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General-Purpose AI Systems Winter School
General-Purpose AI Systems (GPAIS) are reshaping the foundations of our society. Their versatility across domains brings unprecedented opportunities—but also complex ethical, social, and technical challenges. In high-risk scenarios such as healthcare, justice, and security, the development of ethical and responsible GPAIS is more critical than ever. That’s why training professionals to design, evaluate, and deploy these technologies with integrity is a strategic priority.
This initiative is part of the project “Ethical, Responsible, and General-Purpose Artificial Intelligence: Applications in Risk Scenarios (IAFER)” — funded through the ENIA University-Enterprise Chairs under the European Recovery, Transformation and Resilience Plan (Next Generation EU). The activity is also aligned with the IEEE CIS TaskForce on GPAIS. This Winter School aims to foster advanced knowledge, critical debate, and hands-on training in GPAIS, bringing together leading researchers from around the world to offer a unique educational experience for PhD-level students.
Dates: February 4–10, 2026
Location: Sala Pioneras, UGR-AI Building, University of Granada
Address: Av. del Conocimiento, 37, 18016 Granada, Spain
Homepage: https://iafer.dasci.es/en/gpais-winter-school/
The program will feature lectures, practical workshops, discussion panels, and networking opportunities with national and international experts in ethical AI and GPAIS, and will be structured in two parts: (Part I) three days focused on General-Purpose AI Systems (GPAIS)—covering topics such as foundation models, self-supervised learning, contrastive learning, and zero-shot learning—followed by two days (part II) dedicated to the mathematical foundations of AI, including neural networks and kernel theory.
Workshop on General Purpose Artificial Intelligence Systems (GPAIS)
IEEE Conference on Artificial Intelligence – CAI 2026
In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems designed to tackle a single task. The goal is to design AI models with the ability not only to perform well in the modeling tasks for which they were originally designed, but also to carry out some tasks for which they were not explicitly trained.
In this context, a General-Purpose Artificial Intelligence System (GPAIS) refers to an advanced AI system capable of effectively performing a range of distinct tasks. Its degree of autonomy and ability is determined by several key characteristics, including the capacity to adapt or perform well on new tasks that arise at a future time, the demonstration of competence in domains for which it was not intentionally and specifically trained, the ability to learn from limited data, and the proactive acknowledgement of its own limitations in order to enhance its performance. The overarching design goal of a GPAIS is to design AI models with the ability not only to perform. Several AI techniques have been identified as promising approaches to enhance GPAIS.
Paper Submission Deadline: November 30, 2025
Authors Notification: December 30, 2025
Camera-Ready Final version: January 18, 2026
The papers must follow the guidelines in https://www.ieeesmc.org/cai-2026/author-instructions-and-templates-for-conference-proceedings/ using our own submission system that will be available in November.
For final submissions, please select the workshop “W7-GPAIS: Workshop on General-Purpose Artificial Intelligent Systems (GPAIS)”, https://www.ieeesmc.org/cai-2026/workshops/#w7.
Submitted papers will be peer-reviewed with the same criteria as other IEEE CAI 2026 workshops.
Homepage: https://sites.google.com/view/gpais-cai-2026
WCCI / CEC 2026 Special Session on
Evolutionary Computation Meets Large Language Models: Foundations, Synergies, and Emerging Paradigms
The rapid rise of Large Language Models (LLMs) has profoundly reshaped artificial intelligence research and practice, extending beyond natural language processing to influence reasoning, decision-making, and optimization. In parallel, Evolutionary Computation (EC) continues to advance as a powerful global optimization framework characterized by adaptability, scalability, and robustness. The convergence of these two paradigms has given birth to a new frontier in computational intelligence, where human-like reasoning meets large-scale evolutionary search.
This Special Session aims to provide a dedicated forum for exploring the bidirectional synergy between LLMs and EC, spanning theoretical foundations, algorithmic innovation, and real-world applications. From one direction, LLM-enhanced EC leverages the rich knowledge, generative capability, and reasoning skills of LLMs to drive new forms of intelligent evolutionary operators, algorithm generation, and explainable optimization. From the other direction, EC-enhanced LLM employs evolutionary search to optimize prompts, architectures, and model behaviors in closed-box or multi-objective settings, thereby improving efficiency, interpretability, and adaptivity. Together, this interplay opens up promising pathways for developing next-generation AI systems that integrate language understanding, optimization, and self-improvement.
Paper Submission Deadline: January 31, 2026
Authors Notification: March 15, 2026
Camera-Ready Final version: April 15, 2026
Please submit your paper directly through the IEEE-WCCI 2026 submission website, selecting this special session as the main research topic.
For paper guidelines, please visit Information for Authors.
For submissions, please select the single topic “Special Session: Evolutionary Computation Meets Large Language Models: Foundations, Synergies, and Emerging Paradigms” from the “Special Session Papers” on the IEEE-WCCI 2026 submission website.
Homepage: https://wuxingyu-ai.github.io/LLM4EC/
WCCI / IJCNN 2026 Special Session on
Unveiling the Inner Workings of GPAIS: Prediction and Explanation of Emergent Abilities in Neural Networks
This special session is organized in alignment with the goals of the recently established IEEE CIS Task Force on General Purpose AI Systems (https://cis.taskforce.ieee.org/gpais), which recognizes that AI is undergoing a significant paradigm shift from narrow, task-specific models to general-purpose systems. This task force asserts that the Computational Intelligence (CI) community is uniquely positioned to address the critical challenges this transition presents, including robustness, alignment, and explainability. General Purpose AI Systems (GPAIS), particularly large-scale neural networks, have demonstrated a remarkable and often surprising characteristic: as they are scaled, they acquire qualitatively new and unpredictable capabilities. These “emergent abilities,” such as in-context learning or multi-step reasoning, were not explicitly programmed but arose spontaneously from the interplay of architecture, data, and immense computational scale.
While these abilities are powerful, their unpredictability represents a fundamental scientific challenge and a significant barrier to safe and reliable deployment. We currently lack a rigorous framework for forecasting which new abilities will emerge, at what scale, and through which mechanisms. The central goal of this special session is to bring together researchers dedicated to transforming the study of emergence from a descriptive, post-hoc observation into a predictive and explanatory science.
To achieve this, we must look inside the “black box.” Understanding why an ability emerges requires a deep dive into the internal computations of the network. This involves leveraging techniques from mechanistic interpretability to reverse-engineer the neural circuits that implement these new skills. Furthermore, to make these systems trustworthy, we need methods to translate these complex findings into human-understandable terms, a challenge addressed by Explainable AI (XAI). This session will provide a focused forum at IJCNN to present and discuss cutting-edge research aimed at predicting, analyzing, and explaining the emergent phenomena that define the frontier of modern neural networks.
Paper Submission Deadline: January 31, 2026
Authors Notification: March 15, 2026
Camera-Ready Final version: April 15, 2026
Please submit your paper directly through the IEEE-WCCI 2026 submission website, selecting this special session as the main research topic.
For paper guidelines, please visit Information for Authors.
For submissions, please select the single topic “Special Session: Unveiling the Inner Workings of GPAIS: Prediction and Explanation of Emergent Abilities in Neural Networks” from the “Special Session Papers” on the IEEE-WCCI 2026 submission website.
Homepage: https://sites.google.com/view/ijcnn2026-emergent-gpais