OpenRaiser/Intern-Atlas huggingface dataset:
Integrators at War: Mediating in AI-assisted Resort-to-Force Decisions
Venue: arXiv cs.CY 2025
Year: 2025

The integration of AI systems into the military domain is changing the way war-related decisions are made. It binds together three disparate groups of actors - developers, integrators, users - and creates a relationship between these groups and the machine, embedded in the (pre-)existing organisational and system structures. In this article, we focus on the important, but often neglected, group of integrators within such a sociotechnical system. In complex human-machine configurations, integrators carry responsibility for linking the disparate groups of developers and users in the political and military system. To act as the mediating group requires a deep understanding of the other groups' activities, perspectives and norms. We thus ask which challenges and shortcomings emerge from integrating AI systems into resort-to-force (RTF) decision-making processes, and how to address them. To answer this, we proceed in three steps. First, we conceptualise the relationship between different groups of actors and AI systems as a sociotechnical system. Second, we identify challenges within such systems for human-machine teaming in RTF decisions. We focus on challenges that arise a) from the technology itself, b) from the integrators' role in the sociotechnical system, c) from the human-machine interaction. Third, we provide policy recommendations to address these shortcomings when integrating AI systems into RTF decision-making structures.
On Optimizing Locality of Graph Transposition on Modern Architectures
Venue: arXiv cs.DC 2025
Year: 2025

This paper investigates the shared-memory Graph Transposition (GT) problem, a fundamental graph algorithm that is widely used in graph analytics and scientific computing. Previous GT algorithms have significant memory requirements that are proportional to the number of vertices and threads which obstructs their use on large graphs. Moreover, atomic memory operations have become comparably fast on recent CPU architectures, which creates new opportunities for improving the performance of concurrent atomic accesses in GT. We design PoTra, a GT algorithm which leverages graph structure and processor and memory architecture to optimize locality and performance. PoTra limits the size of additional data structures close to CPU cache sizes and utilizes the skewed degree distribution of graph datasets to optimize locality and performance. We present the performance model of PoTra to explain the connection between cache and memory response times and graph locality. Our evaluation of PoTra on three CPU architectures and 20 real-world and synthetic graph datasets with up to 128 billion edges demonstrates that PoTra achieves up to 8.7 times speedup compared to previous works and if there is a performance loss it remains limited to 15.7%, on average.
MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis
Venue: arXiv cs.CV 2025
Year: 2025

As deep learning models gain attraction in medical data, ensuring transparent and trustworthy decision-making is essential. In skin cancer diagnosis, while advancements in lesion detection and classification have improved accuracy, the black-box nature of these methods poses challenges in understanding their decision processes, leading to trust issues among physicians. This study leverages the CLIP (Contrastive Language-Image Pretraining) model, trained on different skin lesion datasets, to capture meaningful relationships between visual features and diagnostic criteria terms. To further enhance transparency, we propose a method called MedGrad E-CLIP, which builds on gradient-based E-CLIP by incorporating a weighted entropy mechanism designed for complex medical imaging like skin lesions. This approach highlights critical image regions linked to specific diagnostic descriptions. The developed integrated pipeline not only classifies skin lesions by matching corresponding descriptions but also adds an essential layer of explainability developed especially for medical data. By visually explaining how different features in an image relates to diagnostic criteria, this approach demonstrates the potential of advanced vision-language models in medical image analysis, ultimately improving transparency, robustness, and trust in AI-driven diagnostic systems.
Introduction to the Usage of Open Data from the Large Hadron Collider for Computer Scientists in the Context of Machine Learning
Venue: arXiv cs.LG 2025
Year: 2025

Deep learning techniques have evolved rapidly in recent years, significantly impacting various scientific fields, including experimental particle physics. To effectively leverage the latest developments in computer science for particle physics, a strengthened collaboration between computer scientists and physicists is essential. As all machine learning techniques depend on the availability and comprehensibility of extensive data, clear data descriptions and commonly used data formats are prerequisites for successful collaboration. In this study, we converted open data from the Large Hadron Collider, recorded in the ROOT data format commonly used in high-energy physics, to pandas DataFrames, a well-known format in computer science. Additionally, we provide a brief introduction to the data's content and interpretation. This paper aims to serve as a starting point for future interdisciplinary collaborations between computer scientists and physicists, fostering closer ties and facilitating efficient knowledge exchange.
Games! What are they good for? The Struggle of Serious Game Adoption for Rehabilitation
Venue: arXiv cs.HC 2025
Year: 2025

