Singapore

Neuroevolution, or optimization of neural networks through evolutionary computation, is a method for constructing intelligent agents through population-based search. It is particularly useful in partially observable domains with sparse and multiobjective reinforcement; compared to other policy search techniques, its power comes from extensive exploration that allows it to find effective, often surprising solutions.   Prime application domains include robotic control, game-playing agents, and decision-making. More recently, it has also been extended to optimizing deep-learning architectures, understanding how biological intelligence evolved, and optimizing neural networks for hardware implementation.  Several synergies have also emerged with reinforcement learning and large language models. This tutorial introduces participants to the basics of neuroevolution, progresses to several advanced topics that make neuroevolution effective and general, reviews example application areas, and proposes further research questions. An optional hands-on exercise makes these concepts concrete and allows the participants to take advantage of neuroevolution immediately.  For more details, see https://www.cs.utexas.edu/~risto/talks/aaai26-tutorial/.

AAAI 2026

Evolution of Neural Networks

tutorial

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

To access this event page, you need to log in with the **email address you registered with**. <br>Access credentials will be sent to your email from Underline -  subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Graph anomaly detection (GAD), which aims to identify rare observations in graphs, has attracted rapidly increasing attention in recent years due to its significance in a wide range of high-impact application domains such as abusive review detection and malicious behavior detection in online shopping applications, web attack detection, and suspicious activity detection in online/offline financial services. A foundation model on GAD refers to a generalist model trained on specific graph data, enabling it to generalize effectively across different domains and tasks. In recent years, such models have attracted increasing attention due to their ability to provide strong zero-shot and few-shot performance without task-specific retraining. By learning domain-invariant and transferable representations across tasks, a GAD foundation model can be readily adapted to new anomaly detection scenarios, making it applicable to a wide range of use cases such as privacy-preserving anomaly detection, transferable cybersecurity and threat detection, and cross-platform anomaly detection in social network.

In this tutorial, we aim to present a comprehensive review of deep learning methods specifically designed for GAD and foundation models for detecting abnormal activities on graphs. Specifically, we will first elaborate on the key concepts and taxonomies in GAD. Then review popular state-of-the-art deep anomaly detection methods from various perspectives of methodology design on graph data, including GNN backbone design, proxy task design, and anomaly measures. Then we will establish the connection between conventional methods and foundation models on GAD, highlighting how recent advancements build upon or differ from conventional approaches. Following this, we will provide a comprehensive overview of existing foundation models that have been proposed for detecting abnormal activities on graphs from cross-domain and cross-task, respectively. We will discuss their underlying principles, design choices, and effectiveness across various settings. The future directions will be finally presented to help researchers gain a deep understanding of this area and promote more high-quality research and real-world applications in the future.  The webiste of this tutorial is  https://sites.google.com/view/aaai26-tutorial-gad/home?read_current=1

Toward Foundation Models for Detecting Abnormal Activities on Graphs

LH03: The Verification of Neural Networks Competition (VNN-COMP): A Lab for Benchmark Proposers, Verification Tool Participants, and the Broader AI Community

How to find a natural grouping of a large real data set? Clustering requires a balance between abstraction and representation. To identify clusters, we need to abstract from superfluous details of individual objects, such as background or lighting in images. But we also need a rich representation that emphasizes the key features shared by groups of objects that distinguish them from other groups of objects. Each clustering algorithm implements a different trade-off between abstraction and representation. Classical K-means implements a high level of abstraction – details are simply averaged out – combined with a very simple representation – all clusters are Gaussians in the original data space. We will see how approaches to subspace and deep clustering support high-dimensional and complex data by allowing richer representations. However, with increasing representational expressiveness comes the need to explicitly enforce abstraction in the objective function to ensure that the resulting method performs clustering and not just representation learning. We will see how current deep clustering methods define and enforce abstraction through centroid-based and density-based clustering losses. Balancing the conflicting goals of abstraction and representation is challenging. Ideas from subspace clustering help by learning one latent space for the information that is relevant to clustering and another latent space to capture all other information in the data. The tutorial ends with an outlook on future research in clustering. Future methods will more adaptively balance abstraction and representation to improve performance, energy efficiency and interpretability.
This tutorial is for machine learning researchers and professionals interested in learning more about clustering high-dimensional data. Practitioners will receive an overview of different approaches to clustering high-dimensional data, along with insights into their benefits and limitations. This knowledge will enable them to select an appropriate method for their problem. Researchers will find starting points for contributing to the topic. We will illustrate foundational and current approaches with Python code examples. We will summarize the evaluation methodology and provide pointers to benchmark data. We will also highlight open problems that require further research. This tutorial is a starting point for actively contributing to this active and fascinating research topic. To illustrate, we will use real use cases from collaborative projects in biology, neuroscience, and archeology, in addition to benchmark data. Basic knowledge in machine learning, data mining, linear algebra and Python programming is beneficial but not required.

