IEEE SIGAGILE, IEEE SIG on Artificial General Intelligence, Models, and Agents (AGILE), is formally approved by IEEE Communications Society (ComSoc) Technical Committee on Big Data (TCBD).


Artificial General Intelligence (AGI) is an implicit or explicit north-star goal since 1956 Dartmouth AI Conference. Given the rapid advancement of Machine Learning (ML) models, the concept of AGI has passed from being the subject of philosophical debate to one with near-term practical relevance. Nowadays, benefiting from the rapid progress and astonishing success in Natural Language Processing (NLP) and Computation Vision (CV), “sparks” of AGI are even regarded to be already present in the latest generation of Large Language Models (LLMs) and Large Vision Models (LVMs), with prominent examples like ChatGPT, Gemini, DALL-E and Sora. Meanwhile, techniques like generative Generative Adversarial Networks (GANs) and Diffusion models as well as scalable Transformers not only boost the arrival of these amazing Foundation Models (FMs), but also are seen as a transformative technology beyond shaping the AI field:

  • FMs promise a tangible enhancement to wireless communications and networks by leveraging the generative capabilities as well as the multimodality nature of the data acquired in wireless networks. It promises to overcome long-standing difficulties such as low generality, limited performance gain, complicated management, and inconvenient collaboration.

  • The application of FMs for inference and decision-making purposes have demonstrated appealing results. It is widely anticipated that FM-empowered (connected) autonomous agents with embodied intelligence are expected to emerge with the astonishing capabilities of accomplishing tasks autonomously and coherently.

Given these facts and visions, there is a clear need to establish a Special Interest Group (SIG) on AGI, Models, and Agents (AGILE) to address the emerging technical challenges therein. On one hand, it still requires ongoing significant efforts to deliver cost-effective AGI solutions. On the other hand, how to tackle the bloated parameters in FMs in edge and user equipment remain under-investigated.


IEEE SIGAGILE aims to organize and solicit researchers from both the academia and the industry to accelerate the study on AGILE. Tentative topics include, but are not limited to

  • Artificial general intelligence techniques for AGILE

  • Model design and training for AGILE

  • Communication techniques in AGILE

  • Communication and learning theory in AGILE

  • Performance evaluation metrics of AGILE

  • Collaboration mechanism in AGILE

  • Network architecture for AGILE

  • Security and privacy of AGILE

  • Data collection and governance of AGILE

  • Full-lifecycle management and orchestration of AGILE

  • Architecture and protocol design & standarization of AGILE