propose DocFetch, to generate different types of documentation from multiple software artifacts. We employ a multi-layer prompt based LLM and generate structured documentation corresponding to different documentation types for the data consolidated in DocMine dataset. We evaluate the performance of DocFetch using a manually curated groundtruth dataset by analysing the artifacts in DocMine. The evaluation yields a highest BLEU-4 score of 43.24% and ROUGE-L score of 0.39 for generation of api-related and file-related information from five documentation sources. The generation of other documentation type related information also reported BLEU-4 scores close to 30% indicating good performance of the approach. Thus,DocFetch can be employed to semi-automatically generate documentation, and helps in comprehending the projects with minimal effort in maintaining the documentation.
Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework
Venue: arXiv cs.CV 2025
Year: 2025

Human Action Anomaly Detection (HAAD) aims to identify anomalous actions given only normal action data during training. Existing methods typically follow a one-model-per-category paradigm, requiring separate training for each action category and a large number of normal samples. These constraints hinder scalability and limit applicability in real-world scenarios, where data is often scarce or novel categories frequently appear. To address these limitations, we propose a unified framework for HAAD that is compatible with few-shot scenarios. Our method constructs a category-agnostic representation space via contrastive learning, enabling AD by comparing test samples with a given small set of normal examples (referred to as the support set). To improve inter-category generalization and intra-category robustness, we introduce a generative motion augmentation strategy harnessing a diffusion-based foundation model for creating diverse and realistic training samples. Notably, to the best of our knowledge, our work is the first to introduce such a strategy specifically tailored to enhance contrastive learning for action AD. Extensive experiments on the HumanAct12 dataset demonstrate the state-of-the-art effectiveness of our approach under both seen and unseen category settings, regarding training efficiency and model scalability for few-shot HAAD.
Talking to Robots: A Practical Examination of Speech Foundation Models for HRI Applications
Venue: arXiv cs.RO 2025
Year: 2025

Automatic Speech Recognition (ASR) systems in real-world settings need to handle imperfect audio, often degraded by hardware limitations or environmental noise, while accommodating diverse user groups. In human-robot interaction (HRI), these challenges intersect to create a uniquely challenging recognition environment. We evaluate four state-of-the-art ASR systems on eight publicly available datasets that capture six dimensions of difficulty: domain-specific, accented, noisy, age-variant, impaired, and spontaneous speech. Our analysis demonstrates significant variations in performance, hallucination tendencies, and inherent biases, despite similar scores on standard benchmarks. These limitations have serious implications for HRI, where recognition errors can interfere with task performance, user trust, and safety.
Paraconsistent Constructive Modal Logic
Venue: arXiv cs.LO 2025
Year: 2025

We present a family of paraconsistent counterparts of the constructive modal logic CK. These logics aim to formalise reasoning about contradictory but non-trivial propositional attitudes like beliefs or obligations. We define their Kripke-style semantics based on intuitionistic frames with two valuations which provide independent support for truth and falsity; they are connected by strong negation as defined in Nelson's logic. A family of systems is obtained depending on whether both modal operators are defined using the same or by different accessibility relations for their positive and negative support. We propose Hilbert-style axiomatisations for all logics determined by this semantic framework. We also propose a~family of modular cut-free sequent calculi that we use to establish decidability.
Evaluating the Quality of the Quantified Uncertainty for (Re)Calibration of Data-Driven Regression Models
Venue: arXiv cs.LG 2025
Year: 2025

In safety-critical applications data-driven models must not only be accurate but also provide reliable uncertainty estimates. This property, commonly referred to as calibration, is essential for risk-aware decision-making. In regression a wide variety of calibration metrics and recalibration methods have emerged. However, these metrics differ significantly in their definitions, assumptions and scales, making it difficult to interpret and compare results across studies. Moreover, most recalibration methods have been evaluated using only a small subset of metrics, leaving it unclear whether improvements generalize across different notions of calibration. In this work, we systematically extract and categorize regression calibration metrics from the literature and benchmark these metrics independently of specific modelling methods or recalibration approaches. Through controlled experiments with real-world, synthetic and artificially miscalibrated data, we demonstrate that calibration metrics frequently produce conflicting results. Our analysis reveals substantial inconsistencies: many metrics disagree in their evaluation of the same recalibration result, and some even indicate contradictory conclusions. This inconsistency is particularly concerning as it potentially allows cherry-picking of metrics to create misleading impressions of success. We identify the Expected Normalized Calibration Error (ENCE) and the Coverage Width-based Criterion (CWC) as the most dependable metrics in our tests. Our findings highlight the critical role of metric selection in calibration research.
Research on Evaluation Methods for Patent Novelty Search Systems and Empirical Analysis
Venue: arXiv cs.IR 2025
Year: 2025