Website: https://dm.cs.univie.ac.at/research/aaai26/

Clustering High-dimensional Data: Balancing Abstraction and Representation

Associative Memories (AMs) like Hopfield Networks are elegant models for describing fully recurrent neural networks that store and retrieve information. Recent theoretical advances have revealed deep connections between AMs and modern AI architectures such as Transformers and diffusion models, opening new possibilities for interpreting and designing neural networks. This tutorial provides an approachable introduction to AMs through three components: theoretical foundations from classical to Dense Associative Memories, hands-on implementations using a universal Lagrangian framework for designing AMs (including coding Transformers as AMs), and practical applications connecting AMs to kernel methods, density estimation, and deep clustering.

We provide a technical, hands-on level tutorial for the general attendee of AAAI i.e., graduate students, industry researchers, faculty, and developers. However, we expect our tutorial to additionally appeal to physicists, neuroscientists, and other researchers interested in brain-inspired models of computation. From a student’s perspective, the ordered priorities of this tutorial are to: (1) build intuition, (2) understand the fundamental tooling needed to work with these systems, and (3) engage with simple proofs that highlight their elegant behavior.

The tutorial is accompanied by a website containing links to all lecture notes, code examples, and presentation slides.

Modern Methods in Associative Memory

TH08: Algorithms and Systems for Efficient Inference in Generative AI
State-of-the-art Generative AI (GenAI) models now span diverse modalities including language, vision, and audio, and are being deployed across an increasingly wide range of applications. However, their growing size and complexity pose significant challenges for efficient inference, particularly in real-time or resource-constrained settings. This tutorial will introduce participants to a range of techniques that enable high-performance, scalable inference without compromising model accuracy. It will cover practical tools, open-source serving frameworks, and deployment considerations across different hardware platforms, and the underlying principles will be broadly applicable to emerging model architectures. These methods will be illustrated through concrete scenarios, such as low-latency serving of large language models (LLMs), on-device deployment of quantized models, and performance tuning for cost-sensitive applications.

The tutorial is organized into three sessions that collectively build a comprehensive understanding of efficient inference for GenAI models. Session 1 provides an overview of the diverse architectural classes of GenAI models, ranging from decoder-only LLMs to multimodal models. This is followed by an introduction to the multi-layered inference stack, spanning high-level ML frameworks such as PyTorch to low-level hardware instructions — an understanding of which is essential for systematically uncovering performance bottlenecks. This will be accompanied with a demonstration of profiling techniques to analyze these bottlenecks. Session 2 dives into algorithmic, modeling, and systems-level optimizations, covering techniques like efficient attention variants, quantization, speculative decoding, and kernel fusion — while examining how each of these techniques alleviates the bottlenecks discussed in Session 1. Session 3 concludes the tutorial by focusing on practical tools and frameworks that implement many of the previously discussed optimization techniques with accompanying demos. It will highlight vLLM for high-throughput LLM serving, and TensorRT for hardware-optimized compilation across modalities. Together, these sessions will equip participants with the technical depth and tooling needed to deploy GenAI models efficiently in real-world systems.

This tutorial is designed for researchers and practitioners in AI who are interested in understanding and improving the efficiency of GenAI models. It will be particularly valuable for those with expertise in a specific layer of the inference stack, such as modeling, quantization, or infrastructure, and are seeking to develop a holistic perspective connecting algorithmic techniques with system-level and hardware-aware optimizations. Attendees are expected to have basic familiarity with deep learning frameworks (e.g., PyTorch). Prior experience with LLM architectures and deployment is beneficial, but not required.

Algorithms and Systems for Efficient Inference in Generative AI

As generative AI systems such as large language models (LLMs), diffusion models, and vision–language models become widely deployed, the ability to “unlearn” sensitive or harmful information has become essential for AI safety, compliance, and trust. Machine unlearning (MU) enables models to selectively forget undesirable knowledge, ranging from private data and copyrighted content to hazardous instructions, but current methods remain fragile and easily reversed. This tutorial offers a structured exploration of the science and practice of machine unlearning, highlighting both recent advances and persistent challenges. We begin by motivating the need for MU through real-world safety applications, then clarify what distinguishes faithful forgetting from shallow suppression. We review major approaches, such as data-driven, model-based, and input-driven, and examine their tradeoffs in effectiveness, efficiency, and robustness. A central focus will be the optimization foundations of unlearning, where we present principled formulations that balance forgetting with utility preservation and enhance its robustness. We also uncover critical vulnerabilities, showing how jailbreak prompts, low-shot fine-tuning, or quantization can reactivate forgotten knowledge, and connect these failure modes to broader themes in adversarial machine learning. In addition to the foundations of MU, participants will gain practical insights through exposure to public unlearning benchmarks and toolkits. Walkthroughs of representative MU pipelines will illustrate common failure modes, stress-testing strategies, and evaluation protocols. The tutorial concludes with open research directions, including interpretability, verification, compliance-aware deployment, and unlearning for reasoning-enabled AI models and agents. Designed for a broad audience, the tutorial requires no prior familiarity with unlearning. All key concepts will be introduced with explanations, intuitive illustrations, and references. By bridging optimization, adversarial robustness, and AI safety, this tutorial charts a principled roadmap toward AI systems that can forget efficiently, robustly, and responsibly.

