OpenRaiser/Intern-Atlas huggingface dataset:
ColNet: Collaborative Optimization in Decentralized Federated Multi-task Learning Systems
Venue: arXiv cs.LG 2025
Year: 2025

The integration of Federated Learning (FL) and Multi-Task Learning (MTL) has been explored to address client heterogeneity, with Federated Multi-Task Learning (FMTL) treating each client as a distinct task. However, most existing research focuses on data heterogeneity (e.g., addressing non-IID data) rather than task heterogeneity, where clients solve fundamentally different tasks. Additionally, much of the work relies on centralized settings with a server managing the federation, leaving the more challenging domain of decentralized FMTL largely unexplored. Thus, this work bridges this gap by proposing ColNet, a framework designed for heterogeneous tasks in decentralized federated environments. ColNet partitions models into a backbone and task-specific heads, and uses adaptive clustering based on model and data sensitivity to form task-coherent client groups. Backbones are averaged within groups, and group leaders perform hyper-conflict-averse cross-group aggregation. Across datasets and federations, ColNet outperforms competing schemes under label and task heterogeneity and shows robustness to poisoning attacks.
Reviewing Uses of Regulatory Compliance Monitoring
Venue: arXiv cs.CY 2025
Year: 2025

Organizations need to manage numerous business processes for delivering their services and products to customers. One important consideration thereby lies in the adherence to regulations such as laws, guidelines, or industry standards. In order to monitor adherence of their business processes to regulations -- in other words, their regulatory compliance -- organizations make use of various techniques that draw on process execution data of IT systems that support these processes. Previous research has investigated conformance checking, an operation of process mining, for the domains in which it is applied, its operationalization of regulations, the techniques being used, and the presentation of results produced. However, other techniques for regulatory compliance monitoring, which we summarize as compliance checking techniques, have not yet been investigated regarding these aspects in a structural manner. To this end, this work presents a systematic literature review on uses of regulatory compliance monitoring of business processes, thereby offering insights into the various techniques being used, their application and the results they generate. We highlight commonalities and differences between the approaches and find that various steps are performed manually; we also provide further impulses for research on compliance monitoring and its use in practice.
Autonomous Microscopy Experiments through Large Language Model Agents
Venue: arXiv cs.CY 2025
Year: 2025

Large language models (LLMs) are revolutionizing self driving laboratories (SDLs) for materials research, promising unprecedented acceleration of scientific discovery. However, current SDL implementations rely on rigid protocols that fail to capture the adaptability and intuition of expert scientists in dynamic experimental settings. We introduce Artificially Intelligent Lab Assistant (AILA), a framework automating atomic force microscopy through LLM driven agents. Further, we develop AFMBench a comprehensive evaluation suite challenging AI agents across the complete scientific workflow from experimental design to results analysis. We find that state of the art models struggle with basic tasks and coordination scenarios. Notably, Claude 3.5 sonnet performs unexpectedly poorly despite excelling in materials domain question answering (QA) benchmarks, revealing that domain specific QA proficiency does not necessarily translate to effective agentic capabilities. Additionally, we observe that LLMs can deviate from instructions, raising safety alignment concerns for SDL applications. Our ablations reveal that multi agent frameworks outperform single-agent architectures. We also observe significant prompt fragility, where slight modifications in prompt structure cause substantial performance variations in capable models like GPT 4o. Finally, we evaluate AILA's effectiveness in increasingly advanced experiments AFM calibration, feature detection, mechanical property measurement, graphene layer counting, and indenter detection. Our findings underscore the necessity for rigorous benchmarking protocols and prompt engineering strategies before deploying AI laboratory assistants in scientific research environments.
A Protocol for Compliant, Obliviously Managed Electronic Transfers
Venue: arXiv cs.CR 2025
Year: 2025