Patent novelty search systems are critical to IP protection and innovation assessment; their retrieval accuracy directly impacts patent quality. We propose a comprehensive evaluation methodology that builds high-quality, reproducible datasets from examiner citations and X-type citations extracted from technically consistent family patents, and evaluates systems using invention descriptions as inputs. Using Top-k Detection Rate and Recall as core metrics, we further conduct multi-dimensional analyses by language, technical field (IPC), and filing jurisdiction. Experiments show the method effectively exposes performance differences across scenarios and offers actionable evidence for system improvement. The framework is scalable and practical, providing a useful reference for development and optimization of patent novelty search systems
Interpretable Early Failure Detection via Machine Learning and Trace Checking-based Monitoring
Venue: arXiv cs.AI 2025
Year: 2025

Monitoring is a runtime verification technique that allows one to check whether an ongoing computation of a system (partial trace) satisfies a given formula. It does not need a complete model of the system, but it typically requires the construction of a deterministic automaton doubly exponential in the size of the formula (in the worst case), which limits its practicality. In this paper, we show that, when considering finite, discrete traces, monitoring of pure past (co)safety fragments of Signal Temporal Logic (STL) can be reduced to trace checking, that is, evaluation of a formula over a trace, that can be performed in time polynomial in the size of the formula and the length of the trace. By exploiting such a result, we develop a GPU-accelerated framework for interpretable early failure detection based on vectorized trace checking, that employs genetic programming to learn temporal properties from historical trace data. The framework shows a 2-10% net improvement in key performance metrics compared to the state-of-the-art methods.
Adaptive Output Steps: FlexiSteps Network for Dynamic Trajectory Prediction
Venue: arXiv cs.RO 2025
Year: 2025

Accurate trajectory prediction is vital for autonomous driving, robotics, and intelligent decision-making systems, yet traditional models typically rely on fixed-length output predictions, limiting their adaptability to dynamic real-world scenarios. In this paper, we introduce the FlexiSteps Network (FSN), a novel framework that dynamically adjusts prediction output time steps based on varying contextual conditions. Inspired by recent advancements addressing observation length discrepancies and dynamic feature extraction, FSN incorporates an pre-trained Adaptive Prediction Module (APM) to evaluate and adjust the output steps dynamically, ensuring optimal prediction accuracy and efficiency. To guarantee the plug-and-play of our FSN, we also design a Dynamic Decoder(DD). Additionally, to balance the prediction time steps and prediction accuracy, we design a scoring mechanism, which not only introduces the Fréchet distance to evaluate the geometric similarity between the predicted trajectories and the ground truth trajectories but the length of predicted steps is also considered. Extensive experiments conducted on benchmark datasets including Argoverse and INTERACTION demonstrate the effectiveness and flexibility of our proposed FSN framework.
MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian Splatting
Venue: arXiv cs.GR 2025
Year: 2025

Surface reconstruction has been widely studied in computer vision and graphics. However, existing surface reconstruction works struggle to recover accurate scene geometry when the input views are extremely sparse. To address this issue, we propose MeshSplat, a generalizable sparse-view surface reconstruction framework via Gaussian Splatting. Our key idea is to leverage 2DGS as a bridge, which connects novel view synthesis to learned geometric priors and then transfers these priors to achieve surface reconstruction. Specifically, we incorporate a feed-forward network to predict per-view pixel-aligned 2DGS, which enables the network to synthesize novel view images and thus eliminates the need for direct 3D ground-truth supervision. To improve the accuracy of 2DGS position and orientation prediction, we propose a Weighted Chamfer Distance Loss to regularize the depth maps, especially in overlapping areas of input views, and also a normal prediction network to align the orientation of 2DGS with normal vectors predicted by a monocular normal estimator. Extensive experiments validate the effectiveness of our proposed improvement, demonstrating that our method achieves state-of-the-art performance in generalizable sparse-view mesh reconstruction tasks. Project Page: https://hanzhichang.github.io/meshsplat_web
In-Memory Computing Enabled Deep MIMO Detection to Support Ultra-Low-Latency Communications
Venue: arXiv cs.AR 2025
Year: 2025

The development of sixth-generation (6G) mobile networks imposes unprecedented latency and reliability demands on multiple-input multiple-output (MIMO) communication systems, a key enabler of high-speed radio access. Recently, deep unfolding-based detectors, which map iterative algorithms onto neural network architectures, have emerged as a promising approach, combining the strengths of model-driven and data-driven methods to achieve high detection accuracy with relatively low complexity. However, algorithmic innovation alone is insufficient; software-hardware co-design is essential to meet the extreme latency requirements of 6G (i.e., 0.1 milliseconds). This motivates us to propose leveraging in-memory computing, which is an analog computing technology that integrates memory and computation within memristor circuits, to perform the intensive matrix-vector multiplication (MVM) operations inherent in deep MIMO detection at the nanosecond scale. Specifically, we introduce a novel architecture, called the deep in-memory MIMO (IM-MIMO) detector, characterized by two key features. First, each of its cascaded computational blocks is decomposed into channel-dependent and channel-independent neural network modules. Such a design minimizes the latency of memristor reprogramming in response to channel variations, which significantly exceeds computation time. Second, we develop a customized detector-training method that exploits prior knowledge of memristor-value statistics to enhance robustness against programming noise. Furthermore, we conduct a comprehensive analysis of the IM-MIMO detector's performance, evaluating detection accuracy, processing latency, and hardware complexity. Our study quantifies detection error as a function of various factors, including channel noise, memristor programming noise, and neural network size.
LLMulator: Generalizable Cost Modeling for Dataflow Accelerators with Input-Adaptive Control Flow
Venue: arXiv cs.AR 2025
Year: 2025

Accurate and fast performance prediction for dataflow-based accelerators is vital for efficient hardware design and design space exploration, yet existing methods struggle to generalize across architectures, applications, and input-dependent control flows. We present LLMulator, a progressive numeric modeling framework leveraging the program semantic knowledge of pre-trained large language models (LLMs) for robust, hardware- and application-aware prediction. Our numeric model treats performance values as categorical token sequences, enabling range-agnostic estimates and confidence-aware predictions for unseen applications. To handle input-dependent control flows, we introduce a reinforcement learning-based dynamic calibration method, reducing cycle prediction error by 9.7% over static models and converging to 11.2% error after a few iterations. For cross-hardware generalization, we develop a progressive data augmentation strategy that generates diverse datasets covering multi-level dataflow structures, memory parameters, and loop mapping primitives, significantly boosting prediction accuracy across architectures and configurations.
Effect of Performance Feedback Timing on Motor Learning for a Surgical Training Task
Venue: arXiv cs.RO 2025
Year: 2025

Objective: Robot-assisted minimally invasive surgery (RMIS) has become the gold standard for a variety of surgical procedures, but the optimal method of training surgeons for RMIS is unknown. We hypothesized that real-time, rather than post-task, error feedback would better increase learning speed and reduce errors. Methods: Forty-two surgical novices learned a virtual version of the ring-on-wire task, a canonical task in RMIS training. We investigated the impact of feedback timing with multi-sensory (haptic and visual) cues in three groups: (1) real-time error feedback, (2) trial replay with error feedback, and (3) no error feedback. Results: Participant performance was evaluated based on the accuracy of ring position and orientation during the task. Participants who received real-time feedback outperformed other groups in ring orientation. Additionally, participants who received feedback in replay outperformed participants who did not receive any error feedback on ring orientation during long, straight path sections. There were no significant differences between groups for ring position overall, but participants who received real-time feedback outperformed the other groups in positional accuracy on tightly curved path sections. Conclusion: The addition of real-time haptic and visual error feedback improves learning outcomes in a virtual surgical task over error feedback in replay or no error feedback at all. Significance: This work demonstrates that multi-sensory error feedback delivered in real time leads to better training outcomes as compared to the same feedback delivered after task completion. This novel method of training may enable surgical trainees to develop skills with greater speed and accuracy.
Box-Level Class-Balanced Sampling for Active Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

Training deep object detectors demands expensive bounding box annotation. Active learning (AL) is a promising technique to alleviate the annotation burden. Performing AL at box-level for object detection, i.e., selecting the most informative boxes to label and supplementing the sparsely-labelled image with pseudo labels, has been shown to be more cost-effective than selecting and labelling the entire image. In box-level AL for object detection, we observe that models at early stage can only perform well on majority classes, making the pseudo labels severely class-imbalanced. We propose a class-balanced sampling strategy to select more objects from minority classes for labelling, so as to make the final training data, \ie, ground truth labels obtained by AL and pseudo labels, more class-balanced to train a better model. We also propose a task-aware soft pseudo labelling strategy to increase the accuracy of pseudo labels. We evaluate our method on public benchmarking datasets and show that our method achieves state-of-the-art performance.
MalLoc: Toward Fine-grained Android Malicious Payload Localization via LLMs
Venue: arXiv cs.CR 2025
Year: 2025

