CFP | The 2026 International Conference on Next-Generation AI Systems (NGEN-AI 2026) | Scopus Indexed | A Hybrid Event | Trento, Italy
Fahed Alkhabbas
Dear Colleague,
I am pleased to invite you to submit your valuable research to the 2026 International Conference on Next-Generation AI Systems (NGEN-AI 2026).
NGEN-AI 2026 brings together researchers, practitioners, and industry leaders working on the next wave of artificial intelligence, including foundational models, generative AI, agentic AI, federated learning, deep learning, explainable and trustworthy AI, and edge/cloud AI systems.
Please find the Call for Papers below for full details. We look forward to receiving your submission and hope to welcome you to Trento, Italy.
Best regards,
Fahed Alkhabbas
On behalf of the NGEN-AI 2026 organizing committee
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You are receiving this invitation because your published work appears relevant to the conference scope.
CFP: The 2026 International Conference on Next-Generation AI Systems (NGEN-AI 2026)
Springer CCIS Proceedings
Theme: One conference for every AI direction: Foundational models, Generative, Agentic, Federated, and Deep Learning, XAI, Trust, and Edge Intelligence.
Venue: Trento, Italy Dates: 1–4 September 2026
Scope
We invite high-quality, original contributions that advance the theory, engineering, and real-world impact of Next Generation AI Systems—spanning federated and distributed intelligence; small, large, and generative models; agentic and interactive AI; deep learning and representation learning; explainability and transparency; trustworthy, responsible, and sustainable AI; MLOps and lifecycle management; AI systems and infrastructures; and application-driven research with societal impact.
NGEN-AI 2026 brings together researchers, practitioners, and industry leaders working on the next wave of artificial intelligence. The conference provides a platform for interdisciplinary collaboration, bridging theoretical foundations and practical implementations in intelligent, trustworthy, and sustainable AI systems deployed across diverse domains and real-world environments.
Indexing
All accepted papers will be published in the Springer CCIS series, indexed in leading databases including SCOPUS, Norwegian Register for Scientific Journals and Series, DBLP, EI Compendex, INSPEC, SCImago, zbMATH, and the Japanese Science and Technology Agency (JST).
General Chairs
- Marco Roveri, University of Trento, Italy
- Sadi Alawadi, Blekinge Institute of Technology, Sweden
Topics of Interest
The NGEN-AI conference welcomes research, experience, and vision papers that explore foundational methods, systems, and applications of next generation AI. Topics of interest for each track include, but are not limited to, the following.
Federated Learning
- Architectures for cross-device and cross-silo federated learning
- Federated optimization under non-IID, sparse, or unbalanced data distributions
- Personalized and on-device adaptation strategies in federated settings
- Communication-efficient FL (compression, sparsification, update scheduling)
- Privacy-preserving FL: secure aggregation, differential privacy, homomorphic encryption
- Robustness to poisoning, backdoor, and Byzantine attacks in federated scenarios
- Energy- and resource-aware FL on mobile, edge, and IoT devices
- Federated learning in vertical, horizontal, and hybrid data partitioning settings
- Federated analytics and federated evaluation techniques
- MLOps for FL: lifecycle management, monitoring, and deployment at scale
- Benchmarking, simulators, datasets, and reproducibility studies for FL
- Real-world applications in healthcare, finance, smart industry, and smart cities
- Regulatory, ethical, and governance aspects of federated and collaborative learning
Small & Large Language Models and Generative AI
- Architectures and training recipes for SLMs, LLMs, and foundation models
- Pre-training, instruction-tuning, alignment (e.g., RLHF, DPO, preference optimization)
- Domain-specific and compact SLMs for on-device and resource-constrained settings
- Prompt engineering, in-context learning, function calling, and tool-augmented pipelines
- Retrieval-augmented generation and knowledge-grounded generative models
- Generative models for text, code, images, audio, video, and multimodal content
- Model compression, distillation, quantization, and sparsity for efficient deployment
- Edge and on-device deployment of SLMs/LLMs and generative models
- Safety, robustness, and red-teaming of generative systems (toxicity, hallucinations, bias)
- Evaluation methodologies, benchmarks, and human-in-the-loop assessment
- Generative AI for scientific discovery, simulation, and data augmentation
- Software engineering with LLMs: code generation, refactoring, testing, and verification
- Governance, transparency, IP, and regulatory aspects of foundation and generative models
Deep Learning Architectures & Representation Learning
- Novel neural architectures (transformers, graph neural networks, diffusion models, etc.)
