[Event at CIG] [Deadline Extended] NGEN-AI 2026 | The International Conference on Next-Generation AI Systems | Scopus Indexed | A Hybrid Event | Trento, Italy

Mon Jun 1 09:45:50 CEST 2026

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,
Jose Gonzales
On behalf of the NGEN-AI 2026 organizing committee

CFP: The 2026 International Conference on Next-Generation AI Systems
(NGEN-AI 2026)

Springer CCIS Proceedings

https://ngen-ai.org/

*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.

Workshops

NGEN-AI 2026 will also host a set of specialized workshops covering key
emerging areas in next-generation AI systems. Papers accepted in the
workshops will appear in the main conference proceedings:

   - *Security for AI Systems and AI for Systems Security (AISEC 2026)*
   - *AI for Industry (AI4I 2026)*
   - *GEN-AI and Software Engineering (GEN-SE 2026)*
   - *Agentic AI Workflows and Technologies (AAWT 2026)*

Important Dates

   - Paper submission deadline: *June 20, 2026*
   - Notification of acceptance: *July 20, 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:

   - fahed.alkhabbas at mau.se

We look forward to receiving your contributions and to welcoming you at
NGEN-AI 2026 in Trento, Italy!