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AIxNET 2026: International Conference on Interconnected AI and NETworks

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投稿締切日:
2026-06-20
通知日:
2026-09-15
開催日:
2026-11-23
開催地:
Paris, France
開催回数:
1
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AIxNET
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使用した入力: 確認できる開催回数：1 · 過去 24 か月にこのページを開いた研究者：4 人

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論文募集

AIxNET 2026 (International Conference on Interconnected AI and NETworks) is an academic conference held in Paris, France on 2026-11-23. The paper submission deadline is 2026-06-20. Acceptance notifications are sent on 2026-09-15.

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)
最終更新：Dou Sun（）

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