This paper systematically analyzes the complex design space of hybrid multi-agent systems combining on-device and cloud AI models, finding that the optimal architecture is highly task-dependent and th…

This systematic review analyzes the bidirectional integration of AI and DLT, finding that while research is growing, most studies neglect cross-layer co-design and fail to demonstrate production-scale…

This paper proposes a lightweight, multi-layer Machine Learning-based security framework for Industrial IoT (IIoT) to enhance trust convergence and detect advanced threats.

The paper provides a holistic threat model for LLM-enabled robotic systems by analyzing how conventional, adversarial, and conversational threats propagate across the entire perception-planning-actuat…

This survey provides a comprehensive, structured review of safety research in Embodied AI, analyzing attacks and defenses across the entire embodied pipeline to guide the development of safe, robust,…

This paper synthesizes the emerging field of blockchain and AI for securing intelligent networks by providing a comprehensive taxonomy, integration patterns, and an evaluation blueprint.

The paper proposes a novel attack paradigm demonstrating how compromising a single robot in an LLM-controlled multi-robot system can rapidly propagate malicious intent to cause coordinated unsafe acti…

The paper proposes a communication-centric 6G-LLM architecture for tactical autonomous defense vehicles, demonstrating significant improvements in coordination and communication efficiency over conven…

The paper proposes a two-timescale governance framework using a multi-agent LLM to safely update and guide RL agents for SDN-IoT defense, significantly improving performance and stability under advers…

Restructuring the communication topology among robots provides significantly greater performance gains in multi-robot coordination than simply increasing the size of the onboard AI models, given fixed…

This paper provides a comparative framework analyzing the distinct security and privacy risks inherent in virtual and robotic assistive systems, culminating in design recommendations for trustworthy t…

This paper investigates the scaling behavior of homogeneous LLM-driven Multi-Agent Systems (MAS) and finds that performance exhibits diminishing returns due to coordination overhead, rather than scali…

This paper analyzes the potential downsides of integrating advanced AI and smart capabilities across the Edge-Cloud continuum in modern industry, focusing specifically on security vulnerabilities, sid…

This paper proposes a modular, agentic AI chatbot architecture to assist students with exercise solving, aiming to ensure responsible and pedagogically sound AI use in education.

This paper surveys how integrating Edge Computing, Machine Learning, and Deep Learning can enhance the security and resilience of complex Internet of Vehicles (IoV) networks.

The paper introduces a diagnostic framework to determine if World-Action Models (WAMs) provide genuinely actionable behavioral improvements beyond simply achieving task success, finding that WAMs ofte…

The paper introduces a unified framework to fairly evaluate LLM agentic capabilities by standardizing diverse benchmarks and separating the effects of the LLM model from the surrounding framework and…

MOSAIC introduces a structured agentic framework that treats automated data science as a staged, context-grounded model selection problem, improving performance and traceability over traditional AutoM…

The paper systematizes the interaction between autonomous AI agents and blockchain platforms using a bidirectional trust framework, identifying significant gaps in current standards and proposing a ta…