OpenRaiser/Intern-Atlas huggingface dataset:
Artificial Intelligence Specialization in the European Union: Underexplored Role of the Periphery at NUTS-3 Level
Venue: arXiv cs.DL 2026
Year: 2026

This study examines the geographical distribution of Artificial Intelligence (AI) research production across European regions at the NUTS-3 level for the period 2015-2024. Using bibliometric data from Clarivate InCites and the Citation Topics classification system, we analyze two hierarchical levels of thematic aggregation: Electrical Engineering, Electronics & Computer Science (Macro Citation Topic 4) and Artificial Intelligence & Machine Learning (Meso Citation Topic 4.61). We calculate the Relative Specialization Index (RSI) and Relative Citation Impact (RCI) for 781 NUTS-3 regions. While major metropolitan hubs such as Paris (IIle-de-France), Warszawa, and Madrid lead in absolute production volume, our findings reveal that peripheral regions, particularly from Eastern Europe and Spain, exhibit the highest levels of relative AI specialization. Notably, we find virtually no correlation between regional specialization and citation impact, identifying four distinct regional profiles: high-impact specialized regions (e.g., Granada, Jaen, Vilniaus), high-volume but low-impact regions (e.g., Bugas, several Polish regions), high-impact non-specialized regions, with Fyn (Denmark) standing out as a remarkable outlier achieving exceptional citation impact (RCI > 4) despite low specialization, and diversified portfolios with selective excellence (e.g., German regions). These results suggest that AI research represents a strategic opportunity for peripheral regions to develop competitive scientific niches, though achieving international visibility requires more than research volume alone.
SEG-JPEG: Simple Visual Semantic Communications for Remote Operation of Automated Vehicles over Unreliable Wireless Networks
Venue: arXiv cs.RO 2026
Year: 2026

Remote Operation is touted as being key to the rapid deployment of automated vehicles. Streaming imagery to control connected vehicles remotely currently requires a reliable, high throughput network connection, which can be limited in real-world remote operation deployments relying on public network infrastructure. This paper investigates how the application of computer vision assisted semantic communication can be used to circumvent data loss and corruption associated with traditional image compression techniques. By encoding the segmentations of detected road users into colour coded highlights within low resolution greyscale imagery, the required data rate can be reduced by 50 \% compared with conventional techniques, while maintaining visual clarity. This enables a median glass-to-glass latency of below 200ms even when the network data rate is below 500kbit/s, while clearly outlining salient road users to enhance situational awareness of the remote operator. The approach is demonstrated in an area of variable 4G mobile connectivity using an automated last-mile delivery vehicle. With this technique, the results indicate that large-scale deployment of remotely operated automated vehicles could be possible even on the often constrained public 4G/5G mobile network, providing the potential to expedite the nationwide roll-out of automated vehicles.
AI Sessions for Network-Exposed AI-as-a-Service
Venue: arXiv cs.NI 2026
Year: 2026

Cloud-based Artificial Intelligence (AI) inference is increasingly latency- and context-sensitive, yet today's AI-as-a-Service is typically consumed as an application-chosen endpoint, leaving the network to provide only best-effort transport. This decoupling prevents enforceable tail-latency guarantees, compute-aware admission control, and continuity under mobility. This paper proposes Network-Exposed AI-as-a-Service (NE-AIaaS) built around a new service primitive: the AI Session (AIS)-a contractual object that binds model identity, execution placement, transport Quality-of-Service (QoS), and consent/charging scope into a single lifecycle with explicit failure semantics. We introduce the AI Service Profile (ASP), a compact contract that expresses task modality and measurable service objectives (e.g., time-to-first-response/token, p99 latency, success probability) alongside privacy and mobility constraints. On this basis, we specify protocol-grade procedures for (i) DISCOVER (model/site discovery), (ii) AI PAGING (context-aware selection of execution anchor), (iii) two-phase PREPARE/COMMIT that atomically co-reserves compute and QoS resources, and (iv) make-before-break MIGRATION for session continuity. The design is standard-mappable to Common API Framework (CAPIF) style northbound exposure, ETSI Multi-access Edge Computing (MEC) execution substrates, 5G QoS flows for transport enforcement, and Network Data Analytics Function (NWDAF) style analytics for closed-loop paging/migration triggers.
Fluoroscopy-Constrained Magnetic Robot Control via Zernike-Based Field Modeling and Nonlinear MPC
Venue: arXiv cs.RO 2026
Year: 2026