We describe a protocol for creating, updating, and transferring digital assets securely, with strong privacy and self-custody features for the initial owner based upon the earlier work of Goodell, Toliver, and Nakib. The architecture comprises three components: a mechanism to unlink counterparties in the transaction channel, a mechanism for oblivious transactions, and a mechanism to prevent service providers from equivocating. We present an approach for the implementation of these components.
Securing the AI Frontier: Urgent Ethical and Regulatory Imperatives for AI-Driven Cybersecurity
Venue: arXiv cs.CR 2025
Year: 2025

This paper critically examines the evolving ethical and regulatory challenges posed by the integration of artificial intelligence (AI) in cybersecurity. We trace the historical development of AI regulation, highlighting major milestones from theoretical discussions in the 1940s to the implementation of recent global frameworks such as the European Union AI Act. The current regulatory landscape is analyzed, emphasizing risk-based approaches, sector-specific regulations, and the tension between fostering innovation and mitigating risks. Ethical concerns such as bias, transparency, accountability, privacy, and human oversight are explored in depth, along with their implications for AI-driven cybersecurity systems. Furthermore, we propose strategies for promoting AI literacy and public engagement, essential for shaping a future regulatory framework. Our findings underscore the need for a unified, globally harmonized regulatory approach that addresses the unique risks of AI in cybersecurity. We conclude by identifying future research opportunities and recommending pathways for collaboration between policymakers, industry leaders, and researchers to ensure the responsible deployment of AI technologies in cybersecurity.
Poxel: Voxel Reconstruction for 3D Printing
Venue: arXiv cs.GR 2025
Year: 2025

Recent advancements in 3D reconstruction, especially through neural rendering approaches like Neural Radiance Fields (NeRF) and Plenoxel, have led to high-quality 3D visualizations. However, these methods are optimized for digital environments and employ view-dependent color models (RGB) and 2D splatting techniques, which do not translate well to physical 3D printing. This paper introduces "Poxel", which stands for Printable-Voxel, a voxel-based 3D reconstruction framework optimized for photopolymer jetting 3D printing, which allows for high-resolution, full-color 3D models using a CMYKWCl color model. Our framework directly outputs printable voxel grids by removing view-dependency and converting the digital RGB color space to a physical CMYKWCl color space suitable for multi-material jetting. The proposed system achieves better fidelity and quality in printed models, aligning with the requirements of physical 3D objects.
Bias in Decision-Making for AI's Ethical Dilemmas: A Comparative Study of ChatGPT and Claude
Venue: arXiv cs.CY 2025
Year: 2025

Recent advances in Large Language Models (LLMs) have enabled human-like responses across various tasks, raising questions about their ethical decision-making capabilities and potential biases. This study systematically evaluates how nine popular LLMs (both open-source and closed-source) respond to ethical dilemmas involving protected attributes. Across 50,400 trials spanning single and intersectional attribute combinations in four dilemma scenarios (protective vs. harmful), we assess models' ethical preferences, sensitivity, stability, and clustering patterns. Results reveal significant biases in protected attributes in all models, with differing preferences depending on model type and dilemma context. Notably, open-source LLMs show stronger preferences for marginalized groups and greater sensitivity in harmful scenarios, while closed-source models are more selective in protective situations and tend to favor mainstream groups. We also find that ethical behavior varies across dilemma types: LLMs maintain consistent patterns in protective scenarios but respond with more diverse and cognitively demanding decisions in harmful ones. Furthermore, models display more pronounced ethical tendencies under intersectional conditions than in single-attribute settings, suggesting that complex inputs reveal deeper biases. These findings highlight the need for multi-dimensional, context-aware evaluation of LLMs' ethical behavior and offer a systematic evaluation and approach to understanding and addressing fairness in LLM decision-making.
Journalists Knowledge and Utilisation of Google Translate Application in South East, Nigeria
Venue: arXiv cs.CY 2025
Year: 2025