- Self-supervised, contrastive, and representation learning at scale
- Multimodal learning and fusion of heterogeneous data sources
- Curriculum learning, meta-learning, and continual / lifelong learning
- Robust and certified deep learning under distribution shift and adversarial attacks
- Interpretable and explainable deep learning methods
- Data-centric AI: dataset curation, quality, and augmentation strategies
- Efficient training and inference: pruning, low-rank adaptation, and sparse models
- Neural architecture search and automated model design
- Applications of deep learning in vision, language, time series, recommender systems, and beyond
Agentic AI
- Architectures for autonomous, semi-autonomous, and mixed-initiative agents
- Planning, reasoning, and long-horizon decision making for agentic systems
- Reinforcement learning, hierarchical RL, and model-based control for agents
- LLM-driven agents, tool-using agents, and workflow / task orchestration
- Multi-agent systems: coordination, negotiation, communication, and cooperation
- Human-agent interaction, explainability, and trust in agentic AI systems
- Safety, verification, alignment, and oversight for autonomous agents
- Simulation environments, digital twins, and benchmarks for agentic AI
- Agents in robotics, autonomous vehicles, logistics, smart grids, and IoT environments
- Social, economic, and ethical implications of pervasive agentic AI
- Engineering methodologies, software frameworks, and tooling for large-scale agent systems
- Hybrid symbolic-subsymbolic approaches for reasoning and acting
MLOps, AI Engineering & Lifecycle Management
- MLOps platforms and infrastructure for scalable training and deployment
- CI/CD for ML, continuous training, and continuous evaluation
- Data and feature management: data versioning, feature stores, and lineage tracking
- Monitoring, observability, and incident response for AI systems
- Model governance, risk management, and compliance (e.g., AI Act, sectoral regulation)
- Testing, debugging, and quality assurance for ML components and pipelines
- Infrastructure for serving LLMs and generative models at scale
- Cost- and energy-aware deployment and scheduling of AI workloads
- Organizational processes and roles for AI/ML teams
- Case studies and lessons learned from real-world AI production deployments
Explainable AI (XAI) & Transparency
- Post-hoc explanations (e.g., feature attribution, saliency, local surrogate models)
- Intrinsic interpretability and transparent model design
- Counterfactual and contrastive explanations
- Uncertainty estimation, calibration, and communicating confidence to users
- Explainability for LLMs and generative AI (faithfulness, grounding, rationale analysis)
- Explainability in federated, privacy-preserving, and edge AI settings
- Explainable decision making for agentic and multi-agent systems
- Human-centered explanation design, usability, and user studies
- Evaluation and benchmarking of explanations (faithfulness, robustness, usefulness)
- Auditing, debugging, and root-cause analysis for AI systems
- Transparency documentation (e.g., model cards, datasheets) and reporting standards
- Regulatory, ethical, and governance aspects related to transparency and explainability
Trustworthy, Responsible & Sustainable AI
- Trustworthiness by design: safety, reliability, and robustness under distribution shift
- Fairness, bias mitigation, and inclusive AI across populations and contexts
- Accountability, transparency, and auditability in AI systems
- Human values and alignment: human-centered objectives, oversight, and control
- Responsible AI governance: policies, risk management, and compliance practices
- Privacy, security, and protection against adversarial and data poisoning attacks
- Evaluation frameworks, metrics, and benchmarks for trustworthy and responsible AI
- Monitoring and lifecycle management for responsible AI in production
- Sustainable AI: energy-efficient training/inference, green AI, and carbon-aware operation
- Responsible data practices: provenance, consent, documentation, and data stewardship
- Socio-technical studies of AI adoption, impact, and organizational readiness
- Case studies and lessons learned from responsible and sustainable AI deployments
AI Systems, Hardware & Edge/Cloud Infrastructures
- Distributed and parallel systems for large-scale training and inference
- Scheduling and placement of AI workloads across edge, fog, and cloud
- Hardware accelerators (GPUs, TPUs, NPUs, FPGAs) and co-design for AI
- Systems support for LLMs and foundation models (sharding, offloading, caching)
- Energy-efficient and green AI computing, including carbon-aware orchestration
- Runtime systems, compilers, and libraries for AI workloads
- Edge AI and embedded AI for IoT, CPS, and real-time applications
- Resilience, fault tolerance, and reliability of AI systems and infrastructures
- Benchmarks, performance analysis, and optimization of AI systems
Applications & Societal Impact of Next Generation AI
- Next generation AI applications in healthcare, finance, education, mobility, and industry
- AI for sustainability, climate, energy, and environmental monitoring
- Human-AI collaboration, co-creation, and augmented decision making
- Fairness, accountability, transparency, and ethics in AI systems
- Regulation, standards, and governance frameworks for AI
- Socio-technical analyses of AI deployment and organizational transformation
- User studies, field deployments, and longitudinal evaluations
- Public sector and civic applications of AI (e-government, public services, smart cities)
- Education, upskilling, and capacity building for AI-literate societies
Submission Types
- Long Papers (16 pages): original research with clear methodology, results, and contributions.
- Short Papers (8 pages): short research contributions, focused studies, and demo or artifact papers.
- Poster Papers (6 pages): concise presentations of work in progress and undergraduate research.
Important Dates
- Paper submission deadline: May 25, 2026
- Notification of acceptance: July 15, 2026
- Camera-ready submission: August 10, 2026
- Conference dates: September 1–4, 2026 (Trento, Italy)
All deadlines are in Anywhere on Earth (AoE) time.
Submission Portal
Submissions are handled via EasyChair.
For submission guidelines and the submission link, please visit: https://ngen-ai.org/index.php
Contact Information
For questions about submissions, please contact me:
We look forward to receiving your contributions and to welcoming you at NGEN-AI 2026 in Trento, Italy!