Magnetic actuation enables surgical robots to navigate complex anatomical pathways while reducing tissue trauma and improving surgical precision. However, clinical deployment is limited by the challenges of controlling such systems under fluoroscopic imaging, which provides low frame rate and noisy pose feedback. This paper presents a control framework that remains accurate and stable under such conditions by combining a nonlinear model predictive control (NMPC) framework that directly outputs coil currents, an analytically differentiable magnetic field model based on Zernike polynomials, and a Kalman filter to estimate the robot state. Experimental validation is conducted with two magnetic robots in a 3D-printed fluid workspace and a spine phantom replicating drug delivery in the epidural space. Results show the proposed control method remains highly accurate when feedback is downsampled to 3 Hz with added Gaussian noise (sigma = 2 mm), mimicking clinical fluoroscopy. In the spine phantom experiments, the proposed method successfully executed a drug delivery trajectory with a root mean square (RMS) position error of 1.18 mm while maintaining safe clearance from critical anatomical boundaries.
Semantics-Aware Denoising: A PLM-Guided Sample Reweighting Strategy for Robust Recommendation
Venue: arXiv cs.IR 2026
Year: 2026

Implicit feedback, such as user clicks, serves as the primary data source for modern recommender systems. However, click interactions inherently contain substantial noise, including accidental clicks, clickbait-induced interactions, and exploratory browsing behaviors that do not reflect genuine user preferences. Training recommendation models with such noisy positive samples leads to degraded prediction accuracy and unreliable recommendations. In this paper, we propose SAID (Semantics-Aware Implicit Denoising), a simple yet effective framework that leverages semantic consistency between user interests and item content to identify and downweight potentially noisy interactions. Our approach constructs textual user interest profiles from historical behaviors and computes semantic similarity with target item descriptions using pre-trained language model (PLM) based text encoders. The similarity scores are then transformed into sample weights that modulate the training loss, effectively reducing the impact of semantically inconsistent clicks. Unlike existing denoising methods that require complex auxiliary networks or multi-stage training procedures, SAID only modifies the loss function while keeping the backbone recommendation model unchanged. Extensive experiments on two real-world datasets demonstrate that SAID consistently improves recommendation performance, achieving up to 2.2% relative improvement in AUC over strong baselines, with particularly notable robustness under high noise conditions.
Automated Multi-Source Debugging and Natural Language Error Explanation for Dashboard Applications
Venue: arXiv cs.SE 2026
Year: 2026

Modern web dashboards and enterprise applications increasingly rely on complex, distributed microservices architectures. While these architectures offer scalability, they introduce significant challenges in debugging and observability. When failures occur, they often manifest as opaque error messages to the end-user such as Something went wrong. This masks the underlying root cause which may reside in browser side exceptions, API contract violations, or server side logic failures. Existing monitoring tools capture these events in isolation but fail to correlate them effectively or provide intelligible explanations to non technical users. This paper proposes a novel system for Automated Multi Source Debugging and Natural Language Error Explanation. The proposed framework automatically collects and correlates error data from disparate sources such as browser, API, server logs and validates API contracts in real time, and utilizes Large Language Models to generate natural language explanations. This approach significantly reduces Mean Time to Resolution for support engineers and improves the user experience by transforming cryptic error codes into actionable insights.
From PhysioNet to Foundation Models -- A history and potential futures
Venue: arXiv cs.CY 2026
Year: 2026

Over the last 35 years, the sharing of medical data and models for research has evolved from sneakernet to the internet - from mailing magnetic tapes and compact discs of a handful of well-curated recordings, to the high-speed download of relatively comprehensive hospital databases. More recently, the fervor around the potential for modern machine learning and 'AI' to catapult us into the next industrial revolution has led to a seemingly insatiable desire to pump almost any source of data into large models. Although this has great potential, it also presents a whole set of new challenges. In this article I examine these trends over the last 30 years, drawing on examples from cardiology, one of the oldest data-intensive fields that is undergoing a renaissance via machine learning. From the early days of computerized cardiology, the Research Resource for Complex Physiologic Signals (PhysioNet) has been at the cutting edge of this field. This article, therefore, includes much of the Resource's history and the contributions drawn from 25 years of firsthand experience of co-developing elements of the Resource with its founders. I identify the most promising future directions for the PhysioNet Resource, and more generally, the growing issues and opportunities around dissemination and use of massive physiological databases, associated open access code, and public competitions, along with potential solutions to the key issues facing our field. Topics range from how we should approach foundation models in the context of the rapidly growing AI carbon footprint, to the potential of Tiny-ML and edge computing. I also cover issues around prizes and incentives, funding models, and scientific repeatability, as well as how we might address these issues by leveraging the PhysioNet Challenges, consistent with the philosophy of open-access from the early days of the PhysioNet Resource.
Making Large Language Models Speak Tulu: Structured Prompting for an Extremely Low-Resource Language
Venue: arXiv cs.CL 2026
Year: 2026