The field of serious games for health has grown significantly, demonstrating effectiveness in various clinical contexts such as stroke, spinal cord injury, and degenerative neurological diseases. Despite their potential benefits, therapists face barriers to adopting serious games in rehabilitation, including limited training and game literacy, concerns about cost and equipment availability, and a lack of evidence-based research on game effectiveness. Serious games for rehabilitation often involve repetitive exercises, which can be tedious and reduce motivation for continued rehabilitation, treating clients as passive recipients of clinical outcomes rather than players. This study identifies gaps and provides essential insights for advancing serious games in rehabilitation, aiming to enhance their engagement for clients and effectiveness as a therapeutic tool. Addressing these challenges requires a paradigm shift towards developing and co-creating serious games for rehabilitation with therapists, researchers, and stakeholders. Furthermore, future research is crucial to advance the development of serious games, ensuring they adhere to evidence-based principles and engage both clients and therapists. This endeavor will identify gaps in the field, inspire new directions, and support the creation of practical guidelines for serious games research.
Risk-Averse Finetuning of Large Language Models
Venue: arXiv cs.AI 2025
Year: 2025

We consider the challenge of mitigating the generation of negative or toxic content by the Large Language Models (LLMs) in response to certain prompts. We propose integrating risk-averse principles into LLM fine-tuning to minimize the occurrence of harmful outputs, particularly rare but significant events. By optimizing the risk measure of Conditional Value at Risk (CVaR), our methodology trains LLMs to exhibit superior performance in avoiding toxic outputs while maintaining effectiveness in generative tasks. Empirical evaluations on sentiment modification and toxicity mitigation tasks demonstrate the efficacy of risk-averse reinforcement learning with human feedback (RLHF) in promoting a safer and more constructive online discourse environment.
Filtering out mislabeled training instances using black-box optimization and quantum annealing
Venue: arXiv cs.LG 2025
Year: 2025

This study proposes an approach for removing mislabeled instances from contaminated training datasets by combining surrogate model-based black-box optimization (BBO) with postprocessing and quantum annealing. Mislabeled training instances, a common issue in real-world datasets, often degrade model generalization, necessitating robust and efficient noise-removal strategies. The proposed method evaluates filtered training subsets based on validation loss, iteratively refines loss estimates through surrogate model-based BBO with postprocessing, and leverages quantum annealing to efficiently sample diverse training subsets with low validation error. Experiments on a noisy majority bit task demonstrate the method's ability to prioritize the removal of high-risk mislabeled instances. Integrating D-Wave's clique sampler running on a physical quantum annealer achieves faster optimization and higher-quality training subsets compared to OpenJij's simulated quantum annealing sampler or Neal's simulated annealing sampler, offering a scalable framework for enhancing dataset quality. This work highlights the effectiveness of the proposed method for supervised learning tasks, with future directions including its application to unsupervised learning, real-world datasets, and large-scale implementations.
Monolithic 3D FPGAs Utilizing Back-End-of-Line Configuration Memories
Venue: arXiv cs.ET 2025
Year: 2025

This work presents a novel monolithic 3D (M3D) FPGA architecture that leverages stackable back-end-of-line (BEOL) transistors to implement configuration memory and pass gates, significantly improving area, latency, and power efficiency. By integrating n-type (W-doped In_2O_3) and p-type (SnO) amorphous oxide semiconductor (AOS) transistors in the BEOL, Si SRAM configuration bits are substituted with a less leaky equivalent that can be programmed at logic-compatible voltages. BEOL-compatible AOS transistors are currently under extensive research and development in the device community, with investment by leading foundries, from which reported data is used to develop robust physics-based models in TCAD that enable circuit design. The use of AOS pass gates reduces the overhead of reconfigurable circuits by mapping FPGA switch block (SB) and connection block (CB) matrices above configurable logic blocks (CLBs), thereby increasing the proximity of logic elements and reducing latency. By interfacing with the latest Verilog-to-Routing (VTR) suite, an AOS-based M3D FPGA design implemented in 7 nm technology is demonstrated with 3.4x lower area-time squared product (AT^2), 27% lower critical path latency, and 26% lower reconfigurable routing block power on benchmarks including hyperdimensional computing and large language models (LLMs).
Learning Implicit Social Navigation Behavior using Deep Inverse Reinforcement Learning
Venue: arXiv cs.RO 2025
Year: 2025

This paper reports on learning a reward map for social navigation in dynamic environments where the robot can reason about its path at any time, given agents' trajectories and scene geometry. Humans navigating in dense and dynamic indoor environments often work with several implied social rules. A rule-based approach fails to model all possible interactions between humans, robots, and scenes. We propose a novel Smooth Maximum Entropy Deep Inverse Reinforcement Learning (S-MEDIRL) algorithm that can extrapolate beyond expert demos to better encode scene navigability from few-shot demonstrations. The agent learns to predict the cost maps reasoning on trajectory data and scene geometry. The agent samples a trajectory that is then executed using a local crowd navigation controller. We present results in a photo-realistic simulation environment, with a robot and a human navigating a narrow crossing scenario. The robot implicitly learns to exhibit social behaviors such as yielding to oncoming traffic and avoiding deadlocks. We compare the proposed approach to the popular model-based crowd navigation algorithm ORCA and a rule-based agent that exhibits yielding.
Algorithmical Aspects of Some Bio Inspired Operations
Venue: arXiv cs.DS 2025
Year: 2025

This thesis investigates three biologically inspired operations: prefix-suffix duplication, bounded prefix-suffix duplication, and prefix-suffix-square completion. Duplication, a common genetic mutation, involves repeating DNA sequences and is modeled here as formal operations on words. The prefix-suffix duplication generates non-context-free languages, even from simple initial words. To better reflect biological processes, we propose a bounded variant that limits duplication length, resolving unsolved problems and aligning with biochemical realities. We also introduce the prefix-suffix-square completion operation, which generates squares at sequence ends. This operation enables the generation of infinite words such as Fibonacci, Period-doubling, and Thue-Morse, which contain squares but avoid higher exponent repetitions, highlighting unique structural properties. In contrast, prefix-suffix duplication cannot generate certain infinite words, such as Thue-Morse, but can produce cube-free words. Additionally, we address the detection of gapped repeats and palindromes-structures important in DNA and RNA analysis. These involve repeating or reversed factors flanking a central gap. Previous studies imposed constraints on gap length or arm-gap relationships; we extend this by solving the problem in three novel settings. This work advances theoretical insights into biologically inspired operations and their computational applications in genetic modeling.
Patent Novelty Assessment Accelerating Innovation and Patent Prosecution
Venue: arXiv cs.DL 2025
Year: 2025

In the rapidly evolving landscape of technological innovation, safeguarding intellectual property rights through patents is crucial for fostering progress and stimulating research and development investments. This report introduces a ground-breaking Patent Novelty Assessment and Claim Generation System, meticulously crafted to dissect the inventive aspects of intellectual property and simplify access to extensive patent claim data. Addressing a crucial gap in academic institutions, our system provides college students and researchers with an intuitive platform to navigate and grasp the intricacies of patent claims, particularly tailored for the nuances of Chinese patents. Unlike conventional analysis systems, our initiative harnesses a proprietary Chinese API to ensure unparalleled precision and relevance. The primary challenge lies in the complexity of accessing and comprehending diverse patent claims, inhibiting effective innovation upon existing ideas. Our solution aims to overcome these barriers by offering a bespoke approach that seamlessly retrieves comprehensive claim information, finely tuned to the specifics of the Chinese patent landscape. By equipping users with efficient access to comprehensive patent claim information, our transformative platform seeks to ignite informed exploration and innovation in the ever-evolving domain of intellectual property. Its envisioned impact transcends individual colleges, nurturing an environment conducive to research and development while deepening the understanding of patented concepts within the academic community.
Towards a visually interpretable analysis of Two-Phase Locking membership
Venue: arXiv cs.DB 2025
Year: 2025

Two-phase locking (2PL) is a consolidated policy commonly adopted by Database Management Systems to enforce serializability of a schedule. While the policy is well understood, both in its standard and in the strict version, automatically deriving a suitable tabular/graphical analysis of schedules with respect to 2PL is far from trivial, and requires several technicalities that do not straightforwardly translate to visual cues. In this paper, we delve into the details of the development of a tool for 2PL analysis.
Graph Contrastive Learning on Multi-label Classification for Recommendations
Venue: arXiv cs.IR 2025
Year: 2025

In business analysis, providing effective recommendations is essential for enhancing company profits. The utilization of graph-based structures, such as bipartite graphs, has gained popularity for their ability to analyze complex data relationships. Link prediction is crucial for recommending specific items to users. Traditional methods in this area often involve identifying patterns in the graph structure or using representational techniques like graph neural networks (GNNs). However, these approaches encounter difficulties as the volume of data increases. To address these challenges, we propose a model called Graph Contrastive Learning for Multi-label Classification (MCGCL). MCGCL leverages contrastive learning to enhance recommendation effectiveness. The model incorporates two training stages: a main task and a subtask. The main task is holistic user-item graph learning to capture user-item relationships. The homogeneous user-user (item-item) subgraph is constructed to capture user-user and item-item relationships in the subtask. We assessed the performance using real-world datasets from Amazon Reviews in multi-label classification tasks. Comparative experiments with state-of-the-art methods confirm the effectiveness of MCGCL, highlighting its potential for improving recommendation systems.
TFLAG:Towards Practical APT Detection via Deviation-Aware Learning on Temporal Provenance Graph
Venue: arXiv cs.CR 2025
Year: 2025

Advanced Persistent Threat (APT) have grown increasingly complex and concealed, posing formidable challenges to existing Intrusion Detection Systems in identifying and mitigating these attacks. Recent studies have incorporated graph learning techniques to extract detailed information from provenance graphs, enabling the detection of attacks with greater granularity. Nevertheless, existing studies have largely overlooked the continuous yet subtle temporal variations in the structure of provenance graphs, which may correspond to surreptitious perturbation anomalies in ongoing APT attacks. Therefore, we introduce TFLAG, an advanced anomaly detection framework that for the first time integrates the structural dynamic extraction capabilities of temporal graph model with the anomaly delineation abilities of deviation networks to pinpoint covert attack activities in provenance graphs. This self-supervised integration framework leverages the graph model to extract neighbor interaction data under continuous temporal changes from historical benign behaviors within provenance graphs, while simultaneously utilizing deviation networks to accurately distinguish authentic attack activities from false positive deviations due to unexpected subtle perturbations. The experimental results indicate that, through a comprehensive design that utilizes both attribute and temporal information, it can accurately identify the time windows associated with APT attack behaviors without prior knowledge (e.g., labeled data samples), demonstrating superior accuracy compared to current state-of-the-art methods in differentiating between attack events and system false positive events.
ViSoLex: An Open-Source Repository for Vietnamese Social Media Lexical Normalization
Venue: arXiv cs.CL 2025
Year: 2025

ViSoLex is an open-source system designed to address the unique challenges of lexical normalization for Vietnamese social media text. The platform provides two core services: Non-Standard Word (NSW) Lookup and Lexical Normalization, enabling users to retrieve standard forms of informal language and standardize text containing NSWs. ViSoLex's architecture integrates pre-trained language models and weakly supervised learning techniques to ensure accurate and efficient normalization, overcoming the scarcity of labeled data in Vietnamese. This paper details the system's design, functionality, and its applications for researchers and non-technical users. Additionally, ViSoLex offers a flexible, customizable framework that can be adapted to various datasets and research requirements. By publishing the source code, ViSoLex aims to contribute to the development of more robust Vietnamese natural language processing tools and encourage further research in lexical normalization. Future directions include expanding the system's capabilities for additional languages and improving the handling of more complex non-standard linguistic patterns.
Neural Probabilistic Circuits: Enabling Compositional and Interpretable Predictions through Logical Reasoning
Venue: arXiv cs.LG 2025
Year: 2025

End-to-end deep neural networks have achieved remarkable success across various domains but are often criticized for their lack of interpretability. While post hoc explanation methods attempt to address this issue, they often fail to accurately represent these black-box models, resulting in misleading or incomplete explanations. To overcome these challenges, we propose an inherently transparent model architecture called Neural Probabilistic Circuits (NPCs), which enable compositional and interpretable predictions through logical reasoning. In particular, an NPC consists of two modules: an attribute recognition model, which predicts probabilities for various attributes, and a task predictor built on a probabilistic circuit, which enables logical reasoning over recognized attributes to make class predictions. To train NPCs, we introduce a three-stage training algorithm comprising attribute recognition, circuit construction, and joint optimization. Moreover, we theoretically demonstrate that an NPC's error is upper-bounded by a linear combination of the errors from its modules. To further demonstrate the interpretability of NPC, we provide both the most probable explanations and the counterfactual explanations. Empirical results on four benchmark datasets show that NPCs strike a balance between interpretability and performance, achieving results competitive even with those of end-to-end black-box models while providing enhanced interpretability.
Value Compass Benchmarks: A Platform for Fundamental and Validated Evaluation of LLMs Values
Venue: arXiv cs.AI 2025
Year: 2025

As Large Language Models (LLMs) achieve remarkable breakthroughs, aligning their values with humans has become imperative for their responsible development and customized applications. However, there still lack evaluations of LLMs values that fulfill three desirable goals. (1) Value Clarification: We expect to clarify the underlying values of LLMs precisely and comprehensively, while current evaluations focus narrowly on safety risks such as bias and toxicity. (2) Evaluation Validity: Existing static, open-source benchmarks are prone to data contamination and quickly become obsolete as LLMs evolve. Additionally, these discriminative evaluations uncover LLMs' knowledge about values, rather than valid assessments of LLMs' behavioral conformity to values. (3) Value Pluralism: The pluralistic nature of human values across individuals and cultures is largely ignored in measuring LLMs value alignment. To address these challenges, we presents the Value Compass Benchmarks, with three correspondingly designed modules. It (i) grounds the evaluation on motivationally distinct \textit{basic values to clarify LLMs' underlying values from a holistic view; (ii) applies a \textit{generative evolving evaluation framework with adaptive test items for evolving LLMs and direct value recognition from behaviors in realistic scenarios; (iii) propose a metric that quantifies LLMs alignment with a specific value as a weighted sum over multiple dimensions, with weights determined by pluralistic values.