OpenRaiser/Intern-Atlas huggingface dataset:
Isolated Bangla Handwritten Character Classification using Transfer Learning
Venue: arXiv cs.CV 2025
Year: 2025

Bangla language consists of fifty distinct characters and many compound characters. Several notable studies have been performed to recognize Bangla characters, both handwritten and optical. Our approach uses transfer learning to classify the basic, distinct, as well as compound Bangla handwritten characters while avoiding the vanishing gradient problem. Deep Neural Network techniques such as 3D Convolutional Neural Network (3DCNN), Residual Neural Network (ResNet), and MobileNet are applied to generate an end-to-end classification of all possible standard formations of handwritten characters in the Bangla language. The Bangla Lekha Isolated dataset, which contains 166,105 Bangla character image samples categorized into 84 distinct classes, is used for this classification model. The model achieved 99.82% accuracy on training data and 99.46% accuracy on test data. Comparisons with various state-of-the-art benchmarks of Bangla handwritten character classification show that the proposed model achieves better accuracy in classifying the data.
Are We SOLID Yet? An Empirical Study on Prompting LLMs to Detect Design Principle Violations
Venue: arXiv cs.SE 2025
Year: 2025

Traditional static analysis methods struggle to detect semantic design flaws, such as violations of the SOLID principles, which require a strong understanding of object-oriented design patterns and principles. Existing solutions typically focus on individual SOLID principles or specific programming languages, leaving a gap in the ability to detect violations across all five principles in multi-language codebases. This paper presents a new approach: a methodology that leverages tailored prompt engineering to assess LLMs on their ability to detect SOLID violations across multiple languages. We present a benchmark of four leading LLMs-CodeLlama, DeepSeekCoder, QwenCoder, and GPT-4o Mini-on their ability to detect violations of all five SOLID principles. For this evaluation, we construct a new benchmark dataset of 240 manually validated code examples. Using this dataset, we test four distinct prompt strategies inspired by established zero-shot, few-shot, and chain-of-thought techniques to systematically measure their impact on detection accuracy. Our emerging results reveal a stark hierarchy among models, with GPT-4o Mini decisively outperforming others, yet even struggles with challenging principles like DIP. Crucially, we show that prompt strategy has a dramatic impact, but no single strategy is universally best; for instance, a deliberative ENSEMBLE prompt excels at OCP detection while a hint-based EXAMPLE prompt is superior for DIP violations. Across all experiments, detection accuracy is heavily influenced by language characteristics and degrades sharply with increasing code complexity. These initial findings demonstrate that effective, AI-driven design analysis requires not a single best model, but a tailored approach that matches the right model and prompt to the specific design context, highlighting the potential of LLMs to support maintainability through AI-assisted code analysis.
LSAM: Asynchronous Distributed Training with Landscape-Smoothed Sharpness-Aware Minimization
Venue: arXiv cs.LG 2025
Year: 2025

While Sharpness-Aware Minimization (SAM) improves generalization in deep neural networks by minimizing both loss and sharpness, it suffers from inefficiency in distributed large-batch training. We present Landscape-Smoothed SAM (LSAM), a novel optimizer that preserves SAM's generalization advantages while offering superior efficiency. LSAM integrates SAM's adversarial steps with an asynchronous distributed sampling strategy, generating an asynchronous distributed sampling scheme, producing a smoothed sharpness-aware loss landscape for optimization. This design eliminates synchronization bottlenecks, accelerates large-batch convergence, and delivers higher final accuracy compared to data-parallel SAM.
Forbal: Force Balanced 2-5 Degree of Freedom Robot Manipulator Built from a Five Bar Linkage
Venue: arXiv cs.RO 2025
Year: 2025

A force balanced manipulator design based on the closed chain planar five bar linkage is developed and experimentally validated. We present 2 variants as a modular design: Forbal-2, a planar 2-DOF manipulator, and its extension to 5-DOF spatial motion called Forbal-5. The design considerations in terms of geometric, kinematic, and dynamic design that fulfill the force balance conditions while maximizing workspace are discussed. Then, the inverse kinematics of both variants are derived from geometric principles. We validate the improvements from force balancing the manipulator through comparative experiments with counter mass balanced and unbalanced configurations. The results show how the balanced configuration yields a reduction in the average reaction moments of up to 66%, a reduction of average joint torques of up to 79%, as well as a noticeable reduction in position error for Forbal-2. For Forbal-5, which has a higher end effector payload mass, the joint torques are reduced up to 84% for the balanced configuration. Experimental results validate that the balanced manipulator design is suitable for applications where the reduction of joint torques and reaction forces/moments helps achieve millimeter level precision.
Kangaroo: A Private and Amortized Inference Framework over WAN for Large-Scale Decision Tree Evaluation
Venue: arXiv cs.CR 2025
Year: 2025

With the rapid adoption of Models-as-a-Service, concerns about data and model privacy have become increasingly critical. To solve these problems, various privacy-preserving inference schemes have been proposed. In particular, due to the efficiency and interpretability of decision trees, private decision tree evaluation (PDTE) has garnered significant attention. However, existing PDTE schemes suffer from significant limitations: their communication and computation costs scale with the number of trees, the number of nodes, or the tree depth, which makes them inefficient for large-scale models, especially over WAN networks. To address these issues, we propose Kangaroo, a private and amortized decision tree inference framework build upon packed homomorphic encryption. Specifically, we design a novel model hiding and encoding scheme, together with secure feature selection, oblivious comparison, and secure path evaluation protocols, enabling full amortization of the overhead as the number of nodes or trees scales. Furthermore, we enhance the performance and functionality of the framework through optimizations, including same-sharing-for-same-model, latency-aware, and adaptive encoding adjustment strategies. Kangaroo achieves a $14\times$ to $59\times$ performance improvement over state-of-the-art (SOTA) one-round interactive schemes in WAN environments. For large-scale decision tree inference tasks, it delivers a $3\times$ to $44\times$ speedup compared to existing schemes. Notably, Kangaroo enables the evaluation of a random forest with $969$ trees and $411825$ nodes in approximately $60$ ms per tree (amortized) under WAN environments.
Rashomon in the Streets: Explanation Ambiguity in Scene Understanding
Venue: arXiv cs.LG 2025
Year: 2025

Explainable AI (XAI) is essential for validating and trusting models in safety-critical applications like autonomous driving. However, the reliability of XAI is challenged by the Rashomon effect, where multiple, equally accurate models can offer divergent explanations for the same prediction. This paper provides the first empirical quantification of this effect for the task of action prediction in real-world driving scenes. Using Qualitative Explainable Graphs (QXGs) as a symbolic scene representation, we train Rashomon sets of two distinct model classes: interpretable, pair-based gradient boosting models and complex, graph-based Graph Neural Networks (GNNs). Using feature attribution methods, we measure the agreement of explanations both within and between these classes. Our results reveal significant explanation disagreement. Our findings suggest that explanation ambiguity is an inherent property of the problem, not just a modeling artifact.
Plan More, Debug Less: Applying Metacognitive Theory to AI-Assisted Programming Education
Venue: arXiv cs.CY 2025
Year: 2025

The growing adoption of generative AI in education highlights the need to integrate established pedagogical principles into AI-assisted learning environments. This study investigates the potential of metacognitive theory to inform AI-assisted programming education through a hint system designed around the metacognitive phases of planning, monitoring, and evaluation. Upon request, the system can provide three types of AI-generated hints--planning, debugging, and optimization--to guide students at different stages of problem-solving. Through a study with 102 students in an introductory data science programming course, we find that students perceive and engage with planning hints most highly, whereas optimization hints are rarely requested. We observe a consistent association between requesting planning hints and achieving higher grades across question difficulty and student competency. However, when facing harder tasks, students seek additional debugging but not more planning support. These insights contribute to the growing field of AI-assisted programming education by providing empirical evidence on the importance of pedagogical principles in AI-assisted learning.
Finding My Way: Influence of Different Audio Augmented Reality Navigation Cues on User Experience and Subjective Usefulness
Venue: arXiv cs.HC 2025
Year: 2025

As augmented reality (AR) becomes increasingly prevalent in mobile and context-aware applications, the role of auditory cues in guiding users through physical environments is becoming critical. This study investigates the effectiveness and user experience of various categories of audio cues, including fully non-verbal sounds and speech-derived Spearcons, during outdoor navigation tasks using the Meta Quest 3 headset. Twenty participants navigated five outdoor routes using audio-only cue types: Artificial Sounds, Nature Sounds, Spearcons, Musical Instruments, and Auditory Icons. Subjective evaluations were collected to assess the perceived effectiveness and user experience of each sound type. Results revealed significant differences in perceived novelty and stimulation across sound types. Artificial Sounds and Musical Instruments were rated higher than Spearcons in novelty, while Artificial Sounds were also rated higher than Spearcons in stimulation. Overall preference was evenly split between Nature Sounds and Artificial Sounds. These findings suggest that incorporating aspects of novelty and user engagement in auditory feedback design may enhance the effectiveness of AR navigation systems.
Triangle Detection in Worst-Case Sparse Graphs via Local Sketching
Venue: arXiv cs.DS 2025
Year: 2025

We present a non-algebraic, locality-preserving framework for triangle detection in worst-case sparse graphs. Our algorithm processes the graph in $O(\log n)$ independent layers and partitions incident edges into prefix-based classes where each class maintains a 1-sparse triple over a prime field. Potential witnesses are surfaced by pair-key (PK) alignment, and every candidate is verified by a three-stage, zero-false-positive pipeline: a class-level 1-sparse consistency check, two slot-level decodings, and a final adjacency confirmation. \textbf{To obtain single-run high-probability coverage, we further instantiate $R=c_G\log n$ independent PK groups per class (each probing a constant number of complementary buckets), which amplifies the per-layer hit rate from $Θ(1/\log n)$ to $1-n^{-Ω(1)}$ without changing the accounting.} A one-shot pairing discipline and class-term triggering yield a per-(layer,level) accounting bound of $O(m)$, while keep-coin concentration ensures that each vertex retains only $O(d^+(x))$ keys with high probability. Consequently, the total running time is $O(m\log^2 n)$ and the peak space is $O(m\log n)$, both with high probability. The algorithm emits a succinct Seeds+Logs artifact that enables a third party to replay all necessary checks and certify a NO-instance in $\tilde O(m\log n)$ time. We also prove a $Θ(1/\log n)$ hit-rate lower bound for any single PK family under a constant-probe local model (via Yao)--motivating the use of $Θ(\log n)$ independent groups--and discuss why global algebraic convolutions would break near-linear accounting or run into fine-grained barriers. We outline measured paths toward Las Vegas $O(m\log n)$ and deterministic near-linear variants.
Vibration Damping in Underactuated Cable-suspended Artwork -- Flying Belt Motion Control
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a comprehensive refurbishment of the interactive robotic art installation Standards and Double Standards by Rafael Lozano-Hemmer. The installation features an array of belts suspended from the ceiling, each actuated by stepper motors and dynamically oriented by a vision-based tracking system that follows the movements of exhibition visitors. The original system was limited by oscillatory dynamics, resulting in torsional and pendulum-like vibrations that constrained rotational speed and reduced interactive responsiveness. To address these challenges, the refurbishment involved significant upgrades to both hardware and motion control algorithms. A detailed mathematical model of the flying belt system was developed to accurately capture its dynamic behavior, providing a foundation for advanced control design. An input shaping method, formulated as a convex optimization problem, was implemented to effectively suppress vibrations, enabling smoother and faster belt movements. Experimental results demonstrate substantial improvements in system performance and audience interaction. This work exemplifies the integration of robotics, control engineering, and interactive art, offering new solutions to technical challenges in real-time motion control and vibration damping for large-scale kinetic installations.
TopoMap: A Feature-based Semantic Discriminator of the Topographical Regions in the Test Input Space
Venue: arXiv cs.LG 2025
Year: 2025

Testing Deep Learning (DL)-based systems is an open challenge. Although it is relatively easy to find inputs that cause a DL model to misbehave, the grouping of inputs by features that make the DL model under test fail is largely unexplored. Existing approaches for DL testing introduce perturbations that may focus on specific failure-inducing features, while neglecting others that belong to different regions of the feature space. In this paper, we create an explicit topographical map of the input feature space. Our approach, named TopoMap, is both black-box and model-agnostic as it relies solely on features that characterise the input space. To discriminate the inputs according to the specific features they share, we first apply dimensionality reduction to obtain input embeddings, which are then subjected to clustering. Each DL model might require specific embedding computations and clustering algorithms to achieve a meaningful separation of inputs into discriminative groups. We propose a novel way to evaluate alternative configurations of embedding and clustering techniques. We used a deep neural network (DNN) as an approximation of a human evaluator who could tell whether a pair of clusters can be discriminated based on the features of the included elements. We use such a DNN to automatically select the optimal topographical map of the inputs among all those that are produced by different embedding/clustering configurations. The evaluation results show that the maps generated by TopoMap consist of distinguishable and meaningful regions. In addition, we evaluate the effectiveness of TopoMap using mutation analysis. In particular, we assess whether the clusters in our topographical map allow for an effective selection of mutation-killing inputs. Experimental results show that our approach outperforms random selection by 35% on average on killable mutants; by 61% on non-killable ones.
Estudio de la eficiencia en la escalabilidad de GPUs para el entrenamiento de Inteligencia Artificial
Venue: arXiv cs.LG 2025
Year: 2025

Training large-scale deep learning models has become a key challenge for the scientific community and industry. While the massive use of GPUs can significantly speed up training times, this approach has a negative impact on efficiency. In this article, we present a detailed analysis of the times reported by MLPerf Training v4.1 on four workloads: BERT, Llama2 LoRA, RetinaNet, and Stable Diffusion, showing that there are configurations that optimise the relationship between performance, GPU usage, and efficiency. The results point to a break-even point that allows training times to be reduced while maximising efficiency.
Bridging Gaps Between Student and Expert Evaluations of AI-Generated Programming Hints
Venue: arXiv cs.CY 2025
Year: 2025

