2026 September - 40 articles
Agentic artificial intelligence requires safeguards that remain effective across trajectories rather than only at individual decisions. This study introduces MARIS-TRA, a hierarchical temporal runtime assurance extension of Controlled Agentic AI Syst...
The rapid growth of social media has intensified the spread of hate speech targeting individuals based on race, gender, religion, and sexual orientation, creating significant technical, social, and ethical challenges. To address this, we propose a co...
In machine learning, feature selection is a fundamental component that plays a crucial role in improving prediction accuracy and reducing the computational time of classification models. Feature selection involves eliminating irrelevant or redundant...
Certified robustness provides guarantees that a model’s prediction is stable within a specified perturbation region, but certified-radius assessment for Transformer text classifiers is computationally expensive because sentence-level certificat...
Graph-based sleep-staging models use different node representations, making it difficult to separate the contribution of the frontend from that of the graph architecture. We investigate whether a common raw-waveform encoder can improve heterogeneous...
Reinforcement-learning-based pedestrian agents can acquire adaptive behaviors without hand-crafted motion rules, but training from scratch in each environment is computationally expensive and often environment dependent. This paper investigates a two...
Process mining is a data-driven technique that acts as a bridge between data science and process management. One of its main tasks is enabling the identification of process models from event logs. However, when event logs contain sensitive data or co...
Accurate localization of the upper end vertebra (UEV), lower end vertebra (LEV), and apex vertebra (AV) is essential for automated scoliosis assessment and Cobb angle measurement. This study proposes a geometry-aware, multi-task sequence learning fra...
Predicting progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) requires models that represent both regional brain abnormalities and their evolution across repeated examinations. We developed a longitudinal graph neural...
The paper presents a novel large language model-based neighbourhood search method for production scheduling using discrete-event simulation (DES) as the modelling and evaluation engine. The combination of large language models (LLMs) and DES represen...
Medical image segmentation requires spatial transformations that remain effective across heterogeneous image statistics and boundary conditions. We introduce FunKAN, a KAN-inspired operator that acts directly on spatial feature maps: analytical Hermi...
Training under extreme class imbalance (>1:100) remains an open problem in weakly supervised learning. The standard remedy—loss-level reweighting (focal loss, asymmetric loss, class-balanced loss)—is widely adopted, yet its behavior at...
Neurosymbolic AI (NeSy AI) seeks to integrate the strengths of symbolic reasoning with computational learning methods, addressing fundamental challenges of each paradigm in isolation. Existing surveys have primarily organized this growing body of res...
Humans acquire meaningful language by storing perceptual categories, category-word mappings, and conditional IF–THEN rules in rich, cross-domain, multimodal contexts. Crucially, structured cross-domain input that pairs visual context and langua...
This study proposes an explainable artificial intelligence (XAI) engineering workflow for provider-level healthcare claim audit prioritization using SHAP-guided hybrid retrieval-augmented generation (RAG). The framework integrates provider-level clai...
Influence maximisation traditionally assumes that each activated neighbour contributes independently to the likelihood of a user adopting information, ignoring conjunctive synergies where a set of users must be active simultaneously to trigger anothe...
The Internet of Vehicles (IoV) is revolutionizing intelligent transportation systems by ubiquitous connectivity of vehicles, roadside infrastructure, pedestrians, edge/cloud platforms, and smart-city services. With the IoV evolving towards highly con...
Physics-informed anomaly detection for industrial control systems (ICSs) is usually validated on synthetic data. How much of that performance survives on real hardware-in-the-loop (HIL) data is an open question. We re-implement PhySec-Edge, a hybrid...
When an autonomous language-model agent fails, a growing body of work hands the diagnosis to a large language model (LLM) that reads the trace and names the responsible step. We ask whether this attribution separates two causes, a localized corruptio...
Large language models (LLMs) incur prohibitive computational costs when deployed as monolithic systems for multi-domain query processing. This paper proposes Accelerated LLM, a modular architecture that replaces a single general-purpose LLM with an e...
Plant diseases, being a subject of interdisciplinary research, significantly reduce crop yield, quality, and economic returns, while the misidentification of pathogens often leads to ineffective treatments and may harm beneficial organisms and ecosys...
(1) Background: Information diffusion models developed for graph-based platforms such as Reddit and broadcast architectures such as Telegram identify temporal features—particularly the timing of peak spread—as dominant predictors of cover...
The integration of Tiny Machine Learning (TinyML) into human activity recognition (HAR) represents a paradigm shift in artificial intelligence, enabling real-time, efficient, and privacy-preserving analysis on resource-constrained edge devices. This...
This paper studies hallucinations in structured extraction using prompt-based Semantic Role Labeling (SRL) as a controlled case study. We implement a DSPy-based pipeline in which each prediction includes a generated rationale and a citation to a gove...
Global attention combines long-range lexical access with semantic-relation retrieval, making whole-layer replacement lossy. We present Strata-HeadQuotient, which statically assigns each key–value (KV) head to full-history attention (GLOBAL), 10...
Laboratory experimentation is shaped by practical constraints, so computational frameworks built for offline settings do not transfer cleanly to an online laboratory routine. Two mismatches dominate. First, they assume on-demand access to high-fideli...
Physics structure-informed neural networks (
Physics-informed neural networks (PINNs) are difficult to train on strongly coupled, multi-objective systems: with fixed loss weights an otherwise identical run succeeds or diverges with the random seed, and a leading explanation attributes the failu...
Deep visual recognition can turn ordinary video into interpretable motor knowledge, yet coordination among the joints of a single body during social interaction remains largely unexplored. We present an interactive playback visualizer that couples ma...
Hyperparameter optimization (HPO) aims to identify high-quality model configurations under a limited evaluation budget. To address mixed search spaces, sparse feedback, and low sample efficiency in reinforcement learning (RL)-based HPO, a Center-Guid...
Recent studies have proposed post-processing strategies that iteratively reconstruct a partial segment of current solutions using Neural Combinatorial Optimization (NCO) solvers, thereby improving their performance on large-scale Vehicle Routing Prob...
Large ensembles of atmospheric dispersion simulations are necessary for uncertainty quantification, source attribution, and emergency response, however, physics-based models like HYSPLIT might be too computationally expensive for operational use. In...
Translating natural language into graph query languages (NL2GQL) enables non-expert users to access graph databases, but supervised parsers depend on large annotated corpora and costly retraining whenever the schema evolves. Few-shot in-context learn...
Network densification will play a vital role in next-generation cellular networks, as it addresses coverage and capacity issues in the radio access domain. However, new cell deployments can negatively impact neighbor cells, requiring careful planning...
Background: Preeclampsia remains a major cause of maternal and perinatal morbidity and mortality, and machine learning (ML) and artificial intelligence (AI) models have increasingly been proposed for risk prediction, diagnosis, monitoring, and progno...
The original ALPHA benchmark introduced a taxonomy-aware penalty for evaluating CWE-level vulnerability prediction in Python and proposed that the penalty could theoretically also serve as a training signal. This paper tests that proposal empirically...
Continual learning has made significant progress toward enabling adaptive machine learning under evolving environments, yet real-world deployment increasingly exposes systems to persistent harsh conditions, including distributional shifts, feature ev...
Clinical practice guidelines require expert synthesis that large language models (LLMs) might partly automate, yet their ability to reproduce clinically actionable recommendations is poorly quantified. We evaluate an LLM (Claude Sonnet 4.6) against t...
We investigate the potential of technology-based attention training (AT), particularly virtual reality (VR), as a stress-management tool. Mental stress is rising globally, and researchers increasingly use immersive technologies, wearable sensors, and...
Undiagnosed diabetes and prediabetes are common among trauma patients and increase perioperative complication rates, yet routine glycemic screening at hospital admission is rarely performed. We compared 10 supervised machine learning (ML) algorithms...