When AI "Forgets" for Good: The Science and Practice of Machine Unlearning for AI Safety — Progress, Pitfalls, and Prospects

The last few years have seen the rapid development of machine learning methods for natural language processing, computer vision, and scientific discovery. Many recently developed tools and cutting-edge methodologies are from the theory of optimal transport (OT) and have been particularly successful for various applications, e.g., the model architectures and learning algorithms driven by theoretical and computational optimal transport techniques. In this tutorial, we plan to introduce the AAAI community to recent advances in theoretical and computational optimal transport techniques and their applications in designing new model architectures and learning algorithms, especially those proven to be very effective in challenging tasks but are not yet well-known.

The content of the tutorial relates to several areas within the AAAI community, such as statistical machine learning, optimization algorithms, neural network design and analysis, and various cutting-edge machine learning applications, including multi-modal representation learning, structured data (like graphs and point clouds) modeling, and high-dimensional data generation. The target audience of this tutorial includes but is not limited to computer scientists, mathematicians, machine learning researchers, and multidisciplinary practitioners, from both theoretical and applied communities, and at both junior and senior levels. The participants will learn about theoretical fundamentals, practical implementations, and innovative applications of optimal transport-driven machine learning techniques, the most recent advances of related topics, as well as open problems and directions for future work.

Optimal Transport-Driven Machine Learning: Techniques and Applications

In recent years, multi-modal time series analysis has gained increasing attention for integrating insights from diverse data sources, enhancing both predictive performance and explanations across various domains. This growing interest is driven by the rapid production of heterogeneous data in the real world, where temporal signals often come not only in numerical form but are also accompanied by complementary modalities such as text, images, or structured metadata. By effectively leveraging information from different modalities, researchers can discover richer patterns and improve model performance in the application. We plan to present a comprehensive half-day tutorial at AAAI 2026, tailored for researchers and practitioners interested in multi-modal time series analysis. This tutorial provides insights into the theoretical and practical aspects of multi-modal time series, covering data characteristics and cross-modality modeling strategies for various downstream tasks. Attendees will also learn best practices for applying multi-modal time series analysis to real-world domains such as finance, healthcare, and transportation. Through these examples, participants will gain a clearer understanding of how to move from theoretical modeling to impactful deployment. The tutorial offers a comprehensive and in-depth understanding, practical skill development, and networking opportunities, connecting theory with real-world applications.

Multi-modal Time Series Analysis: Methods, Datasets, and Applications

Deterministic search and planning typically operate on graphs where each edge has a given cost, aiming to find a path of minimal cost. Heuristic search has developed tools for solving this path-finding problem, including the A* algorithm. However, the objective of search and planning is often much more complex in real life, for example, because one needs to trade-off between different costs. Multi-objective search and planning operate on graphs where each edge has two or more given costs, aiming to find paths that trade-off between the different costs in the form of a Pareto frontier of paths.

For example, transporting hazardous material requires trading off different costs for each street, such as its length and the number of residents that would be exposed to the hazardous material in case of an accident. Other examples include route planning for vehicles and robots (which involves trading off energy consumption and travel time), planning power transmission lines (which involves trading off power-generation cost and power loss), and scheduling satellites and routing packets in computer networks (which involves trading off profit and fairness).

This tutorial will provide an overview of the multi-objective search problem and summarize recent progress in this fast-moving research area. We will cover theoretical foundations, practical algorithms, and challenges that commonly arise in practice in a way that is accessible to all AI researchers and students. Our target audience is anyone interested in search and planning who wants to learn about this fascinating emerging field. More information on the tutorial and a detailed schedule can be found at https://sites.google.com/usc.edu/aaai24-mos-tutorial/home

Recent Advances in Multi-Objective Search

This tutorial provides a comprehensive and unified overview of the rapidly evolving field of LLM-based Multi-Agent Systems (LaMAS). As LLMs become more capable as autonomous agents, the next frontier is orchestrating their collective intelligence to solve complex problems. This tutorial systematically bridges the foundational principles of multi-agent reinforcement learning (MARL) and game theory with the practical design and engineering of modern LaMAS.

The tutorial begins with the theoretical underpinnings of multi-agent interaction before moving to the core components of a modern LLM agent, including reasoning, planning, and tool use. The central part delves into LaMAS architectures, covering communication protocols, coordination strategies, and collaborative optimization. The tutorial concludes by exploring the frontiers of the field, discussing evaluation methodologies, open-source frameworks, and real-world applications. Attendees will gain a robust theoretical understanding, a map of the current research landscape, and the practical knowledge needed to build and evaluate their own LaMAS.

Content not yet available

Next from AAAI 2026

Toward Foundation Models for Detecting Abnormal Activities on Graphs

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES