OpenRaiser/Intern-Atlas huggingface dataset:
On the Effectiveness of Mempool-based Transaction Auditing
Venue: arXiv cs.CR 2026
Year: 2026

While the literature features a number of proposals to defend against transaction manipulation attacks, existing proposals are still not integrated within large blockchains, such as Bitcoin, Ethereum, and Cardano. Instead, the user community opted to rely on more practical but ad-hoc solutions (such as Mempool.space) that aim at detecting censorship and transaction displacement attacks by auditing discrepancies in the mempools of so-called observers. In this paper, we precisely analyze, for the first time, the interplay between mempool auditing and the ability to detect censorship and transaction displacement attacks by malicious miners in Bitcoin and Ethereum. Our analysis shows that mempool auditing can result in mis-accusations against miners with a probability larger than 25% in some settings. On a positive note, however, we show that mempool auditing schemes can successfully audit the execution of any two transactions (with an overwhelming probability of 99.9%) if they are consistently received by all observers and sent at least 30 seconds apart from each other. As a direct consequence, our findings show, for the first time, that batch-order fair-ordering schemes can offer only strong fairness guarantees for a limited subset of transactions in real-world deployments.
Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
Venue: arXiv cs.LG 2026
Year: 2026

Reinforcement learning (RL) has shown promising results in active flow control (AFC), yet progress in the field remains difficult to assess as existing studies rely on heterogeneous observation and actuation schemes, numerical setups, and evaluation protocols. Current AFC benchmarks attempt to address these issues but heavily rely on external computational fluid dynamics (CFD) solvers, are not fully differentiable, and provide limited 3D and multi-agent support. To overcome these limitations, we introduce FluidGym, the first standalone, fully differentiable benchmark suite for RL in AFC. Built entirely in PyTorch on top of the GPU-accelerated PICT solver, FluidGym runs in a single Python stack, requires no external CFD software, and provides standardized evaluation protocols. We present baseline results with PPO and SAC and release all environments, datasets, and trained models as public resources. FluidGym enables systematic comparison of control methods, establishes a scalable foundation for future research in learning-based flow control, and is available at https://github.com/safe-autonomous-systems/fluidgym.
Mixture-of-Experts Models in Vision: Routing, Optimization, and Generalization
Venue: arXiv cs.LG 2026
Year: 2026

Mixture-of-Experts (MoE) architectures enable conditional computation by routing inputs to multiple expert subnetworks and are often motivated as a mechanism for scaling large language models. In this project, we instead study MoE behavior in an image classification setting, focusing on predictive performance, expert utilization, and generalization. We compare dense, SoftMoE, and SparseMoE classifier heads on the CIFAR10 dataset under comparable model capacity. Both MoE variants achieve slightly higher validation accuracy than the dense baseline while maintaining balanced expert utilization through regularization, avoiding expert collapse. To analyze generalization, we compute Hessian-based sharpness metrics at convergence, including the largest eigenvalue and trace of the loss Hessian, evaluated on both training and test data. We find that SoftMoE exhibits higher sharpness by these metrics, while Dense and SparseMoE lie in a similar curvature regime, despite all models achieving comparable generalization performance. Complementary loss surface perturbation analyses reveal qualitative differences in non-local behavior under finite parameter perturbations between dense and MoE models, which help contextualize curvature-based measurements without directly explaining validation accuracy. We further evaluate empirical inference efficiency and show that naively implemented conditional routing does not yield inference speedups on modern hardware at this scale, highlighting the gap between theoretical and realized efficiency in sparse MoE models.
Emergent, not Immanent: A Baradian Reading of Explainable AI
Venue: arXiv cs.AI 2026
Year: 2026

Explainable AI (XAI) is frequently positioned as a technical problem of revealing the inner workings of an AI model. This position is affected by unexamined onto-epistemological assumptions: meaning is treated as immanent to the model, the explainer is positioned outside the system, and a causal structure is presumed recoverable through computational techniques. In this paper, we draw on Barad's agential realism to develop an alternative onto-epistemology of XAI. We propose that interpretations are material-discursive performances that emerge from situated entanglements of the AI model with humans, context, and the interpretative apparatus. To develop this position, we read a comprehensive set of XAI methods through agential realism and reveal the assumptions and limitations that underpin several of these methods. We then articulate the framework's ethical dimension and propose design directions for XAI interfaces that support emergent interpretation, using a speculative text-to-music interface as a case study.
A Curriculum-Based Deep Reinforcement Learning Framework for the Electric Vehicle Routing Problem
Venue: arXiv cs.LG 2026
Year: 2026

The electric vehicle routing problem with time windows (EVRPTW) is a complex optimization problem in sustainable logistics, where routing decisions must minimize total travel distance, fleet size, and battery usage while satisfying strict customer time constraints. Although deep reinforcement learning (DRL) has shown great potential as an alternative to classical heuristics and exact solvers, existing DRL models often struggle to maintain training stability-failing to converge or generalize when constraints are dense. In this study, we propose a curriculum-based deep reinforcement learning (CB-DRL) framework designed to resolve this instability. The framework utilizes a structured three-phase curriculum that gradually increases problem complexity: the agent first learns distance and fleet optimization (Phase A), then battery management (Phase B), and finally the full EVRPTW (Phase C). To ensure stable learning across phases, the framework employs a modified proximal policy optimization algorithm with phase-specific hyperparameters, value and advantage clipping, and adaptive learning-rate scheduling. The policy network is built upon a heterogeneous graph attention encoder enhanced by global-local attention and feature-wise linear modulation. This specialized architecture explicitly captures the distinct properties of depots, customers, and charging stations. Trained exclusively on small instances with N=10 customers, the model demonstrates robust generalization to unseen instances ranging from N=5 to N=100, significantly outperforming standard baselines on medium-scale problems. Experimental results confirm that this curriculum-guided approach achieves high feasibility rates and competitive solution quality on out-of-distribution instances where standard DRL baselines fail, effectively bridging the gap between neural speed and operational reliability.
Systematic Evaluation of Hip Exoskeleton Assistance Parameters for Enhancing Gait Stability During Ground Slip Perturbations
Venue: arXiv cs.RO 2026
Year: 2026

Falls are the leading cause of injury related hospitalization and mortality among older adults. Consequently, mitigating age-related declines in gait stability and reducing fall risk during walking is a critical goal for assistive devices. Lower-limb exoskeletons have the potential to support users in maintaining stability during walking. However, most exoskeleton controllers are optimized to reduce the energetic cost of walking rather than to improve stability. While some studies report stability benefits with assistance, the effects of specific parameters, such as assistance magnitude and duration, remain unexplored. To address this gap, we systematically modulated the magnitude and duration of torque provided by a bilateral hip exoskeleton during slip perturbations in eight healthy adults, quantifying stability using whole-body angular momentum (WBAM). WBAM responses were governed by a significant interaction between assistance magnitude and duration, with duration determining whether exoskeleton assistance was stabilizing or destabilizing relative to not wearing the exoskeleton device. Compared to an existing energy-optimized controller, experimentally identified stability-optimal parameters reduced WBAM range by 25.7% on average. Notably, substantial inter-subject variability was observed in the parameter combinations that minimized WBAM during perturbations. We found that optimizing exoskeleton assistance for energetic outcomes alone is insufficient for improving reactive stability during gait perturbations. Stability-focused exoskeleton control should prioritize temporal assistance parameters and include user-specific personalization. This study represents an important step toward personalized, stability-focused exoskeleton control, with direct implications for improving stability and reducing fall risk in older adults.
Turning Citation Networks Inside Out: Studying Science Using Content-Based Knowledge Graphs from LLM-Derived Taxonomies
Venue: arXiv cs.SI 2026
Year: 2026

Scientific fields are often mapped using citations and metadata, despite knowledge being transmitted primarily through content. We introduce an 'inside-out' approach that reconstructs field structure directly from text by representing each paper as a small set of interpretable knowledge components. Using a large language model to induce domain-specific taxonomies and label papers, each publication is encoded as a triplet of measure, data type, and research-question type. These triplets define a knowledge graph with edges weighted by shared papers. Applied to 617 studies on intergenerational wealth mobility, the graph reveals a stable methodological backbone centered on regression-based mobility measures, alongside substantial temporal variation in component recombination. We further utilize normalized betweenness-to-connectivity ratios to identify components and pairings that act as structural bridges disproportionate to their prevalence. This content-derived, taxonomy-driven mapping complements citation-based approaches by exposing the evolving architecture of methods, data, and questions that define a field.
Enhancing Few-Shot Out-of-Distribution Detection via the Refinement of Foreground and Background
Venue: arXiv cs.CV 2026
Year: 2026

CLIP-based foreground-background (FG-BG) decomposition methods have demonstrated remarkable effectiveness in improving few-shot out-of-distribution (OOD) detection performance. However, existing approaches still suffer from several limitations. For background regions obtained from decomposition, existing methods adopt a uniform suppression strategy for all patches, overlooking the varying contributions of different patches to the prediction. For foreground regions, existing methods fail to adequately consider that some local patches may exhibit appearance or semantic similarity to other classes, which may mislead the training process. To address these issues, we propose a new plug-and-play framework. This framework consists of three core components: (1) a Foreground-Background Decomposition module, which follows previous FG-BG methods to separate an image into foreground and background regions; (2) an Adaptive Background Suppression module, which adaptively weights patch classification entropy; and (3) a Confusable Foreground Rectification module, which identifies and rectifies confusable foreground patches. Extensive experimental results demonstrate that the proposed plug-and-play framework significantly improves the performance of existing FG-BG decomposition methods. Code is available at: https://github.com/lounwb/FoBoR.
LoRAP: Low-Rank Aggregation Prompting for Quantized Graph Neural Networks Training
Venue: arXiv cs.LG 2026
Year: 2026

Graph Neural Networks (GNNs) are neural networks that aim to process graph data, capturing the relationships and interactions between nodes using the message-passing mechanism. GNN quantization has emerged as a promising approach for reducing model size and accelerating inference in resource-constrained environments. Compared to quantization in LLMs, quantizing graph features is more emphasized in GNNs. Inspired by the above, we propose to leverage prompt learning, which manipulates the input data, to improve the performance of quantization-aware training (QAT) for GNNs. To mitigate the issue that prompting the node features alone can only make part of the quantized aggregation result optimal, we introduce Low-Rank Aggregation Prompting (LoRAP), which injects lightweight, input-dependent prompts into each aggregated feature to optimize the results of quantized aggregations. Extensive evaluations on 4 leading QAT frameworks over 9 graph datasets demonstrate that LoRAP consistently enhances the performance of low-bit quantized GNNs while introducing a minimal computational overhead.
DeLog: An Efficient Log Compression Framework with Pattern Signature Synthesis
Venue: arXiv cs.SE 2026
Year: 2026

Parser-based log compression, which separates static templates from dynamic variables, is a promising approach to exploit the unique structure of log data. However, its performance on complex production logs is often unsatisfactory. This performance gap coincides with a known degradation in the accuracy of its core log parsing component on such data, motivating our investigation into a foundational yet unverified question: does higher parsing accuracy necessarily lead to better compression ratio? To answer this, we conduct the first empirical study quantifying this relationship and find that a higher parsing accuracy does not guarantee a better compression ratio. Instead, our findings reveal that compression ratio is dictated by achieving effective pattern-based grouping and encoding, i.e., the partitioning of tokens into low entropy, highly compressible groups. Guided by this insight, we design DeLog, a novel log compressor that implements a Pattern Signature Synthesis mechanism to achieve efficient pattern-based grouping. On 16 public and 10 production datasets, DeLog achieves state-of-the-art compression ratio and speed.
Circadian Modulation of Semantic Exploration in Social Media Language
Venue: arXiv cs.CL 2026
Year: 2026

Human cognition exhibits strong circadian modulation, yet its influence on high-dimensional semantic behavior remains poorly understood. Using large-scale Reddit data, we quantify time-of-day variation in language use by embedding text into a pretrained transformer model and measuring semantic entropy as an index of linguistic exploration-exploitation, for which we show a robust circadian rhythmicity that could be entrained by seasonal light cues. Distinguishing between local and global semantic entropy reveals a systematic temporal dissociation: local semantic exploration peaks in the morning, reflecting broader exploration of semantic space, whereas global semantic diversity peaks later in the day as submissions accumulate around already established topics, consistent with "rich-get-richer" dynamics. These patterns are not explained by sentiment or affective valence, indicating that semantic exploration captures a cognitive dimension distinct from mood. The observed temporal structure aligns with known diurnal patterns in neuromodulatory systems, suggesting that biological circadian rhythms extend to the semantic domain.
Three-dimensional visualization of X-ray micro-CT with large-scale datasets: Efficiency and accuracy for real-time interaction
Venue: arXiv cs.CV 2026
Year: 2026

As Micro-CT technology continues to refine its characterization of material microstructures, industrial CT ultra-precision inspection is generating increasingly large datasets, necessitating solutions to the trade-off between accuracy and efficiency in the 3D characterization of defects during ultra-precise detection. This article provides a unique perspective on recent advances in accurate and efficient 3D visualization using Micro-CT, tracing its evolution from medical imaging to industrial non-destructive testing (NDT). Among the numerous CT reconstruction and volume rendering methods, this article selectively reviews and analyzes approaches that balance accuracy and efficiency, offering a comprehensive analysis to help researchers quickly grasp highly efficient and accurate 3D reconstruction methods for microscopic features. By comparing the principles of computed tomography with advancements in microstructural technology, this article examines the evolution of CT reconstruction algorithms from analytical methods to deep learning techniques, as well as improvements in volume rendering algorithms, acceleration, and data reduction. Additionally, it explores advanced lighting models for high-accuracy, photorealistic, and efficient volume rendering. Furthermore, this article envisions potential directions in CT reconstruction and volume rendering. It aims to guide future research in quickly selecting efficient and precise methods and developing new ideas and approaches for real-time online monitoring of internal material defects through virtual-physical interaction, for applying digital twin model to structural health monitoring (SHM).
Field-Space Autoencoder for Scalable Climate Emulators
Venue: arXiv cs.LG 2026
Year: 2026

Kilometer-scale Earth system models are essential for capturing local climate change. However, these models are computationally expensive and produce petabyte-scale outputs, which limits their utility for applications such as probabilistic risk assessment. Here, we present the Field-Space Autoencoder, a scalable climate emulation framework based on a spherical compression model that overcomes these challenges. By utilizing Field-Space Attention, the model efficiently operates on native climate model output and therefore avoids geometric distortions caused by forcing spherical data onto Euclidean grids. This approach preserves physical structures significantly better than convolutional baselines. By producing a structured compressed field, it serves as a good baseline for downstream generative emulation. In addition, the model can perform zero-shot super-resolution that maps low-resolution large ensembles and scarce high-resolution data into a shared representation. We train a generative diffusion model on these compressed fields. The model can simultaneously learn internal variability from abundant low-resolution data and fine-scale physics from sparse high-resolution data. Our work bridges the gap between the high volume of low-resolution ensemble statistics and the scarcity of high-resolution physical detail.
Economic feasibility of virtual operators in 5G via network slicing
Venue: arXiv cs.NI 2026
Year: 2026

