Networks are entering an era where both classical ML and emerging generative and agentic AI are transforming end-to-end networking—from intent capture to closed-loop control across RAN, Core, transport, and edge/cloud. AIxNET welcomes contributions that advance algorithms, architectures, protocols, evaluations, and safeguards for trustworthy, explainable, and safe-to-operate AI-driven networking. We particularly encourage rigorous comparative studies across control layers (SMO/intent vs near-RT vs lower-layer control), and the release of open datasets and artifacts to help the community build together.

AIxNET is intending to build a stimulating, open, dynamic, and friendly forum to co-create the future and spark collaborations across teams. The conference will be a unique opportunity to gather academic and industry research on this crucial topic for 2030 networks. Expect interactive sessions, demos, and time for discussion.

Main Topics of Interest include (but are not limited to)

1. Agentic AI: from Human Intent to Action Autonomy

Networked “xLM” challenges: Intent capture/parsing/policy synthesis at SMO and service layers, use of Large, Small or Machine Language Models (LLM, SLM, MLM)
Hierarchical/heterogeneous agents spanning non-RT and near-RT control (e.g., O-RAN RIC), Core CNFs, and edge resources
Agentic 6G functions
Interconnection and collaboration between AI agents
Tool and protocols for network-facing agents (e.g., MCP-enabled clients/servers), conflict resolution, safe rollbacks

2. New paradigms for networking: from Classical ML to xLM-based Control at Scale

Supervised/unsupervised/self-supervised learning for prediction, anomaly detection, resource allocation, QoE optimization
ML and LLM techniques for scheduling, slicing, mobility, energy saving; cross-domain orchestration across RAN/Core/transport for B5G and 6G
Programmable data planes (P4/eBPF) and SDN control plane with ML-in-the-loop; NWDAF-enabled analytics
Challenges for access networks and edge networking, use of alternative models, SLM, TRM
Architecture and framework for agentic AI networking
Data collection and labeling

3. Comparative Designs Across Layers: SMO/Intent vs Near-RT vs Lower-Layer Control

Side-by-side evaluations of top-down (intent-driven) vs bottom-up (local) autonomy
Responsibility split across SMO policies, RIC xApps/rApps, Core functions, device/edge controllers
Stability, latency and safety; arbitration under competing objectives (QoE, energy, cost, SLAs)
Cross-layer observability, auditability, and explainability methodologies

4. Explainability and trustworthiness: Bias and Functional Safety

Human in the loop supervision and autonomy levels for safe operations
Explainability for operator oversight (pre/post methods, rationales, provenance, accountability logs)
Security and governance for AI-operated changes (access control, authorization, verification, compliance-by-design)
Possible Bias sources and mitigation (data, prompts, tools, policies); fairness in resource allocation and service admission
Trust, safety and ethical considerations in generative and agentic AI networking

5. Evaluation, Benchmarks, Open Datasets, and experimentations

Public datasets/benchmarks for RAN/Core/transport/edge; simulated vs real testbeds
Evaluation methodology and built of meaningful KPIs (e.g., relying on MTTR, SLO, energy–QoE trade-offs…)
Network performance metric in generative and agentic AI communication systems
Digital twins, experimentation platforms, and testbeds for generative and agentic AI networking
Reproducible pipelines, artifact sharing, and insightful negative results, robustness to drift
Sustainability and cost modeling (e.g., compute budgets, edge vs cloud placement)