Generative AI has the potential to enhance education by providing personalized feedback to students at scale. Recent work has proposed techniques to improve AI-generated programming hints and has evaluated their performance based on expert-designed rubrics or student ratings. However, it remains unclear how the rubrics used to design these techniques align with students' perceived helpfulness of hints. In this paper, we systematically study the mismatches in perceived hint quality from students' and experts' perspectives based on the deployment of AI-generated hints in a Python programming course. We analyze scenarios with discrepancies between student and expert evaluations, in particular, where experts rated a hint as high-quality while the student found it unhelpful. We identify key reasons for these discrepancies and classify them into categories, such as hints not accounting for the student's main concern or not considering previous help requests. Finally, we propose and discuss preliminary results on potential methods to bridge these gaps, first by extending the expert-designed quality rubric and then by adapting the hint generation process, e.g., incorporating the student's comments or history. These efforts contribute toward scalable, personalized, and pedagogically sound AI-assisted feedback systems, which are particularly important for high-enrollment educational settings.
AgenTracer: Who Is Inducing Failure in the LLM Agentic Systems?
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Model (LLM)-based agentic systems, often comprising multiple models, complex tool invocations, and orchestration protocols, substantially outperform monolithic agents. Yet this very sophistication amplifies their fragility, making them more prone to system failure. Pinpointing the specific agent or step responsible for an error within long execution traces defines the task of agentic system failure attribution. Current state-of-the-art reasoning LLMs, however, remain strikingly inadequate for this challenge, with accuracy generally below 10%. To address this gap, we propose AgenTracer, the first automated framework for annotating failed multi-agent trajectories via counterfactual replay and programmed fault injection, producing the curated dataset TracerTraj. Leveraging this resource, we develop AgenTracer-8B, a lightweight failure tracer trained with multi-granular reinforcement learning, capable of efficiently diagnosing errors in verbose multi-agent interactions. On the Who&When benchmark, AgenTracer-8B outperforms giant proprietary LLMs like Gemini-2.5-Pro and Claude-4-Sonnet by up to 18.18%, setting a new standard in LLM agentic failure attribution. More importantly, AgenTracer-8B delivers actionable feedback to off-the-shelf multi-agent systems like MetaGPT and MaAS with 4.8-14.2% performance gains, empowering self-correcting and self-evolving agentic AI.
Temporal social network modeling of mobile connectivity data with graph neural networks
Venue: arXiv cs.SI 2025
Year: 2025

Graph neural networks (GNNs) have emerged as a state-of-the-art data-driven tool for modeling connectivity data of graph-structured complex networks and integrating information of their nodes and edges in space and time. However, as of yet, the analysis of social networks using the time series of people's mobile connectivity data has not been extensively investigated. In the present study, we investigate four snapshot - based temporal GNNs in predicting the phone call and SMS activity between users of a mobile communication network. In addition, we develop a simple non - GNN baseline model using recently proposed EdgeBank method. Our analysis shows that the ROLAND temporal GNN outperforms the baseline model in most cases, whereas the other three GNNs perform on average worse than the baseline. The results show that GNN based approaches hold promise in the analysis of temporal social networks through mobile connectivity data. However, due to the relatively small performance margin between ROLAND and the baseline model, further research is required on specialized GNN architectures for temporal social network analysis.
VulnRepairEval: An Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities
Venue: arXiv cs.SE 2025
Year: 2025

The adoption of Large Language Models (LLMs) for automated software vulnerability patching has shown promising outcomes on carefully curated evaluation sets. Nevertheless, existing datasets predominantly rely on superficial validation methods rather than exploit-based verification, leading to overestimated performance in security-sensitive applications. This paper introduces VulnRepairEval, an evaluation framework anchored in functional Proof-of-Concept (PoC) exploits. Our framework delivers a comprehensive, containerized evaluation pipeline that enables reproducible differential assessment, where repair success requires the original exploit to fail execution against the modified code. The benchmark construction involved extensive data curation: we processed over 400 CVEs and approximately 2,500 potential sources to extract a collection of authentic vulnerability instances (23 Python CVEs) amenable to automated testing with working PoCs. Through VulnRepairEval, we conduct a comprehensive evaluation of 12 popular LLMs and observe a significant performance deficit: even the top-performing model successfully addresses merely 5/23 instances (about 21.7%), exposing critical weaknesses in security-focused applications. Our failure analysis reveals that most unsuccessful attempts stem from imprecise vulnerability identification and patches containing syntactic or semantic errors. Enhanced prompting strategies and multi-agent approaches yield minimal improvements, with overall effectiveness remaining largely unaffected. This work contributes a stringent, practical evaluation framework for LLM-driven vulnerability remediation and underscores the necessity for assessment protocols that authentically reflect real-world exploitation scenarios.