OpenRaiser/Intern-Atlas huggingface dataset:
TwinArch: A Digital Twin Reference Architecture
Venue: arXiv cs.SE 2025
Year: 2025

Background. Digital Twins (DTs) are dynamic virtual representations of physical systems, enabled by seamless, bidirectional communication between the physical and digital realms. Among the challenges impeding the widespread adoption of DTs is the absence of a universally accepted definition and a standardized DT Reference Architecture (RA). Existing state-of-the-art architectures remain largely domain-specific, primarily emphasizing aspects like modeling and simulation. Furthermore, they often combine structural and dynamic elements into unified, all-in-one diagrams, which adds to the ambiguity and confusion surrounding the concept of Digital Twins. Objective. To address these challenges, this work aims to contribute a domain-independent, multi-view Digital Twin Reference Architecture that can help practitioners in architecting and engineering their DTs. Method. We adopted the design science methodology, structured into three cycles: (i) an initial investigation conducting a Systematic Literature Review to identify key architectural elements, (ii) preliminary design refined via feedback from practitioners, and (iii) final artifact development, integrating knowledge from widely adopted DT development platforms and validated through an expert survey of 20 participants. Results. The proposed Digital Twin Reference Architecture is named TwinArch. It is documented using the Views and Beyond methodology by the Software Engineering Institute. TwinArch website and replication package: https://alessandrasomma28.github.io/twinarch/ Conclusion. TwinArch offers practitioners practical artifacts that can be utilized for designing and developing new DT systems across various domains. It enables customization and tailoring to specific use cases while also supporting the documentation of existing DT systems.
A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure
Venue: arXiv cs.AI 2025
Year: 2025

Epistemic injustice related to AI is a growing concern. In relation to machine learning models, epistemic injustice can have a diverse range of sources, ranging from epistemic opacity, the discriminatory automation of testimonial prejudice, and the distortion of human beliefs via generative AI's hallucinations to the exclusion of the global South in global AI governance, the execution of bureaucratic violence via algorithmic systems, and interactions with conversational artificial agents. Based on a proposed general taxonomy of epistemic injustice, this paper first sketches a taxonomy of the types of epistemic injustice in the context of AI, relying on the work of scholars from the fields of philosophy of technology, political philosophy and social epistemology. Secondly, an additional conceptualization on epistemic injustice in the context of AI is provided: generative hermeneutical erasure. I argue that this injustice the automation of 'epistemicide', the injustice done to epistemic agents in their capacity for collective sense-making through the suppression of difference in epistemology and conceptualization by LLMs. AI systems' 'view from nowhere' epistemically inferiorizes non-Western epistemologies and thereby contributes to the erosion of their epistemic particulars, gradually contributing to hermeneutical erasure. This work's relevance lies in proposal of a taxonomy that allows epistemic injustices to be mapped in the AI domain and the proposal of a novel form of AI-related epistemic injustice.
Formalizing Representation Theorems for a Logical Framework with Rewriting
Venue: arXiv cs.LO 2025
Year: 2025

Representation theorems for formal systems often take the form of an inductive translation that satisfies certain invariants, which are proved inductively. Theory morphisms and logical relations are common patterns of such inductive constructions. They allow representing the translation and the proofs of the invariants as a set of translation rules, corresponding to the cases of the inductions. Importantly, establishing the invariants is reduced to checking a finite set of, typically decidable, statements. Therefore, in a framework supporting theory morphisms and logical relations, translations that fit one of these patterns become much easier to formalize and to verify. The $λΠ$-calculus modulo rewriting is a logical framework designed for representing and translating between formal systems that has previously not systematically supported such patterns. In this paper, we extend it with theory morphisms and logical relations. We apply these to define and verify invariants for a number of translations between formal systems. In doing so, we identify some best practices that enable us to obtain elegant novel formalizations of some challenging translations, in particular type erasure translations from typed to untyped languages.
Just TestIt! An SBST Approach To Automate System-Integration Testing
Venue: arXiv cs.AR 2025
Year: 2025

This paper introduces TestIt, an open-source Python package designed to automate full-system integration testing using a Software-Based Self-Test (SBST) approach. By dynamically generating test vectors and golden references, TestIt significantly reduces development time and complexity while supporting both simulation and FPGA environments. Its flexible design positions TestIt as a key enabler for the widespread adoption of CI/CD methodologies in open-source RTL development. A case study on the X-HEEP RISC-V microcontroller (MCU), which integrates a custom accelerator, showcases TestIt's ability to detect hardware and software faults that traditional formal methods may overlook. Furthermore, the case study highlights how TestIt can be leveraged to characterize system performance with minimal effort. By automating testing on the PYNQ-Z2 FPGA development board, we achieved a 11x speed-up with respect to RTL simulations.
Towards a Computational Quantum Logic: An Overview of an Ongoing Research Program
Venue: arXiv cs.LO 2025
Year: 2025

