CFP

Rev-AI 2026: The IEEE 2026 International Conference on Revolutionary Artificial Intelligence and Future Applications

Varna, Bulgaria, June 3-5, 2026

Conference website	https://rev-ai.org/
Submission link	https://easychair.org/conferences/?conf=revai2026

Topics: revolutionary ai large language models machine learning deep learning

The IEEE 2026 International Conference on Revolutionary Artificial Intelligence and Future Applications (Rev-AI 2026) Conference is the premier global event convening pioneering researchers, industry leaders, and visionary innovators to explore the frontier of artificial intelligence. This three-day conference, set in the dynamic and innovative city of Varna, will showcase paradigm-shifting research, transformative applications, and collaborative dialogues on the technologies that are redefining the future of intelligence. Attendees will have the unparalleled opportunity to engage with the minds spearheading the AI revolution, and network with a global community of experts. From foundational models that rethink cognition to AI-driven solutions for humanity's greatest challenges, REV-AI 2026 promises a comprehensive expedition into the next era of artificial intelligence.

We are thrilled to invite researchers, industry professionals, and innovators to the IEEE Rev-AI 2026 Conference, the foremost event at the nexus of cybersecurity and artificial intelligence.

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. Don't miss this unparalleled opportunity to shape the future of secure, intelligent systems with special interest in but not limited to:

Foundational & Revolutionary AI Models

Next-Generation Architectures (e.g., beyond Transformers, State Space Models, Neuro-Symbolic AI)
AI for Scientific Discovery (AI4Sci)
Causal AI & Reasoning
Embodied AI and Active Perception
Foundation Models for Multimodal Understanding
Theoretical Limits of AI
Parameter-Efficient Fine-Tuning (PEFT): Advanced methods including LoRA (Low-Rank Adaptation), Adapters, and Prompt Tuning for rapidly adapting massive models to new tasks with minimal computational overhead.
QLoRA and Quantized PEFT: Techniques for fine-tuning quantized models (e.g., 4-bit) without performance degradation, enabling the adaptation of billion-parameter models on a single GPU.
Conditional Computation & Mixture-of-Experts (MoE): Advanced routing algorithms, training stability for sparse models, and dynamic activation of model parts for extreme efficiency at scale.
Mathematical Reformulations for Efficiency: Replacing core operations (e.g., attention mechanisms) with more computationally efficient approximations without loss of performance.
State Space Models (SSMs) for Efficiency: Leveraging SSMs like Mamba for sequential data handling that is fundamentally faster and more memory-efficient than traditional Transformers.

Training & Learning Process Optimization

Efficient Fine-Tuning Paradigms: Novel approaches to LoRA (e.g., DoRA, VeRA) and fusion with other methods for greater efficiency and effectiveness.
Fast Convergence Techniques: Novel optimizers, learning rate schedules, and training curricula that reduce total training time and computational cost.
Gradient-Free & Few-Step Optimization: Exploring methods like Evolutionary Strategies for training where backpropagation is impractical.
Sparse Training: Techniques to train a model with a sparse architecture from the very beginning, avoiding the expensive "train-then-prune" cycle.

Inference & Deployment Optimization

Dynamic Neural Networks: Models that can adapt their inference path based on input complexity, slashing latency for "easier" tasks.
Speculative Decoding & Lookahead Reasoning: Using smaller, faster models to "draft" responses verified by a larger model, dramatically accelerating LLM inference.
Hardware-Aware Neural Compression: Co-designing compression techniques with specific hardware properties for maximal throughput.
Merging and Consolidating LoRA Adapters: Methods for efficiently merging multiple fine-tuned LoRA adapters into a single, powerful model without catastrophic interference.

Sustainable & Green AI

Energy-Aware Model Design: Techniques to directly model, measure, and minimize the energy consumption of AI models.
Carbon-Efficient Training: Scheduling and locating training jobs in data centers powered by renewable energy.
The Green Impact of PEFT: Quantifying the massive reduction in computational resources and energy enabled by methods like LoRA and QLoRA.

Unified Frameworks & Evaluation

Multi-Objective Optimization Frameworks: New tools that simultaneously optimize for accuracy, latency, memory, energy, and robustness.
Benchmarking Efficiency at Scale: Developing robust benchmarks for evaluating the optimization of massive models across diverse hardware.
The Efficiency-Ability Trade-off: Theoretical and empirical studies on the relationship between a model's computational budget and its emergent capabilities.

AI & Human Collaboration & Society

Human-AI Teaming and Cognitive Augmentation
AI for Creativity, Art, and Co-Creation
AI-Driven Education and Personalized Learning
The Future of Work and Economics in an AI-Dominant World
AI for Governance and Public Policy
Philosophical and Ethical Frameworks for AGI

Conversational AI platforms such as:

ChatGPT (OpenAI), Gemini (Google), Copilot (Microsoft), Claude (Anthropic), Meta AI (Meta), Grok (xAI) Leading Open-Weight Models: Llama (Meta), Mistral AI, Qwen (Alibaba)

Major Chinese Platforms such as:

DeepSeek, Ernie Bot (Baidu), Tongyi Qianwen (Alibaba), Zhipu AI

Specialized Coding Assistants such as:

GitHub Copilot, Replit CodeComplete, Amazon CodeWhisperer, Codium

Committees

General chairs

Plamen Zahariev, University of Ruse, Bulgaria
Georgi Hristov, University of Ruse, Bulgaria

Technical Program Chair

Alaeddin Abuabed, University of Central Oklahoma, USA

Publicity Chair

Diyana Kinaneva, University of Ruse, Bulgaria
Majdi Maabreh, The Hashemite University, Jordan

Publication Chair

Georgi Georgiev, University of Ruse, Bulgaria
Eyad Elyan, Robert Gordan University, UK
Omar Darwish, Eastern Michigan University, USA

Workshops Chair

Ola Karajeh, Ferris State University, USA
Hazem Migdady, Oman Collage of Management & Technology, Oman
Yazan A. Alshboul, Prince Sultan University, Saudi Arabia

Local Organizers

Nina Bencheva, University of Ruse, Bulgaria
Yoana Ruseva, University of Ruse, Bulgaria
Diyana Kinaneva, University of Ruse, Bulgaria
Georgi Georgiev, University of Ruse, Bulgaria

Steering Committee

Plamen Zahariev, University of Ruse, Bulgaria
Georgi Hristov, University of Ruse, Bulgaria
Valentina Markova, Technical University of Varna, Bulgaria
Anas AlSobeh, Southern Illinois University,Carbondale, IL, USA
Yahya Tashtoush, Jordan University of Science and Technology, Jordan

Technical Program Committee

Plamen Zahariev, University of Ruse, Bulgaria
Georgi Hristov, University of Ruse, Bulgaria
Valentina Markova, Technical University of Varna, Bulgaria
Brian Hildebrand, Eastern Michigan University, USA
Jon Walatkiewicz, Eastern Michigan University, USA
Izzat Alsmadi, Texas A&M, San Antonio, USA
Firas Al-Balas, Jordan University of Science and Technology, Jordan
Saed Alrabaee, United Arab Emirates University (UAEU), UAE
Ali ALQahtani, Taibah University, Saudi Arabia
Alaa Alslaity, Dalhousie University, Canada
Yaser Jararweh, Jordan University of Science and Technology, Jordan
Yazan A. Alshboul, Prince Sultan University, Saudi Arabia
Sanaa Alwidian, Faculty of Engineering and Applied Science, Canada
Fathi Amsaad, Wright State University, USA
Yahya Tashtoush, Jordan University of Science and Technology, Jordan
Eman Hammad, Texas A&M University-Collage Station, USA
Majdi Maabreh, The Hashemite University, Jordan
Amani Shatanwi, Weber State University, USA
Noor Abu-el-rub, University of Kansas Medical Center, Kansas City, KS, USA
Shorouq Al-Eidi, Tafila Technical University, Jordan

Contact

All questions about submissions, Chairs conatct and helpdesk: info@rev-ai.org