The global ageing population necessitates new and emerging strategies for caring for older adults. In this article, we explore the potential for transformation in elderly care through Agentic Artificial Intelligence (AI), powered by Large Language Models (LLMs). We discuss how Agentic AI facilitates proactive, autonomous decision-making in elderly care. Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older adults, represents important areas of application. With the potential to significantly transform elderly care, Agentic AI also raises profound concerns about data privacy and security, decision independence, and access. We share key insights to emphasize the need for ethical safeguards, privacy protections, and transparent decision-making. Our goal in this article is to provide a balanced discussion of both the potential and the challenges of Agentic AI, and to offer insights into its responsible use in elderly care, aligning it with the requirements and vulnerabilities specific to the elderly. Finally, we identify the priorities for the academic research communities to achieve human-centred advancements and integration of Agentic AI in elderly care. To the best of our knowledge, this is one of the first comprehensive studies explicitly focused on LLM-based Agentic AI for elderly care. Hence, we address the literature gap by analyzing the unique capabilities, applications, and limitations of LLM-based Agentic AI in elderly care.
Internet of things (IoT) ecosystems introduce significant cybersecurity challenges due to device heterogeneity, firmware opacity, constrained resources, distributed deployment, and the integration of devices within wider socio-technical systems where they are used. Existing approaches to address IoT cybersecurity typically address isolated aspects of this problem, such as vulnerability enumeration, anomaly detection, or risk assessment; but without integrating them across the full lifecycle of devices and systems. This paper presents an extensible architecture that unifies cybersecurity testing, runtime monitoring, contextual risk modelling, secure update mechanisms, and auditable evidence management for IoT ecosystems that aims to address these challenges. The framework supports both device under test and system under test perspectives and integrates component-level techniques (such as SBOM generation, network fuzzing, machine learning-based anomaly detection, and access control risk evaluation) with system-level, knowledge-based, risk modelling to capture threat propagation across interconnected assets. A distributed ledger-backed auditable data infrastructure ensures integrity and traceability of indicators, results, and decisions. Automated workflow orchestration enables flexible tool chaining and lifecycle-aware execution aligned with established security development lifecycles. The approach is validated through three industrial use cases in aviation cargo monitoring, smart manufacturing, and telecommunication residential gateways. Results demonstrate the feasibility of combining static analysis, runtime indicators, and dynamic risk assessment to prioritise vulnerabilities contextually, detect anomalous behaviour, and support secure patch deployment in resource-constrained environments. The work advances lifecycle-integrated, system-aware cybersecurity assurance for IoT ecosystems and highlights the need for contextualised, interoperable tooling to address systemic vulnerability and risk propagation in complex systems where IoT, ICT and people interact.
Current evaluations of sentence embedding models typically rely on static test beds such as the Massive Text Embedding Benchmark (MTEB). While invaluable, repeated tuning on a fixed suite can inflate reported performance and obscure real-world robustness. We introduce the Paraphrasing Text Embedding Benchmark (PTEB), a dynamic protocol that stochastically generates meaning-preserving paraphrases at evaluation time and aggregates results across multiple runs. Using a cost-efficient LLM-based method grounded in semantic textual similarity gold ratings, we show that LLMs generate token-diverse but semantically preserving, paraphrases. Across 7 MTEB tasks, we validate our hypothesis that the performance of sentence encoders is sensitive to changes in token space even when semantics remain fixed. We also observe that smaller models are not disproportionately affected relative to larger ones. Our results are statistically robust over multiple runs and we extended our experiments to 3 multilingual datasets covering 10 languages. More generally, we aim to propose a new evaluation paradigm in NLP that relies less on static, pre-defined benchmarks but shifts towards dynamic, stochastic evaluation leveraging eval-time compute.
The rapid spread of information and rumors through social media platforms, especially in group settings, motivates the need for more sophisticated models of rumor propagation. Traditional pairwise models do not account for group interactions, a limitation that we address by proposing a higher-order rumor model based on hypergraphs. Our model incorporates a group-based annihilation mechanism, where a spreader becomes a stifler when the fraction of hyperedges aware of the rumor exceeds a threshold. The dynamics has two distinct subcritical behaviors: exponential and power-law decay, which can coexist depending on the heterogeneity of the hypergraph. Interestingly, in the set of parameters we analysed, we found continuous phase transitions in both homogeneous and heterogeneous hypergraphs. This finding aligns with the literature suggesting that real-world rumor propagation occurs near criticality. Finally, we validated our model using empirical data from Telegram and email cascades, which provides additional evidence and possible explanations for this criticality claim. These results open the door to a more detailed understanding of rumor dynamics in higher-order systems.
With the advancement in the technological architecture of Sixth Generation (6G) networks, near-field-driven designs have become increasingly prominent in wireless communications systems. This technological shift towards achieving efficient communication with enhanced performance has introduced a new research trend and opened problems in near-field communications. With the prime objective of highlighting these open research problems and subsequent challenges, this tutorial focuses on introducing the fundamental concepts and novel technological innovations in near-field-driven 6G networks. It accentuates the potential of the near-field communication systems to enhance localization precision, improve the beamfocusing for energyefficient transmission, and ability to unify various wireless subsystems, forming more advanced and integrated communication systems. Moreover, this tutorial also addresses open research problems in the areas mentioned to improve energy efficiency and system performance and focuses on practical implementation issues in these domains. Furthermore, in order to demonstrate the importance of near-field-driven designs, a subsequent simulation of a Quadrature Phase-shift Keying (QPSK)-modulated end-toend system is given, which highlights that the beamfocusing can lead to spatial separation of users, granting the beam-focusing vector two degrees of freedom.
