This paper proposes Autogenic network management, a self-programming extension to agentic AI for next-generation network management in 6G networks.

The paper introduces APIOT, the first LLM framework capable of autonomously performing the full discovery, exploitation, patching, and verification cycle against bare-metal industrial OT devices.

This paper proposes a distributed architecture for automating and controlling multi-vendor, multi-layer IP over DWDM networks using SDN, enabling end-to-end service lifecycle automation and closed-loo…

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…

This paper demonstrates that autonomous aircraft can self-organize into efficient corridor flows in decentralized settings.

This paper proposes a definition for 'AI-nativeness' in systems, based on an AI agent's authority over system decisions.

This paper presents a tutorial-and-survey on integrating agentic AI into Next-Generation Networks (NGNs), addressing the gap in protocol integration, evaluation, and standardization alignment.

The paper identifies and demonstrates the existence of a covert sublayer, called the Exclusive Network, within the I2P anonymous network, which allows nodes to host services without being discoverable…

The paper introduces a systematic framework and defense mechanisms to analyze and mitigate autonomous LLM agent worms that propagate through persistent agent state and cross-platform multi-agent syste…

This paper proposes ARTSN, a scheduling paradigm for autonomous real-time systems using time-sensitive networking, addressing volatility and absence challenges of self-triggered traffic.

This paper synthesizes expert knowledge from a workshop to provide a comprehensive framework and best-practice guidelines for developing high-quality reinforcement learning environments for autonomous…

The paper proposes and tests a novel, non-security 'Recuse Signal'—an in-band signal—to allow operators to tell autonomous LLM agents to voluntarily withdraw access, demonstrating that compliant agent…

The paper argues that the Authorization-Execution Gap (AEG)—the divergence between intended authorization and actual execution—is a critical safety and security flaw in open-world agents, requiring so…

Proposed EvoOMG framework optimizes throughput in heterogeneous Wi-Fi networks with both legacy and MLO-capable stations using an evolution-oriented multi-agent guidance approach.

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…

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 presents TypoNet, a system that constructs and validates a symbolic model of a production-scale WAN from network artifacts using large language models for translation and a solver for relia…

The paper proposes the Redpanda Agentic Data Plane (ADP), an architecture that uses out-of-band metadata channels to deterministically enforce security policies and governance for autonomous AI agents…