Workshop on General Purpose Artificial Intelligence Systems (GPAIS)

Within the field of GPAIS, several research areas contribute to the design of AI models with greater versatility, facilitating the learning of new tasks. These areas include, but are not limited to Multi-task models, which handle multiple tasks simultaneously; few-shot learning models, which require fewer training data points to understand and perform new tasks; auto machine learning (AutoML) and neural architecture search (NAS) systems, which can automatically adapt the AI model to a new problem; and models adaptation for new problems capable of taking advantage of prior knowledge. Collectively, these research areas are making progress in the development of GPAIS models. The flexibility and ease of adaptation of EC make them a perfect match to cope with the stringent properties sought for GPAIS, including the multimodality of the tasks being solved, their variability over time or the large dimensionality of the design and construction of GPAIS.  Actually, there are many research areas in EC that can be useful in either designing or enriching these AI models. However, the research in this line is often developed in parallel, without communication channels between them, which would allow a global vision of how EC can improve the design of these increasingly generic models.

In this Workshop, we encourage researchers to submit original contributions proposing new algorithmic approaches, improvements for GPAIS. Potential topics of interest for the workshop include, but are not limited to, the following:

Using Computational Intelligence (CI) to Enhance GPAIS Performance and Expand GPAIS Application Boundaries

    • Neuro-symbolic and CI-enhanced LLM architectures

    • CI-based preprocessing for GPAIS

    • Foundation models

    • Explainability and safety in GPAIS

    • Low-resource adaptation and efficiency for GPAIS

    • CI for robust multi-modal and multi-agent AI

    • Evolutionary fine-tuning and prompt optimization

    • Structural optimization of LLM for different objectives (alignment, XAI, efficiency)

    • Large scale transformers and distributed computing strategies to build GPAIS

Using GPAIS to Advance Intelligent, Explainable, and Semantic-Aware CI: Leverage GPAIS to select:

    • GPAIS-driven intelligent evolutionary algorithms.

    • GPAIS-driven intelligent fuzzy systems

    • GPAIS for automated CI algorithm configuration and new CI algorithm design.

    • Automated algorithm construction using LLM translated domain knowledge.

    • GPAIS-driven explainable CI techniques

    • Design of more efficient CI techniques

Application scenarios for CI+GPAIS: 

    • Benchmarks and validation frameworks for GPAIS

    • Robotics

Conversational agents

    • Bioinformatics

    • Healthcare diagnosis with CI-enhanced LLMs

    • Neuro-symbolic architectures in autonomous vehicle navigation systems

Paper Submission Deadline: January 30, 2026

Authors Notification: February 10, 2026

Camera-Ready Final version: February 15, 2026

The papers must follow the guidelines in https://www.ieeesmc.org/cai-2026/author-instructions-and-templates-for-conference-proceedings/.

To submit a contribution for this workshop, please use the conference submission wizard on Papercept, using the code W7-GPAIS.

Submitted papers will be peer-reviewed with the same criteria as other IEEE CAI 2026 workshops.

Prof. Dr. Xingyu Wu

Dept. of Data Science and Artificial Intelligence.

The Hong Kong Polytecnic University (PolyU), Hong Kong SAR, China

xingy.wu@polyu.edu.hk

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Prof. Dr. Isaac Triguero

Dept. of Computer Science and Artificial Intelligence.

DaSCI, Andalusian Research Institute in Data Science and Computational Intelligence

University of Granada, Spain

triguero@decsai.ugr.es

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Prof. Dr. Daniel Molina

Dept. of Computer Science and Artificial Intelligence.

DaSCI, Andalusian Research Institute in Data Science and Computational Intelligence

University of Granada, Spain

dmolina@decsai.ugr.es

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Prof. Dr. Javier del Ser

TECNALIA, Basque Research & Technology Alliance (BRTA), and Department of Mathematics

University of the Basque Country (UPV/EHU)

javier.delser@tecnalia.com

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