This invited paper presents an overview of an ongoing research program aimed at extending the Curry-Howard-Lambek correspondence to quantum computation. We explore two key frameworks that provide both logical and computational foundations for quantum programming languages. The first framework, the Lambda-$S$ calculus, extends the lambda calculus by incorporating quantum superposition, enforcing linearity, and ensuring unitarity, to model quantum control. Its categorical semantics establishes a structured connection between classical and quantum computation through an adjunction between Cartesian closed categiries and additive symmetric monoidal closed categories. The second framework, the $\mathcal L^{\mathbb C}$ calculus, introduces a proof language for intuitionistic linear logic augmented with sum and scalar operations. This enables the formal encoding of quantum superpositions and measurements, leading to a computational model grounded in categorical structures with biproducts. These approaches suggest a fundamental duality between quantum computation and linear logic, highlighting structural correspondences between logical proofs and quantum programs. We discuss ongoing developments, including extensions to polymorphism, categorical and realizability models, as well as the integration of the modality !, which further solidify the connection between logic and quantum programming languages.
What Contributes to Affective Polarization in Networked Online Environments? Evidence from an Agent-Based Model
Venue: arXiv cs.MA 2025
Year: 2025

Affective polarization, or, inter-party hostility, is increasingly recognized as a pervasive issue in democracies worldwide, posing a threat to social cohesion. The digital media ecosystem, now widely accessible and ever-present, has often been implicated in accelerating this phenomenon. However, the precise causal mechanisms responsible for driving affective polarization have been a subject of extensive debate. While the concept of echo chambers, characterized by individuals ensconced within like-minded groups, bereft of counter-attitudinal content, has long been the prevailing hypothesis, accumulating empirical evidence suggests a more nuanced picture. This study aims to contribute to the ongoing debate by employing an agent-based model to illustrate how affective polarization is either fostered or hindered by individual news consumption and dissemination patterns based on ideological alignment. To achieve this, we parameterize three key aspects: (1) The affective asymmetry of individuals' engagement with in-party versus out-party content, (2) The proportion of in-party members within one's social neighborhood, and (3) The degree of partisan bias among the elites within the population. Subsequently, we observe macro-level changes in affective polarization within the population under various conditions stipulated by these parameters. This approach allows us to explore the intricate dynamics of affective polarization within digital environments, shedding light on the interplay between individual behaviors, social networks, and information exposure.
VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
Venue: arXiv cs.CV 2025
Year: 2025

Recently DeepSeek R1 has shown that reinforcement learning (RL) can substantially improve the reasoning capabilities of Large Language Models (LLMs) through a simple yet effective design. The core of R1 lies in its rule-based reward formulation, which leverages tasks with deterministic ground-truth answers to enable precise and stable reward computation. In the visual domain, we similarly observe that a wide range of visual understanding tasks are inherently equipped with well-defined ground-truth annotations. This property makes them naturally compatible with rule-based reward mechanisms. Motivated by this observation, we investigate the extension of R1-style reinforcement learning to Vision-Language Models (VLMs), aiming to enhance their visual reasoning capabilities. To this end, we develop VLM-R1, a dedicated framework designed to harness RL for improving VLMs' performance on general vision-language tasks. Using this framework, we further explore the feasibility of applying RL to visual domain. Experimental results indicate that the RL-based model not only delivers competitive performance on visual understanding tasks but also surpasses Supervised Fine-Tuning (SFT) in generalization ability. Furthermore, we conduct comprehensive ablation studies that uncover a series of noteworthy insights, including the presence of reward hacking in object detection, the emergence of the "OD aha moment", the impact of training data quality, and the scaling behavior of RL across different model sizes. Through these analyses, we aim to deepen the understanding of how reinforcement learning enhances the capabilities of vision-language models, and we hope our findings and open-source contributions will support continued progress in the vision-language RL community. Our code and model are available at https://github.com/om-ai-lab/VLM-R1
Joint Travel Route Optimization Framework for Platooning
Venue: arXiv cs.ET 2025
Year: 2025

Platooning represents an advanced driving technology designed to assist drivers in traffic convoys of varying lengths, enhancing road safety, reducing driver fatigue, and improving fuel efficiency. Sophisticated automated driving assistance systems have facilitated this innovation. Recent advancements in platooning emphasize cooperative mechanisms within both centralized and decentralized architectures enabled by vehicular communication technologies. This study introduces a cooperative route planning optimization framework aimed at promoting the adoption of platooning through a centralized platoon formation strategy at the system level. This approach is envisioned as a transitional phase from individual (ego) driving to fully collaborative driving. Additionally, this research formulates and incorporates travel cost metrics related to fuel consumption, driver fatigue, and travel time, considering regulatory constraints on consecutive driving durations. The performance of these cost metrics has been evaluated using Dijkstra's and A* shortest path algorithms within a network graph framework. The results indicate that the proposed architecture achieves an average cost improvement of 14 % compared to individual route planning for long road trips.
Agent That Debugs: Dynamic State-Guided Vulnerability Repair
Venue: arXiv cs.SE 2025
Year: 2025

In recent years, more vulnerabilities have been discovered every day, while manual vulnerability repair requires specialized knowledge and is time-consuming. As a result, many detected or even published vulnerabilities remain unpatched, thereby increasing the exposure of software systems to attacks. Recent advancements in agents based on Large Language Models have demonstrated their increasing capabilities in code understanding and generation, which can be promising to achieve automated vulnerability repair. However, the effectiveness of agents based on static information retrieval is still not sufficient for patch generation. To address the challenge, we propose a program repair agent called VulDebugger that fully utilizes both static and dynamic context, and it debugs programs in a manner akin to humans. The agent inspects the actual state of the program via the debugger and infers expected states via constraints that need to be satisfied. By continuously comparing the actual state with the expected state, it deeply understands the root causes of the vulnerabilities and ultimately accomplishes repairs. We experimentally evaluated VulDebugger on 50 real-life projects. With 60.00% successfully fixed, VulDebugger significantly outperforms state-of-the-art approaches for vulnerability repair.
Generative Artificial Intelligence for Internet of Things Computing: A Systematic Survey
Venue: arXiv cs.AI 2025
Year: 2025

The integration of Generative Artificial Intelligence (GenAI) within the Internet of Things (IoT) is garnering considerable interest. This growing attention stems from the continuous evolution and widespread adoption they are both having individually, enough to spontaneously reshape numerous sectors, including Healthcare, Manufacturing, and Smart Cities. Hence, their increasing popularity has catalyzed further extensive research for understanding the potential of the duo GenAI-IoT, how they interplay, and to which extent their synergy can innovate the state-of-the-art in their individual scenarios. However, despite the increasing prominence of GenAI for IoT Computing, much of the existing research remains focused on specific, narrowly scoped applications. This fragmented approach highlights the need for a more comprehensive analysis of the potential, challenges, and implications of GenAI integration within the broader IoT ecosystem. This survey exactly aims to address this gap by providing a holistic overview of the opportunities, issues, and considerations arising from the convergence of these mainstream paradigms. Our contribution is realized through a systematic literature review following the PRISMA methodology. A comparison framework is presented, and well-defined research questions are outlined to comprehensively explore the past, present, and future directions of GenAI integration with IoT Computing, offering valuable insights for both experts and newcomers.
CollEX -- A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections
Venue: arXiv cs.IR 2025
Year: 2025

In this paper, we introduce CollEx, an innovative multimodal agentic Retrieval-Augmented Generation (RAG) system designed to enhance interactive exploration of extensive scientific collections. Given the overwhelming volume and inherent complexity of scientific collections, conventional search systems often lack necessary intuitiveness and interactivity, presenting substantial barriers for learners, educators, and researchers. CollEx addresses these limitations by employing state-of-the-art Large Vision-Language Models (LVLMs) as multimodal agents accessible through an intuitive chat interface. By abstracting complex interactions via specialized agents equipped with advanced tools, CollEx facilitates curiosity-driven exploration, significantly simplifying access to diverse scientific collections and records therein. Our system integrates textual and visual modalities, supporting educational scenarios that are helpful for teachers, pupils, students, and researchers by fostering independent exploration as well as scientific excitement and curiosity. Furthermore, CollEx serves the research community by discovering interdisciplinary connections and complementing visual data. We illustrate the effectiveness of our system through a proof-of-concept application containing over 64,000 unique records across 32 collections from a local scientific collection from a public university.
Unveiling the Impact of Multimodal Features on Chinese Spelling Correction: From Analysis to Design
Venue: arXiv cs.CL 2025
Year: 2025

The Chinese Spelling Correction (CSC) task focuses on detecting and correcting spelling errors in sentences. Current research primarily explores two approaches: traditional multimodal pre-trained models and large language models (LLMs). However, LLMs face limitations in CSC, particularly over-correction, making them suboptimal for this task. While existing studies have investigated the use of phonetic and graphemic information in multimodal CSC models, effectively leveraging these features to enhance correction performance remains a challenge. To address this, we propose the Multimodal Analysis for Character Usage (\textbf{MACU}) experiment, identifying potential improvements for multimodal correctison. Based on empirical findings, we introduce \textbf{NamBert}, a novel multimodal model for Chinese spelling correction. Experiments on benchmark datasets demonstrate NamBert's superiority over SOTA methods. We also conduct a comprehensive comparison between NamBert and LLMs, systematically evaluating their strengths and limitations in CSC. Our code and model are available at https://github.com/iioSnail/NamBert.
FMNV: A Dataset of Media-Published News Videos for Fake News Detection
Venue: arXiv cs.CV 2025
Year: 2025