The rapid evolution of Android malware poses significant challenges to the maintenance and security of mobile applications (apps). Traditional detection techniques often struggle to keep pace with emerging malware variants that employ advanced tactics such as code obfuscation and dynamic behavior triggering. One major limitation of these approaches is their inability to localize malicious payloads at a fine-grained level, hindering precise understanding of malicious behavior. This gap in understanding makes the design of effective and targeted mitigation strategies difficult, leaving mobile apps vulnerable to continuously evolving threats. To address this gap, we propose MalLoc, a novel approach that leverages the code understanding capabilities of large language models (LLMs) to localize malicious payloads at a fine-grained level within Android malware. Our experimental results demonstrate the feasibility and effectiveness of using LLMs for this task, highlighting the potential of MalLoc to enhance precision and interpretability in malware analysis. This work advances beyond traditional detection and classification by enabling deeper insights into behavior-level malicious logic and opens new directions for research, including dynamic modeling of localized threats and targeted countermeasure development.
Camera Pose Refinement via 3D Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

Camera pose refinement aims at improving the accuracy of initial pose estimation for applications in 3D computer vision. Most refinement approaches rely on 2D-3D correspondences with specific descriptors or dedicated networks, requiring reconstructing the scene again for a different descriptor or fully retraining the network for each scene. Some recent methods instead infer pose from feature similarity, but their lack of geometry constraints results in less accuracy. To overcome these limitations, we propose a novel camera pose refinement framework leveraging 3D Gaussian Splatting (3DGS), referred to as GS-SMC. Given the widespread usage of 3DGS, our method can employ an existing 3DGS model to render novel views, providing a lightweight solution that can be directly applied to diverse scenes without additional training or fine-tuning. Specifically, we introduce an iterative optimization approach, which refines the camera pose using epipolar geometric constraints among the query and multiple rendered images. Our method allows flexibly choosing feature extractors and matchers to establish these constraints. Extensive empirical evaluations on the 7-Scenes and the Cambridge Landmarks datasets demonstrate that our method outperforms state-of-the-art camera pose refinement approaches, achieving 53.3% and 56.9% reductions in median translation and rotation errors on 7-Scenes, and 40.7% and 53.2% on Cambridge.
TRUCE-AV: A Multimodal dataset for Trust and Comfort Estimation in Autonomous Vehicles
Venue: arXiv cs.HC 2025
Year: 2025

Understanding and estimating driver trust and comfort are essential for the safety and widespread acceptance of autonomous vehicles. Existing works analyze user trust and comfort separately, with limited real-time assessment and insufficient multimodal data. This paper introduces a novel multimodal dataset called TRUCE-AV, focusing on trust and comfort estimation in autonomous vehicles. The dataset collects real-time trust votes and continuous comfort ratings of 31 participants during a simulator-based fully autonomous driving. Simultaneously, physiological signals, such as heart rate, gaze, and emotions, along with environmental data (e.g., vehicle speed, nearby vehicle positions, and velocity), are recorded throughout the drives. Standard pre- and post-drive questionnaires were also administered to assess participants' trust in automation and overall well-being, enabling the correlation of subjective assessments with real-time responses. To demonstrate the utility of our dataset, we evaluated various machine learning models for trust and comfort estimation using physiological data. Our analysis showed that tree-based models like Random Forest and XGBoost and non-linear models such as KNN and MLP regressor achieved the best performance for trust classification and comfort regression. Additionally, we identified key features that contribute to these estimations by using SHAP analysis on the top-performing models. Our dataset enables the development of adaptive AV systems capable of dynamically responding to user trust and comfort levels non-invasively, ultimately enhancing safety, user experience, and human-centered vehicle design.
Evaluating Citizen Satisfaction with Saudi Arabia's E-Government Services: A Standards-Based, Theory-Informed Approach
Venue: arXiv cs.SE 2025
Year: 2025

As digital government platforms become central to public service delivery, understanding citizen assessment is crucial for enhancing usability, trust, and inclusivity. This study investigates citizen satisfaction with the e-government services in Saudi Arabia through a quality-in-use framework based on ISO/IEC 25010 and ISO/IEC 25022 standards, interpreted through the lens of the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to 500 citizens, yielding 276 valid responses. Satisfaction was evaluated across four dimensions: overall satisfaction, feature satisfaction, trust, and emotional engagement (pleasure). The findings demonstrate consistently high levels of satisfaction regarding usability and trust, aligning with Saudi Arabia's top-tier global ranking in e-government development. However, the results also highlight persistent challenges related to service clarity and system responsiveness. Emotional engagement was limited, indicating that users perceive these services primarily as functional tools rather than as engaging digital experiences. The study offers valuable insights for policymakers and contributes to the theoretical integration of standards-based and behavioral adoption models in the context of citizenship.