This study was aimed at finding out if journalists in South East Nigeria have knowledge of Google Translate Application and also utilise it. It adopted a survey design with a sample size of 320 which was determined using Krejcie & Morgan (1970). Its objectives were to ascertain the extent journalists in South East Nigeria know about Google Translate Application, assess the utilisation of Google Translate Application among journalists in South East Nigeria, and identify the challenges affecting the journalists in South East Nigeria while using Google Translate Application. The theoretical underpin was Knowledge Attitude and Practise Model (KAP). The findings showed that journalists in South East Nigeria have knowledge of Google Translate Application but apply it mostly outside the region. It concludes that journalists in South East Nigeria have the knowledge of the App. but apply it outside the zone. The study recommends increased usage of the App. within South East Nigeria.
A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

In pursuit of reinforcement learning systems that could train in physical environments, we investigate multi-task approaches as a means to alleviate the need for massive data acquisition. In a tabular scenario where the Q-functions are collected across tasks, we model our learning problem as optimizing a higher order tensor structure. Recognizing that close-related tasks may require similar actions, our proposed method imposes a low-rank condition on this aggregated Q-tensor. The rationale behind this approach to multi-task learning is that the low-rank structure enforces the notion of similarity, without the need to explicitly prescribe which tasks are similar, but inferring this information from a reduced amount of data simultaneously with the stochastic optimization of the Q-tensor. The efficiency of our low-rank tensor approach to multi-task learning is demonstrated in two numerical experiments, first in a benchmark environment formed by a collection of inverted pendulums, and then into a practical scenario involving multiple wireless communication devices.
An Empirical Study to Understand How Students Use ChatGPT for Writing Essays
Venue: arXiv cs.HC 2025
Year: 2025

As large language models (LLMs) advance and become widespread, students increasingly turn to systems like ChatGPT for assistance with writing tasks. Educators are concerned with students' usage of ChatGPT beyond cheating; using ChatGPT may reduce their critical engagement with writing, hindering students' learning processes. The negative or positive impact of using LLM-powered tools for writing will depend on how students use them; however, how students use ChatGPT remains largely unknown, resulting in a limited understanding of its impact on learning. To better understand how students use these tools, we conducted an online study $(n=70)$ where students were given an essay-writing task using a custom platform we developed to capture the queries they made to ChatGPT. To characterize their ChatGPT usage, we categorized each of the queries students made to ChatGPT. We then analyzed the relationship between ChatGPT usage and a variety of other metrics, including students' self-perception, attitudes towards AI, and the resulting essay itself. We found that factors such as gender, race, and perceived self-efficacy can help predict different AI usage patterns. Additionally, we found that different usage patterns were associated with varying levels of enjoyment and perceived ownership over the essay. The results of this study contribute to discussions about how writing education should incorporate generative AI-powered tools in the classroom.
ColorGrid: A Multi-Agent Non-Stationary Environment for Goal Inference and Assistance
Venue: arXiv cs.AI 2025
Year: 2025

Autonomous agents' interactions with humans are increasingly focused on adapting to their changing preferences in order to improve assistance in real-world tasks. Effective agents must learn to accurately infer human goals, which are often hidden, to collaborate well. However, existing Multi-Agent Reinforcement Learning (MARL) environments lack the necessary attributes required to rigorously evaluate these agents' learning capabilities. To this end, we introduce ColorGrid, a novel MARL environment with customizable non-stationarity, asymmetry, and reward structure. We investigate the performance of Independent Proximal Policy Optimization (IPPO), a state-of-the-art (SOTA) MARL algorithm, in ColorGrid and find through extensive ablations that, particularly with simultaneous non-stationary and asymmetric goals between a ``leader'' agent representing a human and a ``follower'' assistant agent, ColorGrid is unsolved by IPPO. To support benchmarking future MARL algorithms, we release our environment code, model checkpoints, and trajectory visualizations at https://github.com/andreyrisukhin/ColorGrid.
Mutual Regression Distance
Venue: arXiv cs.LG 2025
Year: 2025

