[Event at CIG] CFP: The 2026 International Conference on Next-Generation AI Systems | SCOPUS Indexed | Trento, Italy

[FLICS]

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

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 (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
Important Dates

Paper Submission Deadline:       May 25, 2026

Notification of Acceptance           July 15, 2026

Camera-ready Submission           August 10, 2026

All deadlines are in Anywhere on Earth (AoE) time.
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

The Federated Learning track focuses on learning paradigms that enable
models to be trained collaboratively across devices and organizations
without centralizing raw data. We are particularly interested in approaches
that push computation closer to the edge while preserving privacy,
robustness, and scalability in realistic deployments.

Contributions may address algorithms for heterogeneous and non-IID data,
personalization strategies, communication-efficient protocols, secure
aggregation and differential privacy, resource-aware federated learning on
constrained hardware, as well as demonstrators and case studies in domains
such as healthcare, finance, smart cities, and industrial IoT.

·       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

This track targets advances in small and large language models (SLMs and
LLMs) and other generative AI models that synthesize and reason over text,
code, images, audio, and multimodal data. We welcome work on model
architectures, training strategies, and deployment techniques that improve
performance, controllability, safety, and efficiency of foundation and
generative models in real-world settings.

Relevant contributions span from core modelling and optimization to
evaluation, alignment, and application-driven studies, including systems
that tightly integrate language and generative models with external tools,
data sources, and complex software architectures.

·       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

This track focuses on advances in deep learning architectures and
representation learning methods that underpin next generation AI systems.
We invite contributions that improve expressiveness, robustness,
interpretability, and efficiency across supervised, unsupervised, and
self-supervised learning paradigms.

We particularly encourage work that bridges novel architectures with
deployment constraints, addresses data scarcity and bias, or opens up new
application domains.

·       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

This track concentrates on agentic AI systems that perceive, reason, plan,
and act over extended time horizons-often in dynamic environments and in
collaboration with humans or other agents. We are interested in both
theoretical foundations and practical deployments of autonomous and
semi-autonomous agents in digital and physical settings.

We particularly encourage submissions that connect planning and decision
making with learning, perception, and interaction, and that critically
examine the reliability, safety, and societal impact of 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

This track addresses the engineering and operational aspects of building,
deploying, and maintaining AI systems in production. It covers the full
lifecycle from data and model pipelines to monitoring, governance, and
socio-technical considerations in organizations.

We invite contributions that connect software engineering, DevOps, and
platform engineering practices with the unique requirements of machine
learning and foundation models.

·       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

This track focuses on methods and practices that make next generation AI
systems understandable, transparent, and auditable for humans. We welcome
contributions that improve how AI systems explain their decisions and
behaviors, enabling trust, accountability, and effective human oversight in
real-world deployments.

We encourage work spanning intrinsic interpretability and post-hoc
explanations, explanation quality evaluation, and human-centered design of
explanations, including applications to foundation models, agentic systems,
and distributed AI settings.

·       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

This track focuses on the qualities that enable next generation AI systems
to be adopted and relied upon in society: trustworthiness, responsibility,
and sustainability. We welcome contributions that align AI systems with
human values and public expectations, ensuring they are safe, fair,
transparent, and robust across real-world contexts.

We particularly encourage work that connects technical advances with
governance and socio-technical practices, including evaluation
methodologies and lifecycle approaches that reduce risk and environmental
impact while improving accountability.

·       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

This track focuses on systems, architectures, and hardware platforms that
enable efficient and sustainable execution of next generation AI workloads.
Submissions may address full-stack co-design from algorithms and compilers
down to accelerators and distributed infrastructures.

We particularly welcome work that bridges AI models with constraints of
real-world platforms, including edge devices, heterogeneous clusters, and
specialized hardware.

·       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

This track brings together application-driven research and critical
perspectives on the impact of next generation AI systems on individuals,
organizations, and society. We welcome studies that combine technical
innovation with domain insights, as well as empirical analyses of adoption,
impact, and governance.

Interdisciplinary work at the intersection of AI, human-computer
interaction, social sciences, and policy is particularly encouraged.

·       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.
Submission Portal

Submissions are handled via EasyChair. For submission guidelines and the
submission link, please visit: https://ngen-ai.org/index.php

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