Singapore

Pattern separation, essential for encoding distinct memories of overlapping contexts, relies on dentate gyrus coding, which is shaped by entorhinal input and strong lateral inhibition. The pattern-separated state space provided by the hippocampus is thought to facilitate striatal-dependent reinforcement learning, enabling associations between sensory features and outcomes. Although synaptic plasticity, value prediction error modulation, and adult neurogenesis have been implicated in this process, their precise contributions remain unclear. To investigate the computational mechanisms underlying pattern separation, we developed neural network models incorporating an entorhinal cortex–dentate gyrus–striatal circuit. Simulations suggest that lateral inhibition is necessary for forming a decorrelated coding subspace, whereas hippocampal plasticity and dopamine modulation are not required for value learning. These findings dissociate neural pattern separation in hidden-layer representations from behavioral discrimination at the model output, highlighting how biologically grounded architectures and learning rules can enhance interpretability.

AAAI 2026

Unsupervised Hebbian Learning Drives Biologically Interpretable Pattern Separation in a Hippocampal–Striatal Network

workshop paper

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>

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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.

In recent years, the challenge of handling out-of-distribution (OOD) data has become central to ensuring the reliability of artificial intelligence deployed in open-world environments. This half-day tutorial at AAAI’26 is designed for students, researchers, and practitioners seeking to advance the machine learning models by providing an integrated overview of OOD detection and OOD generalization. The tutorial will introduce foundational concepts and theoretical principles, survey advanced methodologies across post-hoc scoring, outlier exposure, representation learning, as well as causality-inspired approaches, and highlight challenges together with emerging solutions. Real-world case studies across different domains will demonstrate the critical importance of addressing OOD risks in practice, while discussions will emphasize the distinctions and synergies between detection and generalization as well as open research directions. To participate effectively, attendees are expected to have basic familiarity with linear algebra, probability, and fundamental machine learning concepts, though the content is designed to remain accessible and emphasize intuitive understanding over technical formalism.

Handling Out-of-Distribution Data in the Open World: Principles and Practice for Reliable AI

In this tutorial, we chart a practical path from raw capability to trustworthy reasoning with foundation models. We begin by motivating why trustworthy reasoning is essential: when models bluff multiplications or invent drug interactions, their value collapses and risks increase. We adopt four pillars of trustworthiness, i.e., capability, safety, robustness, and explainability, as the organizing framework for the entire session.

In Part I, we trace the evolution from early language models to today’s foundation models that produce extended chains of thought and act in the world. Through concrete case studies, we dissect jailbreaks, hallucinations, and brittle logic, and we connect these failure modes to regulatory pressure such as the EU AI Act. The takeaway is clear: we must design for trustworthy reasoning from the outset, especially in high-stakes domains such as clinical or financial decision-making.

In Part II, we move from leaderboards to a science of measurement. We show how to build reliable, valid evaluations using psychometric tools, including item response theory, amortized evaluation, and predictability analysis. We implement three open-source pipelines hands-on: TruthfulQA for hallucination detection, HellaSwag for robustness testing, and MATH with formal-verification hooks in Lean4. Along the way, we demonstrate red-teaming stress tests and reasoning-trace metrics that surface subtle errors leaderboards miss, and we practice calibration, dataset curation, and transparent reporting for honest progress tracking.

In Part III, we deliver a compact methodology for trustworthy machine reasoning. We cover training-free prompting methods (chain-of-thought, retrieval-augmented generation, constrained decoding), post-training algorithms (supervised fine-tuning, RLHF, verifiable rewards, self-reward), and test-time techniques (self-consistency, reflection, tree search, tool-augmented verification). We introduce guardrails—safe sampling and semantic filters—that reduce risk without crippling capability. For each technique, we map effects to the four pillars, highlight trade-offs and failure signatures, and summarize when to combine methods for maximum leverage.

In Part IV, we turn to deployment. We walk through real-world agents and workflows, e.g., Lean4-based code verification assistants and bioinformatics pipelines proposing candidate compounds. We share step-by-step recipes, failure checklists, and diagnostics so participants can preserve trust while shipping. We also outline governance artifacts—risk registers, evaluation cards, and incident playbooks—that align technical practice with policy expectations.

We emphasize open, reproducible assets and decision rubrics that translate research into dependable products. Our goal is simple: help you move from compelling demos to trustworthy systems that earn and deserve user trust.

All materials will be available at: https://trustworthy-machine-reasoning.github.io/

Trustworthy Machine Reasoning with Foundation Models

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/.

Evolution of Neural Networks

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

Although many LLM-based agents have recently been developed, progress on agents that are capable of solving complex, real-world problems is severely limited. One such important and challenging area is that of addressing IT management tasks, including solving IT incidents, which often require extensive human expertise and effort. To enable the development of agents for these tasks, in this lab, we introduce IT-Bench, an open benchmark for IT automation that simulates realistic environments where agents interact with IT systems and multi-modal operational data, including logs, metrics, alerts, and traces. ITBench provides a two- tiered benchmark: a static dataset (ITBench_static) that closely mirrors the live, gym-like environment (ITBench_live). Together, they provide a testbed for benchmarking agentic systems across a host of critical challenges, such as planning and reasoning over massive and heterogeneous IT data, safety, and stochasticity in live IT systems. Through demonstrations, participants will learn all of these challenges. ITBench_static is more beginner-friendly, enabling rapid benchmarking of agents against a smaller set of unique IT-domain challenges. ITBench_live enables more advanced researchers and practitioners familiar with IT systems to develop and test their agents against the full suite of challenging IT problems. We will guide attendees to use both ITBench_static and ITBench_live to develop baseline multi-agent systems and benchmark their performance. No prior IT or site reliability engineering (SRE) experience is required.

Developing AI Agents for IT Automation Tasks with ITBench

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

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.

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