Can large language models converse in languages virtually absent from their training data? We investigate this question through a case study on Tulu, a Dravidian language with over 2 million speakers but minimal digital presence. Rather than fine-tuning an LLM, we examine whether structured prompts alone can elicit basic conversational ability under controlled prompting. We systematically tackle various challenges posed by absence of training data for Tulu by combining explicit grammar documentation, negative constraints to suppress high-probability tokens from related languages, romanization standardization, and quality-controlled synthetic data generation via self-play. Evaluated on a manually curated held-out set across three LLMs (Gemini 2.0 Flash, GPT-4o, Llama 3.1 70B) and validated by native speakers, our approach reduces vocabulary contamination from 80% to 5% while achieving 85% grammatical accuracy. Cross-model analysis reveals that negative constraints provide consistent improvements (12--18 percentage points), while grammar documentation effects vary by model architecture (8--22 points).
FlashMem: Supporting Modern DNN Workloads on Mobile with GPU Memory Hierarchy Optimizations
Venue: arXiv cs.DC 2026
Year: 2026

The increasing size and complexity of modern deep neural networks (DNNs) pose significant challenges for on-device inference on mobile GPUs, with limited memory and computational resources. Existing DNN acceleration frameworks primarily deploy a weight preloading strategy, where all model parameters are loaded into memory before execution on mobile GPUs. We posit that this approach is not adequate for modern DNN workloads that comprise very large model(s) and possibly execution of several distinct models in succession. In this work, we introduce FlashMem, a memory streaming framework designed to efficiently execute large-scale modern DNNs and multi-DNN workloads while minimizing memory consumption and reducing inference latency. Instead of fully preloading weights, FlashMem statically determines model loading schedules and dynamically streams them on demand, leveraging 2.5D texture memory to minimize data transformations and improve execution efficiency. Experimental results on 11 models demonstrate that FlashMem achieves 2.0x to 8.4x memory reduction and 1.7x to 75.0x speedup compared to existing frameworks, enabling efficient execution of large-scale models and multi-DNN support on resource-constrained mobile GPUs.
Fractional-Order Federated Learning
Venue: arXiv cs.LG 2026
Year: 2026

Federated learning (FL) allows remote clients to train a global model collaboratively while protecting client privacy. Despite its privacy-preserving benefits, FL has significant drawbacks, including slow convergence, high communication cost, and non-independent-and-identically-distributed (non-IID) data. In this work, we present a novel FedAvg variation called Fractional-Order Federated Averaging (FOFedAvg), which incorporates Fractional-Order Stochastic Gradient Descent (FOSGD) to capture long-range relationships and deeper historical information. By introducing memory-aware fractional-order updates, FOFedAvg improves communication efficiency and accelerates convergence while mitigating instability caused by heterogeneous, non-IID client data. We compare FOFedAvg against a broad set of established federated optimization algorithms on benchmark datasets including MNIST, FEMNIST, CIFAR-10, CIFAR-100, EMNIST, the Cleveland heart disease dataset, Sent140, PneumoniaMNIST, and Edge-IIoTset. Across a range of non-IID partitioning schemes, FOFedAvg is competitive with, and often outperforms, these baselines in terms of test performance and convergence speed. On the theoretical side, we prove that FOFedAvg converges to a stationary point under standard smoothness and bounded-variance assumptions for fractional order $0<α\le 1$. Together, these results show that fractional-order, memory-aware updates can substantially improve the robustness and effectiveness of federated learning, offering a practical path toward distributed training on heterogeneous data.
MEV in Binance Builder
Venue: arXiv cs.CR 2026
Year: 2026

We study the builder-driven MEV arbitrage on BNB Smart Chain (BSC). BSC's Proposer--Builder Separation (PBS) adopts a leaner design: only whitelisted builders can participate, blocks are produced at shorter intervals, and private order flow bypasses the public mempool. These features have long raised community concerns over centralization, which we empirically confirm by tracing arbitrage activity of the two dominant builders from May to November 2025. Within months, 48Club and Blockrazor produced over 96\% of blocks and captured about 92\% of MEV profits. We find that profits concentrate in short, low-hop arbitrage routes over wrapped tokens and stablecoins, and that block construction rapidly converges toward monopoly. Beyond concentration alone, our analysis reveals a structural source of inequality: BSC's short block interval and whitelisted PBS collapse the contestable window for MEV competition, amplifying latency advantages and excluding slower builders and searchers. MEV extraction on BSC is not only more centralized than on Ethereum, but also structurally more vulnerable to censorship and weakened fairness.
One Agent to Guide Them All: Empowering MLLMs for Vision-and-Language Navigation via Explicit World Representation
Venue: arXiv cs.RO 2026
Year: 2026

A navigable agent needs to understand both high-level semantic instructions and precise spatial perceptions. Building navigation agents centered on Multimodal Large Language Models (MLLMs) demonstrates a promising solution due to their powerful generalization ability. However, the current tightly coupled design dramatically limits system performance. In this work, we propose a decoupled design that separates low-level spatial state estimation from high-level semantic planning. Unlike previous methods that rely on predefined, oversimplified textual maps, we introduce an interactive metric world representation that maintains rich and consistent information, allowing MLLMs to interact with and reason on it for decision-making. Furthermore, counterfactual reasoning is introduced to further elicit MLLMs' capacity, while the metric world representation ensures the physical validity of the produced actions. We conduct comprehensive experiments in both simulated and real-world environments. Our method establishes a new zero-shot state-of-the-art, achieving 48.8\% Success Rate (SR) in R2R-CE and 42.2\% in RxR-CE benchmarks. Furthermore, to validate the versatility of our metric representation, we demonstrate zero-shot sim-to-real transfer across diverse embodiments, including a wheeled TurtleBot 4 and a custom-built aerial drone. These real-world deployments verify that our decoupled framework serves as a robust, domain-invariant interface for embodied Vision-and-Language navigation.
Social Life of Code: Modeling Evolution through Code Embedding and Opinion Dynamics
Venue: arXiv cs.SE 2026
Year: 2026

Software repositories provide a detailed record of software evolution by capturing developer interactions through code-related activities such as pull requests and modifications. To better understand the underlying dynamics of codebase evolution, we introduce a novel approach that integrates semantic code embeddings with opinion dynamics theory, offering a quantitative framework to analyze collaborative development processes. Our approach begins by encoding code snippets into high-dimensional vector representations using state-of-the-art code embedding models, preserving both syntactic and semantic features. These embeddings are then processed using Principal Component Analysis (PCA) for dimensionality reduction, with data normalized to ensure comparability. We model temporal evolution using the Expressed-Private Opinion (EPO) model to derive trust matrices and track opinion trajectories across development cycles. These opinion trajectories reflect the underlying dynamics of consensus formation, influence propagation, and evolving alignment (or divergence) within developer communities -- revealing implicit collaboration patterns and knowledge-sharing mechanisms that are otherwise difficult to observe. By bridging software engineering and computational social science, our method provides a principled way to quantify software evolution, offering new insights into developer influence, consensus formation, and project sustainability. We evaluate our approach on data from three prominent open-source GitHub repositories, demonstrating its ability to reveal interpretable behavioral trends and variations in developer interactions. The results highlight the utility of our framework in improving open-source project maintenance through data-driven analysis of collaboration dynamics.
StatCounter: A Longitudinal Study of a Portable Scholarly Metric Display
Venue: arXiv cs.HC 2026
Year: 2026

This study explores a handheld, battery-operated e-ink device displaying Google Scholar citation statistics. The StatCounter places academic metrics into the flow of daily life rather than a desktop context. The work draws on a first-person, longitudinal auto-ethnographic inquiry examining how constant access to scholarly metrics influences motivation, attention, reflection, and emotional responses across work and non-work settings. The ambient proximity and pervasive availability of scholarly metrics invites frequent micro-checks, short reflective pauses, but also introduces moments of second-guessing when numbers drop or stagnate. Carrying the device prompts new narratives about academic identity, including a sense of companionship during travel and periods away from the office. Over time, the presence of the device turns metrics from an occasional reference into an ambient background of scholarly life. The study contributes insight into how situated, embodied access to academic metrics reshapes their meaning, and frames opportunities for designing tools that engage with scholarly evaluation in reflective ways.
Measuring Social Integration Through Participation: Categorizing Organizations and Leisure Activities in the Displaced Karelians Interview Archive using LLMs
Venue: arXiv cs.CL 2026
Year: 2026

Digitized historical archives make it possible to study everyday social life on a large scale, but the information extracted directly from text often does not directly allow one to answer the research questions posed by historians or sociologists in a quantitative manner. We address this problem in a large collection of Finnish World War II Karelian evacuee family interviews. Prior work extracted more than 350K mentions of leisure time activities and organizational memberships from these interviews, yielding 71K unique activity and organization names -- far too many to analyze directly. We develop a categorization framework that captures key aspects of participation (the kind of activity/organization, how social it typically is, how regularly it happens, and how physically demanding it is). We annotate a gold-standard set to allow for a reliable evaluation, and then test whether large language models can apply the same schema at scale. Using a simple voting approach across multiple model runs, we find that an open-weight LLM can closely match expert judgments. Finally, we apply the method to label the 350K entities, producing a structured resource for downstream studies of social integration and related outcomes.