News media, particularly video-based platforms, have become deeply embed-ded in daily life, concurrently amplifying the risks of misinformation dissem-ination. Consequently, multimodal fake news detection has garnered signifi-cant research attention. However, existing datasets predominantly comprise user-generated videos characterized by crude editing and limited public en-gagement, whereas professionally crafted fake news videos disseminated by media outlets-often politically or virally motivated-pose substantially greater societal harm. To address this gap, we construct FMNV, a novel da-taset exclusively composed of news videos published by media organizations. Through empirical analysis of existing datasets and our curated collection, we categorize fake news videos into four distinct types. Building upon this taxonomy, we employ Large Language Models (LLMs) to automatically generate deceptive content by manipulating authentic media-published news videos. Furthermore, we propose FMNVD, a baseline model featuring a dual-stream architecture that integrates spatio-temporal motion features from a 3D ResNeXt-101 backbone and static visual semantics from CLIP. The two streams are fused via an attention-based mechanism, while co-attention modules refine the visual, textual, and audio features for effective multi-modal aggregation. Comparative experiments demonstrate both the generali-zation capability of FMNV across multiple baselines and the superior detec-tion efficacy of FMNVD. This work establishes critical benchmarks for de-tecting high-impact fake news in media ecosystems while advancing meth-odologies for cross-modal inconsistency analysis. Our dataset is available in https://github.com/DennisIW/FMNV.
Sim-to-Real Transfer in Reinforcement Learning for Maneuver Control of a Variable-Pitch MAV
Venue: arXiv cs.RO 2025
Year: 2025

Reinforcement learning (RL) algorithms can enable high-maneuverability in unmanned aerial vehicles (MAVs), but transferring them from simulation to real-world use is challenging. Variable-pitch propeller (VPP) MAVs offer greater agility, yet their complex dynamics complicate the sim-to-real transfer. This paper introduces a novel RL framework to overcome these challenges, enabling VPP MAVs to perform advanced aerial maneuvers in real-world settings. Our approach includes real-to-sim transfer techniques-such as system identification, domain randomization, and curriculum learning to create robust training simulations and a sim-to-real transfer strategy combining a cascade control system with a fast-response low-level controller for reliable deployment. Results demonstrate the effectiveness of this framework in achieving zero-shot deployment, enabling MAVs to perform complex maneuvers such as flips and wall-backtracking.
Managing Security Issues in Software Containers: From Practitioners Perspective
Venue: arXiv cs.SE 2025
Year: 2025

Software development industries are increasingly adopting containers to enhance the scalability and flexibility of software applications. Security in containerized projects is a critical challenge that can lead to data breaches and performance degradation, thereby directly affecting the reliability and operations of the container services. Despite the ongoing effort to manage the security issues in containerized projects in software engineering (SE) research, more focused investigations are needed to explore the human perspective of security management and the technical approaches to security management in containerized projects. This research aims to explore security management in containerized projects by exploring how SE practitioners perceive the security issues in containerized software projects and their approach to managing such issues. A clear understanding of security management in containerized projects will enable industries to develop robust security strategies that enhance software reliability and trust. To achieve this, we conducted two separate semi-structured interview studies to examine how practitioners approach security management. The first study focused on practitioners perceptions of security challenges in containerized environments, where we interviewed 15 participants between December 2022 and October 2023. The second study explored how to enhance container security, with 20 participants interviewed between October 2024 and December 2024. Analyzing the data from both studies reveals how SE practitioners address the various security challenges in containerized projects. Our analysis also identified the technical and non-technical enablers that can be utilized to enhance security.
Merging Embedded Topics with Optimal Transport for Online Topic Modeling on Data Streams
Venue: arXiv cs.LG 2025
Year: 2025

Topic modeling is a key component in unsupervised learning, employed to identify topics within a corpus of textual data. The rapid growth of social media generates an ever-growing volume of textual data daily, making online topic modeling methods essential for managing these data streams that continuously arrive over time. This paper introduces a novel approach to online topic modeling named StreamETM. This approach builds on the Embedded Topic Model (ETM) to handle data streams by merging models learned on consecutive partial document batches using unbalanced optimal transport. Additionally, an online change point detection algorithm is employed to identify shifts in topics over time, enabling the identification of significant changes in the dynamics of text streams. Numerical experiments on simulated and real-world data show StreamETM outperforming competitors. We provide the code publicly available at https://github.com/fgranese/StreamETM.