--This paper introduces a novel approach to the power system security assessment using Multi-T ask Learning (MTL), and reformulating the problem as a multi-label classification task. The proposed MTL framework simultaneously assesses static, voltage, transient, and small-signal stability, improving both accuracy and interpretability with respect to the most state of the art machine learning methods. It consists of a shared encoder and multiple decoders, enabling knowledge transfer between stability tasks. Experiments on the IEEE 68-bus system demonstrate a measurable superior performance of the proposed method compared to the extant state-of-the-art approaches. The power system security assessment (PSSA) is essential power application in energy management systems [1] apparatus that ensures the reliability and stability of energy delivery [2]. Power system operators routinely perform security assessments to ensure the system can withstand disturbances, typically involving steady-state and dynamic simulations every 15 minutes to prepare contingency plans for critical scenarios [3]. In recent years, mainly due to the ongoing changing landscape in the energy mix of electricity grids around the globe, conducting real-time PSSA has become more complex to the point that many power utilities may abandon this critical function. Instead, they rely solely on static security assessment, risking blackout as a result of dynamic instabilities.

Large language models (LLMs) are rapidly pushing the limits of contemporary computing hardware. For example, training GPT-3 has been estimated to consume around 1300 MWh of electricity, and projections suggest future models may require city-scale (gigawatt) power budgets. These demands motivate exploration of computing paradigms beyond conventional von Neumann architectures. This review surveys emerging photonic hardware optimized for next-generation generative AI computing. We discuss integrated photonic neural network architectures (e.g., Mach-Zehnder interferometer meshes, lasers, wavelength-multiplexed microring resonators) that perform ultrafast matrix operations. We also examine promising alternative neuromorphic devices, including spiking neural network circuits and hybrid spintronic-photonic synapses, which combine memory and processing. The integration of two-dimensional materials (graphene, TMDCs) into silicon photonic platforms is reviewed for tunable modulators and on-chip synaptic elements. Transformer-based LLM architectures (self-attention and feed-forward layers) are analyzed in this context, identifying strategies and challenges for mapping dynamic matrix multiplications onto these novel hardware substrates. We then dissect the mechanisms of mainstream LLMs, such as ChatGPT, DeepSeek, and LLaMA, highlighting their architectural similarities and differences. We synthesize state-of-the-art components, algorithms, and integration methods, highlighting key advances and open issues in scaling such systems to mega-sized LLM models. We find that photonic computing systems could potentially surpass electronic processors by orders of magnitude in throughput and energy efficiency, but require breakthroughs in memory, especially for long-context windows and long token sequences, and in storage of ultra-large datasets.

Although prototype-based explanations provide a human-understandable way of representing model predictions they often fail to direct user attention to the most relevant features. We propose a novel approach to identify the most informative features within prototypes, termed alike parts. Using feature importance scores derived from an agnostic explanation method, it emphasizes the most relevant overlapping features between an instance and its nearest prototype. Furthermore, the feature importance score is incorporated into the objective function of the prototype selection algorithms to promote global prototypes diversity. Through experiments on six benchmark datasets, we demonstrate that the proposed approach improves user comprehension while maintaining or even increasing predictive accuracy.

Trust is a fundamental component of human-agent interaction. With the increasing presence of artificial agents in daily life, it is essential to understand how people perceive and trust these agents. One of the key challenges affecting this perception is the Uncanny Valley Effect (UVE), where increasingly human-like artificial beings can be perceived as eerie or repelling. Despite growing interest in trust and the UVE, existing research varies widely in terms of how these concepts are defined and operationalized. This inconsistency raises important questions about how and under what conditions the UVE influences trust in agents. A systematic understanding of their relationship is currently lacking. This review aims to examine the impact of the UVE on human trust in agents and to identify methodological patterns, limitations, and gaps in the existing empirical literature. Following PRISMA guidelines, a systematic search identified 53 empirical studies that investigated both UVE-related constructs and trust or trust-related outcomes. Studies were analyzed based on a structured set of categories, including types of agents and interactions, methodological and measurement approaches, and key findings. The results of our systematic review reveal that most studies rely on static images or hypothetical scenarios with limited real-time interaction, and the majority use subjective trust measures. This review offers a novel framework for classifying trust measurement approaches with regard to the best-practice criteria for empirically investigating the UVE. As the first systematic attempt to map the intersection of UVE and trust, this review contributes to a deeper understanding of their interplay and offers a foundation for future research. Keywords: the uncanny valley effect, trust, human-likeness, affinity response, human-agent interaction

Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are recognized to have significant effects on industry and business dynamics, not least because of their impact on the preconditions for entrepreneurship. There is still a lack of knowledge of GenAI as a theme in entrepreneurship research. This paper presents a systematic literature review aimed at identifying and analyzing the evolving landscape of research on the effects of GenAI on entrepreneurship. We analyze 83 peer-reviewed articles obtained from leading academic databases: Web of Science and Scopus. Using natural language processing and unsupervised machine learning techniques with TF-IDF vectorization, Principal Component Analysis (PCA), and hierarchical clustering, five major thematic clusters are identified: (1) Digital Transformation and Behavioral Models, (2) GenAI-Enhanced Education and Learning Systems, (3) Sustainable Innovation and Strategic AI Impact, (4) Business Models and Market Trends, and (5) Data-Driven Technological Trends in Entrepreneurship. Based on the review, we discuss future research directions, gaps in the current literature, as well as ethical concerns raised in the literature. We highlight the need for more macro-level research on GenAI and LLMs as external enablers for entrepreneurship and for research on effective regulatory frameworks that facilitate business experimentation, innovation, and further technology development.

Autonomous AI is no longer a hard-to-reach concept, it enables the agents to move beyond executing tasks to independently addressing complex problems, adapting to change while handling the uncertainty of the environment. However, what makes the agents truly autonomous? It is agentic reasoning, that is crucial for foundation models to develop symbolic logic, statistical correlations, or large-scale pattern recognition to process information, draw inferences, and make decisions. However, it remains unclear why and how existing agentic reasoning approaches work, in comparison to biological reasoning, which instead is deeply rooted in neural mechanisms involving hierarchical cognition, multimodal integration, and dynamic interactions. In this work, we propose a novel neuroscience-inspired framework for agentic reasoning. Grounded in three neuroscience-based definitions and supported by mathematical and biological foundations, we propose a unified framework modeling reasoning from perception to action, encompassing four core types, perceptual, dimensional, logical, and interactive, inspired by distinct functional roles observed in the human brain. We apply this framework to systematically classify and analyze existing AI reasoning methods, evaluating their theoretical foundations, computational designs, and practical limitations. We also explore its implications for building more generalizable, cognitively aligned agents in physical and virtual environments. Finally, building on our framework, we outline future directions and propose new neural-inspired reasoning methods, analogous to chain-of-thought prompting. By bridging cognitive neuroscience and AI, this work offers a theoretical foundation and practical roadmap for advancing agentic reasoning in intelligent systems. The associated project can be found at: https://github.com/BioRAILab/Awesome-Neuroscience-Agent-Reasoning .

In recent years, as the sophistication and capabilities of large language models (LLMs) have grown, so have the tasks for which they're applicable, going beyond information extraction and synthesis15 to include analysis, content creation, and reasoning.8 Unsurprisingly, many researchers find them useful for research tasks, such as identifying relevant papers,19 synthesizing literature reviews,3 writing proposals,11 and analyzing data.31 They have also been found effective for investigative tasks, such as drug discovery.35 There is growing concern, however, that a large portion of this success hinges on prompt engineering, which is often an ad-hoc method to revise prompts being fed into an LLM to achieve a desired response or analysis.24 LLMs are increasingly being used in scientific research, but their application often involves ad-hoc decisions that can impact research quality.

The rapid adoption of Vision Language Models (VLMs), pre-trained on large image-text and video-text datasets, calls for protecting and informing users about when to trust these systems. This survey reviews studies on trust dynamics in user-VLM interactions, through a multi-disciplinary taxonomy encompassing different cognitive science capabilities, collaboration modes, and agent behaviours. Literature insights and findings from a workshop with prospective VLM users inform preliminary requirements for future VLM trust studies.

The emergence of multimodal content, particularly text and images on social media, has positioned Multimodal Named Entity Recognition (MNER) as an increasingly important area of research within Natural Language Processing. Despite progress in high-resource languages such as English, MNER remains underexplored for low-resource languages like Urdu. The primary challenges include the scarcity of annotated multimodal datasets and the lack of standardized baselines. To address these challenges, we introduce the U-MNER framework and release the Twitter2015-Urdu dataset, a pioneering resource for Urdu MNER. Adapted from the widely used Twitter2015 dataset, it is annotated with Urdu-specific grammar rules. We establish benchmark baselines by evaluating both text-based and multimodal models on this dataset, providing comparative analyses to support future research on Urdu MNER. The U-MNER framework integrates textual and visual context using Urdu-BERT for text embeddings and ResNet for visual feature extraction, with a Cross-Modal Fusion Module to align and fuse information. Our model achieves state-of-the-art performance on the Twitter2015-Urdu dataset, laying the groundwork for further MNER research in low-resource languages.

Machine Learning (ML) algorithms are deeply embedded in var ious aspects of modern life, influencing everything from enhancing daily conveniences and sh aping online purchasing behavior to making critical decisions in areas such as hiring, loan appr ovals, college admissions, and probation rulings. Given the high stakes of these decisions, individu als often have strong incentives to strategically modify the data they provide to these algorithms to s ecure more favorable outcomes. For instance, individuals might open additional credit accoun ts or take other steps to improve their credit scores before applying for a loan. In the context of co llege admissions, applicants may retake standardized tests like the GRE, enroll in test preparation courses, or even switch schools to boost their class rankings, all in efforts to present themselves as m ore competitive candidates. Such instances of "strategic adaptation" have been extensi vely documented across disciplines including Economics, CS, and Public Policy Bj orkegren et al. [ 2020 ], Dee et al. [ 2019 ], Dranove et al. [ 2003 ], Greenstone et al. [ 2022 ], Gonzalez-Lira and Mobarak [ 2019 ], Chang et al. [ 2024 ]. The challenge arises when decision-makers deploying ML algorithms fail to account for these adaptations, potentially undermining the original goals of the policies the algorithms are intended to support. For example, in college admissions, a student's decision to change schools solely to improve their class ranking may not necessarily reflect a substantive impr ovement in their qualifications. This literature review was recently published in SIGEcom Ex changes.