[hpc-announce] AIxNET 2026 - Call for Papers - Due Date: June 20th, 2026

[Kun Qiu]

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CALL FOR PAPERS – AIxNET 2026


International Conference on Interconnected AI and NETworks 2026


Nov. 23-25, 2026 – Paris, France


https://urldefense.us/v3/__https://aixnet.dnac.org/__;!!G_uCfscf7eWS!bLGlTFsiFHF9v5aDaodN3PYF2XjYhYTujo2XEEsa1rnIaseoHheWLjJTWSQBDsFUPHsUt_DP8MqIZvoVMA$ 

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Important dates

Paper submission deadline: June 20, 2026

Paper Acceptance Notification: September 15, 2026

Camera ready: October 5, 2026

Conference Date: November 23-25, 2026


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 nearRT vs lower layer control), and the release of open
datasets and artifacts to help

the community builds together.


AIxNET is intending to build a stimulating, open, dynamic, and friendly

forum to cocreate 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.


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 nonRT and nearRT control (e.g.,
ORAN RIC), Core CNFs,

         and edge resources

·Agentic 6G functions

·Interconnection and collaboration between AI agents

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

· Supervised/unsupervised/selfsupervised learning for prediction, anomaly
detection,

          resource allocation, QoE optimization

· ML and LLM techniques for scheduling, slicing, mobility, energy saving;
crossdomain orchestration across

          RAN/Core/transport for B5G and 6G

· Programmable data planes (P4/eBPF) and SDN control plane with ML in the
loop; NWDA Fenabled 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 build of meaningful KPIs (e.g., relying on
MTTR, SLO, energy–QoE tradeoffs…)

· 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)


Paper Submission:


Authors  are  invited  to  submit  original  contributions  that  have

not  been published or  submitted  for publication  elsewhere.

Papers should  be  prepared using the  IEEE 2-column  conference

style and  are limited to 8 pages including references  for regular papers,

 5 pages  including references for short papers and 2-4 pages for

Demos/Positions. Papers must be submitted electronically in

 PDF format through EDAS at: *https://urldefense.us/v3/__https://edas.info/N35032*__;Kg!!G_uCfscf7eWS!bLGlTFsiFHF9v5aDaodN3PYF2XjYhYTujo2XEEsa1rnIaseoHheWLjJTWSQBDsFUPHsUt_DP8MqLsTJvrQ$ 
<https://urldefense.us/v3/__https://edas.info/N35032__;!!G_uCfscf7eWS!bLGlTFsiFHF9v5aDaodN3PYF2XjYhYTujo2XEEsa1rnIaseoHheWLjJTWSQBDsFUPHsUt_DP8Mq3AqgQRA$ >



Open artifacts are encouraged: release code/data/measurement scripts

when possible; otherwise provide high fidelity synthetic surrogates

or detailed reproduction recipes. Comparative studies must clearly state

the targeted control layer(s) and report stability/latency/safety metrics

 alongside performance.



For accepted papers to be included on AIxNEt 2026 proceedings,

at least one author must register at the Author rate and papers

must be presented in-person at the conference by a registered co-author.


Technical Program Co-Chairs:


Sahar Hoteit (Paris-Saclay University, France)

Chiara Contoli (University of Urbino, Italy)



General Co-Chairs:

Stefano Secci (CNAM, France)

Emmanuel Bertin(Orange Innovation, France)



--

Dr. Kun Qiu

Assistant Professor

Institute of Space Internet

Fudan University, China

qkun at fudan.edu.cn

qkun at ieee.org