The provision of services by more than one operator over a common network infrastructure, as enabled by 5G network slicing, is analyzed. Two business models to be implemented by a network operator, who owns the network, and a virtual operator, who does not, are proposed. In one business model, named \emph{strategic}, the network operator provides service to its user base and the virtual operator provides service to its user base and pays a per-subscriber fee to the network operator. In the other business model, named \emph{monopolistic}, the network operator provides service to both user bases. The two proposals are analyzed by means of a model that captures both system and economic features. As regards the systems features, the slicing of the network is modeled by means of a Discriminatory Processor Sharing queue. As regards the economic features, the incentives are modeled by means of the user utilities and the operators' revenues; and game theory is used to model the strategic interaction between the users' subscription decision and the operators' pricing decision. In both business models, it is shown that the network operator can be provided with the appropriate economic incentives so that it acquiesces in serving the virtual operator's user base (monopolistic model) and in allowing the virtual operator to provide service over the network operator's infrastructure (strategic model). From the point of view of the users, the strategic model results in a higher subscription rate than the monopolistic model.
An Agentic Operationalization of DISARM for FIMI Investigation on Social Media
Venue: arXiv cs.SI 2026
Year: 2026

The interoperability of data and intelligence across allied partners and their respective end-user groups is considered a foundational enabler of the collective defense capability -- both conventional and hybrid -- of NATO countries. Foreign Information Manipulation and Interference (FIMI) and related hybrid activities are conducted across various societal dimensions and infospheres, posing an ever greater challenge to threat characterization, sustained situational awareness, and response coordination. Recent advances in AI have further reduced the cost of AI-augmented trolling and interference activities, such as through the generation and amplification of manipulative content. Despite the introduction of the DISARM framework as a standardized metadata and analytical framework for FIMI, operationalizing it at the scale of social media remains a challenge. We propose a framework-agnostic, agent-based operationalization of DISARM to investigate FIMI on social media. We develop an agent coordination pipeline in which specialized agentic AI components collaboratively (1) detect candidate manipulative behaviors and (2) map these behaviors onto standard DISARM taxonomies in a transparent manner. We evaluate the approach on two real-world datasets annotated by domain practitioners. Our results show that the approach is effective in scaling the predominantly manual and heavily interpretive work of FIMI analysis -- including uncovering more than 30 previously undetected Russian bot accounts during manual analysis -- and provides a direct contribution to enhancing situational awareness and data interoperability in the context of operating in media- and information-rich settings.
Pb4U-GNet: Resolution-Adaptive Garment Simulation via Propagation-before-Update Graph Network
Venue: arXiv cs.CV 2026
Year: 2026

Garment simulation is fundamental to various applications in computer vision and graphics, from virtual try-on to digital human modelling. However, conventional physics-based methods remain computationally expensive, hindering their application in time-sensitive scenarios. While graph neural networks (GNNs) offer promising acceleration, existing approaches exhibit poor cross-resolution generalisation, demonstrating significant performance degradation on higher-resolution meshes beyond the training distribution. This stems from two key factors: (1) existing GNNs employ fixed message-passing depth that fails to adapt information aggregation to mesh density variation, and (2) vertex-wise displacement magnitudes are inherently resolution-dependent in garment simulation. To address these issues, we introduce Propagation-before-Update Graph Network (Pb4U-GNet), a resolution-adaptive framework that decouples message propagation from feature updates. Pb4U-GNet incorporates two key mechanisms: (1) dynamic propagation depth control, adjusting message-passing iterations based on mesh resolution, and (2) geometry-aware update scaling, which scales predictions according to local mesh characteristics. Extensive experiments show that even trained solely on low-resolution meshes, Pb4U-GNet exhibits strong generalisability across diverse mesh resolutions, addressing a fundamental challenge in neural garment simulation.