arXiv AI
Jun 26
EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning
arXiv:2606. 26327v1 Announce Type: cross Abstract: In actor-critic reinforcement learning, network architectures are typically manually designed.
Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems
arXiv:2606. 24416v1 Announce Type: new Abstract: Network operators' changing policies, service requirements, and stringent real-time constraints render existing methods designed with fixed objectives and constraints ineffective.
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arXiv AI
Jun 19
A Multi-Agent system for Multi-Objective constrained optimization
arXiv:2606. 20236v1 Announce Type: new Abstract: Many decision-making problems in computing and networking systems can be naturally formulated as cost-minimization problems under performance constraints.
arXiv AI
Jun 10
Event-Driven Reinforcement Learning Enables Long-Horizon Control in Semiconductor Fabrication
arXiv:2606. 10705v1 Announce Type: cross Abstract: Reinforcement learning promises to optimize sequential decisions in large-scale systems.
arXiv AI
Jun 4
Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers
arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.
arXiv AI
Jul 7
AgenticPD: A Stage-Aware Agentic Framework for Physical Design QoR Optimization
arXiv:2607. 04758v1 Announce Type: new Abstract: Physical design quality-of-results~(QoR) optimization is hard and expensive.
arXiv AI
Jul 29
Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering
arXiv:2607. 25090v1 Announce Type: new Abstract: Machine learning engineering (MLE) tasks require long-horizon decision making over iterative solution debugging and refinement, under expensive and feedback-driven environment interactions.
arXiv Machine Learning
Jul 7
Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence
arXiv:2602. 24115v2 Announce Type: replace Abstract: Open RAN (O-RAN) exposes rich control and telemetry interfaces across the Non-RT RIC, Near-RT RIC, and distributed units, but also makes it harder to operate multi-tenant, multi-objective RANs in a safe and auditable manner.
arXiv AI
Jun 2
AgentxGCore: Agentic AI for Next-Generation Mobile Core Network
arXiv:2606. 00417v1 Announce Type: cross Abstract: To meet the stringent requirements of emerging applications and the increasingly complex network management and operation, the Next Generation Mobile Networks (NextG), or 6G, will adopt an AI-native architecture on the Core Network (CN).
arXiv AI
Jun 30
Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks
arXiv:2606. 28339v1 Announce Type: cross Abstract: Industrial 6G networks require ultra-reliable, low-latency, and energy-efficient connectivity in dynamic and blockage-prone environments, where conventional terrestrial deployments often fail to ensure stable coverage.
arXiv AI
Jun 19
Oranits: Mission Assignment and Task Offloading in Open RAN-based ITS using Metaheuristic and Deep Reinforcement Learning
arXiv:2507. 19712v3 Announce Type: replace-cross Abstract: In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing.
arXiv AI
Jul 7
ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning
arXiv:2602. 21534v3 Announce Type: replace Abstract: Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks.
arXiv AI
Aug 6
EASy: Towards Efficient LLM-Based Agentic System
arXiv:2608. 04588v1 Announce Type: cross Abstract: Agentic systems have emerged as a promising paradigm for solving complex tasks by coordinating specialized LLM-based agents.