The maximum mean discrepancy and Wasserstein distance are popular distance measures between distributions and play important roles in many machine learning problems such as metric learning, generative modeling, domain adaption, and clustering. However, since they are functions of pair-wise distances between data points in two distributions, they do not exploit the potential manifold properties of data such as smoothness and hence are not effective in measuring the dissimilarity between the two distributions in the form of manifolds. In this paper, different from existing measures, we propose a novel distance called Mutual Regression Distance (MRD) induced by a constrained mutual regression problem, which can exploit the manifold property of data. We prove that MRD is a pseudometric that satisfies almost all the axioms of a metric. Since the optimization of the original MRD is costly, we provide a tight MRD and a simplified MRD, based on which a heuristic algorithm is established. We also provide kernel variants of MRDs that are more effective in handling nonlinear data. Our MRDs especially the simplified MRDs have much lower computational complexity than the Wasserstein distance. We provide theoretical guarantees, such as robustness, for MRDs. Finally, we apply MRDs to distribution clustering, generative models, and domain adaptation. The numerical results demonstrate the effectiveness and superiority of MRDs compared to the baselines.
Primary Breadth-First Development (PBFD): An Approach to Full Stack Software Development
Venue: arXiv cs.SE 2025
Year: 2025

Full stack software applications are often simplified to basic CRUD operations, which can overlook the intricate principles of computer science necessary for addressing complex development challenges. Current methodologies frequently fall short in efficiency when managing these complexities. This paper presents an innovative approach that leverages foundational computer science principles, specifically using Directed Acyclic Graphs (DAGs), to model sophisticated business problems. We introduce Breadth-First Development (BFD), Depth-First Development (DFD), Cyclic Directed Development (CDD), Directed Acyclic Development (DAD), Primary BFD (PBFD), and Primary DFD (PDFD), to enhance application development. By employing bitmaps, this approach eliminates junction tables, resulting in more compact and efficient data processing within relational databases. Rigorous testing and over eight years of production deployment for tens of thousands of users have yielded remarkable results: zero bugs, development speed improvements of up to twenty times, performance gains of seven to eight times, and storage requirements reduced to one-eleventh compared to traditional methods.
Answering Related Questions
Venue: arXiv cs.DS 2025
Year: 2025

We introduce the meta-problem Sidestep$(Π, \mathsf{dist}, d)$ for a problem $Π$, a metric $\mathsf{dist}$ over its inputs, and a map $d: \mathbb N \to \mathbb R_+ \cup \{\infty\}$. A solution to Sidestep$(Π, \mathsf{dist}, d)$ on an input $I$ of $Π$ is a pair $(J, Π(J))$ such that $\mathsf{dist}(I,J) \leqslant d(|I|)$ and $Π(J)$ is a correct answer to $Π$ on input $J$. This formalizes the notion of answering a related question (or sidestepping the question), for which we give some practical and theoretical motivations, and compare it to the neighboring concepts of smoothed analysis, planted problems, and edition problems. Informally, we call hardness radius the ``largest'' $d$ such that Sidestep$(Π, \mathsf{dist}, d)$ is NP-hard. This framework calls for establishing the hardness radius of problems $Π$ of interest for the relevant distances $\mathsf{dist}$. We exemplify it with graph problems and two distances $\mathsf{dist}_Δ$ and $\mathsf{dist}_e$ (the edge edit distance) such that $\mathsf{dist}_Δ(G,H)$ (resp. $\mathsf{dist}_e(G,H)$) is the maximum degree (resp. number of edges) of the symmetric difference of $G$ and $H$ if these graphs are on the same vertex set, and $+\infty$ otherwise. We show that the decision problems Independent Set, Clique, Vertex Cover, Coloring, Clique Cover have hardness radius $n^{\frac{1}{2}-o(1)}$ for $\mathsf{dist}_Δ$, and $n^{\frac{4}{3}-o(1)}$ for $\mathsf{dist}_e$, that Hamiltonian Cycle has hardness radius 0 for $\mathsf{dist}_Δ$, and somewhere between $n^{\frac{1}{2}-o(1)}$ and $n/3$ for $\mathsf{dist}_e$, and that Dominating Set has hardness radius $n^{1-o(1)}$ for $\mathsf{dist}_e$. We leave several open questions.
Efficient and Safe Trajectory Planning for Autonomous Agricultural Vehicle Headland Turning in Cluttered Orchard Environments
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous agricultural vehicles (AAVs), including field robots and autonomous tractors, are becoming essential in modern farming by improving efficiency and reducing labor costs. A critical task in AAV operations is headland turning between crop rows. This task is challenging in orchards with limited headland space, irregular boundaries, operational constraints, and static obstacles. While traditional trajectory planning methods work well in arable farming, they often fail in cluttered orchard environments. This letter presents a novel trajectory planner that enhances the safety and efficiency of AAV headland maneuvers, leveraging advancements in autonomous driving. Our approach includes an efficient front-end algorithm and a high-performance back-end optimization. Applied to vehicles with various implements, it outperforms state-of-the-art methods in both standard and challenging orchard fields. This work bridges agricultural and autonomous driving technologies, facilitating a broader adoption of AAVs in complex orchards.
ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning
Venue: arXiv cs.CV 2025
Year: 2025

Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have dominated the field of Computer Vision (CV). Graph Neural Networks (GNN) have performed remarkably well across diverse domains because they can represent complex relationships via unstructured graphs. However, the applicability of GNNs for visual tasks was unexplored till the introduction of Vision GNNs (ViG). Despite the success of ViGs, their performance is severely bottlenecked due to the expensive $k$-Nearest Neighbors ($k$-NN) based graph construction. Recent works addressing this bottleneck impose constraints on the flexibility of GNNs to build unstructured graphs, undermining their core advantage while introducing additional inefficiencies. To address these issues, in this paper, we propose a novel method called Dynamic Efficient Graph Convolution (DEGC) for designing efficient and globally aware ViGs. DEGC partitions the input image and constructs graphs in parallel for each partition, improving graph construction efficiency. Further, DEGC integrates local intra-graph and global inter-graph feature learning, enabling enhanced global context awareness. Using DEGC as a building block, we propose a novel CNN-GNN architecture, ClusterViG, for CV tasks. Extensive experiments indicate that ClusterViG reduces end-to-end inference latency for vision tasks by up to $5\times$ when compared against a suite of models such as ViG, ViHGNN, PVG, and GreedyViG, with a similar model parameter count. Additionally, ClusterViG reaches state-of-the-art performance on image classification, object detection, and instance segmentation tasks, demonstrating the effectiveness of the proposed globally aware learning strategy. Finally, input partitioning performed by DEGC enables ClusterViG to be trained efficiently on higher-resolution images, underscoring the scalability of our approach.
PB-NBV: Efficient Projection-Based Next-Best-View Planning Framework for Reconstruction of Unknown Objects
Venue: arXiv cs.RO 2025
Year: 2025

Completely capturing the three-dimensional (3D) data of an object is essential in industrial and robotic applications. The task of next-best-view (NBV) planning is to calculate the next optimal viewpoint based on the current data, gradually achieving a complete 3D reconstruction of the object. However, many existing NBV planning algorithms incur heavy computational costs due to the extensive use of ray-casting. Specifically, this framework refits different types of voxel clusters into ellipsoids based on the voxel structure. Then, the next optimal viewpoint is selected from the candidate views using a projection-based viewpoint quality evaluation function in conjunction with a global partitioning strategy. This process replaces extensive ray-casting, significantly improving the computational efficiency. Comparison experiments in the simulation environment show that our framework achieves the highest point cloud coverage with low computational time compared to other frameworks. The real-world experiments also confirm the efficiency and feasibility of the framework. Our method will be made open source to benefit the community.