The paper proposes a policy-neutral execution and measurement layer to mediate between reinforcement learning policies and industrial environments, transforming ambiguous execution failures into struc…

The paper introduces DEFT, a novel Mixture-of-Experts DRL architecture, to intelligently schedule dynamic cloud workflows with varying deadlines, significantly improving performance over existing sing…

MOSAIC is a novel scheduling framework that significantly accelerates Mixture-of-Agents (MoA) workloads by jointly optimizing expert placement and utilizing confidence-aware adaptive aggregation.

AlphaTransit introduces a novel search-based planning framework that combines Monte Carlo Tree Search (MCTS) with a neural policy-value network to efficiently design high-quality, city-scale bus trans…

The paper introduces EvoXXLTraffic, an ultra-large, sensor-evolving dataset that simulates real-world road network growth, demonstrating that existing state-of-the-art traffic forecasting models fail…

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

This paper introduces Selective Field Transmission (SFT), a mechanism that dynamically adapts transmitted message components to each receiver's needs in publish-subscribe systems, achieving significan…

This paper analyzes darknet traffic to characterize advanced, AI-assisted bot reconnaissance, finding that modern evasion techniques allow most bot traffic to bypass standard IDS thresholds.

SPARK introduces a predictive, traffic-aware autoscaling toolchain for Kubernetes that uses eBPF to enhance security and significantly reduce timeout errors during sudden traffic spikes.

The paper develops a trust-aware framework to model how connected vehicles adapt their routing decisions and overall traffic flow when exposed to misinformation, showing that endogenous trust provides…

RACE-Sched is an asynchronous agentic framework that successfully integrates low-latency, real-time scheduling decisions with advanced, long-horizon reasoning provided by Large Language Models.

This paper provides a comprehensive review of network optimization aspects for Connected and Autonomous Vehicles (CAVs), aiming to clarify misconceptions and outline future research directions.

The paper introduces Agent Control Protocol (ACP), a stateful temporal admission control mechanism that enforces behavioral properties over execution traces to prevent harmful patterns from individual…

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…

SwarmHarness introduces a decentralized, incentive-aligned protocol enabling self-organizing compute swarms for AI tasks, eliminating the need for central coordinators or heavy blockchain infrastructu…

OrbitBFT introduces a novel two-stage hierarchical BFT consensus protocol that enables scalable and robust Byzantine Fault-Tolerant coordination for large-scale Low Earth Orbit satellite constellation…

This paper introduces the $k$-Step-Central Shortest Path problem to maximize reachability in transportation networks and provides a polynomial-time algorithm for unweighted graphs.

The paper introduces Post-Deterministic Distributed Systems (PDDS) as a new model to coordinate autonomous infrastructure where participants, including stochastic agents, produce divergent reasoning p…

The paper proposes a novel hardware extension that enables deterministic, kernel-bypass switching to user-level protection domains upon interrupt arrival, significantly reducing worst-case latency for…

A novel multi-agent reinforcement learning algorithm, TC-GQN, is proposed for high-level control and orchestration of mobile networks, enabling energy savings while maintaining QoS.