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 novel hybrid authorization framework that combines roles and First-Order Logic to enforce fine-grained, triple-level access control for autonomous agents interacting with knowledg…

The paper introduces RadioMaster, a novel multi-agent system that successfully translates high-level user intents into physically viable, real-world radio signals, significantly outperforming existing…

This paper demonstrates that using a communication algorithm (CommFormer) with heterogeneous agents significantly improves the speed and performance of multi-agent reinforcement learning for autonomou…

The paper proposes the Intelligent Computing Architecture Model (ICAM), a six-layer framework that unifies disparate concepts in model-native computing by viewing the LLM stack through a dual-plane ar…

The paper introduces an AI red teaming agent that drastically reduces the time and effort required for security testing by allowing operators to define complex attack goals using natural language, com…

The paper proposes a Digital Twin-assisted Adaptive Multi-Agent Deep Reinforcement Learning framework to intelligently manage spectrum and resources in complex, dynamic Open-RAN 6G networks utilizing…

UI-KOBE is a framework that enhances lightweight mobile GUI agents by integrating reusable, app-specific knowledge graphs, allowing them to perform complex tasks efficiently on-device without relying…

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…

The paper proposes ExAI5G, a logic-based explainable AI framework that integrates a Transformer-based IDS with XAI techniques to provide highly accurate and transparent intrusion detection for 5G netw…

The paper proposes a declarative, autonomous, self-protecting framework for securing complex 5G/6G networks by leveraging a standardized security ontology and automated graph reasoning to neutralize l…

The paper introduces Hyperparam, a set of lightweight JavaScript libraries designed to enable direct, model-aware querying of unstructured data (like agent traces) within client-side AI applications.

AgenTEE is a system that enables the secure, confidential execution of complex LLM agent pipelines directly on edge devices by using isolated confidential virtual machines.

The paper proposes Multi-Agent Computer Use (MACU) systems, which significantly improve performance on complex, long-horizon tasks by enabling parallel execution and dynamic task decomposition compare…

The paper proposes A2X, an LLM-native progressive-disclosure scheme that structures service taxonomies hierarchically and searches them layer-by-layer at query time, solving context overflow and impro…

The paper evaluates Language Model Agents (LMAs) for red-teaming by benchmarking their ability to perform lateral movement, finding that expert-defined action plans are most effective, though all moda…

LanG is a governance-aware, open-source agentic AI platform that unifies security operations by providing advanced correlation, automated rule generation, and attack reconstruction capabilities.

This paper models the security risks of subagent spawning in multi-agent networks, demonstrating that insecure memory inheritance from parent agents allows local compromises to spread across system bo…

The paper proposes an autonomous red teaming framework combining LLMs and RL to generate sophisticated, multi-stage cyber attack campaigns, demonstrating its necessity for evaluating robust AI-enabled…

The paper introduces AgentWard, a lifecycle-oriented, defense-in-depth architecture designed to systematically secure autonomous AI agents by protecting them across all stages of their operation.