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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.

AAAI 2026

Modern Methods in Associative Memory

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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.

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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.

Post-training alignment is crucial for refining the reasoning capabilities of Large Language Models (LLMs). A dominant paradigm for this involves optimizing the model's policy using reinforcement learning, powered by techniques such as Proximal Policy Optimization (PPO), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO). The success of these methods, whether using an explicit reward model or optimizing directly on preference data, is critically dependent on the quality of the guiding signal. However, these signals are conventionally derived from task-specific outcomes, such as correctness in math or fluency in summarization. This approach often limits the model's ability to generalize its reasoning skills across diverse domains and can lead to reward hacking or model collapse. This paper challenges this outcome-based paradigm by introducing a conceptual framework, GRIT (Generalizable Reasoning via Intrinsic Traits). This novel framework aims to shift the emphasis from rewarding what the model answers to how it reasons. To accomplish this, we define a set of universal, task-agnostic traits of sound cognition inspired by human reasoning. These intrinsic traits are encoded as distinct reward components: (1) ensuring sequential logical coherence, (2) penalizing cyclic or redundant reasoning, (3) rewarding successful and integrated tool utilization, and (4) maintaining semantic alignment with the user's query. By fine-tuning an LLM to optimize for these intrinsic traits, we hypothesize that the model will develop a more robust and generalizable cognitive process.

Rethinking Reward Models! A Conceptual Framework for Enhancing LLM Reasoning through Intrinsic Traits

Online continual learning (OCL) methods adapt to changing environments without forgetting past knowledge. Similarly, online time series forecasting (OTSF) is a real-world problem where data evolve in time and success depends on both rapid adaptation and long-term memory. Indeed, time-varying and regime-switching forecasting models have been extensively studied, offering a strong justification for the use of OCL in these settings. Building on recent work that applies OCL to OTSF, this paper aims to strengthen the theoretical and practical connections between time series methods and OCL. First, we reframe neural network optimization as a parameter filtering problem, showing that natural gradient descent is a score-driven method and proving its information-theoretic optimality. Then, we show that using a Student’s t likelihood in addition to natural gradient induces a bounded update, which improves robustness to outliers. Finally, we introduce Natural Score-driven Replay (NatSR), which combines our robust optimizer with a replay buffer and a dynamic scale heuristic that improves fast adaptation at regime drifts. Empirical results demonstrate that NatSR achieves stronger forecasting performance than more complex state-of-the-art methods.

Online Continual Learning for Time Series: a Natural Score-driven Approach

Current Large Language Model (LLM) training paradigms, while effective at pattern matching and knowledge retrieval, often fall short of replicating the complex, adaptive and generalizable reasoning characteristic of human intelligence. We argue that this arises from a fundamental disconnect between the static, data-driven training of LLMs and the dynamic, lifelong learning process inherent to human cognitive development. To bridge this gap, we introduce Learn-Master-Teach Tuning (LMT) , a novel, end-to-end training framework that simulates the complete human `student-to-teacher' life-cycle. Our paradigm guides the model through a comprehensive developmental trajectory, from a beginner who is trying to adjust and learn a curriculum, to a seasoned educator capable of lifelong learning and knowledge synthesis. By situating learning within a comprehensive, cognitive-inspired framework, we explore two fundamental research questions: Can the deep simulation of a human persona, in this case, a developing academic, lead to a genuine replication of that persona's capabilities? And, does this student-teacher life-cycle offer a superior training paradigm for fostering robust and generalizable reasoning in LLMs? We present the complete LMT methodology and position it within the landscape of existing training paradigms, arguing that by emulating the human journey of learning, we can unlock new frontiers in artificial general intelligence such as lifelong learning and continual adaptation with history retention. Initial results using small language models in the challenging domain of differential equations already demonstrate significant improvements of about 33% over baselines indicating the transformative potential of the proposed human-pedagogy inspired framework.

Human-Pedagogy Inspired LLM Fine-Tuning Paradigm for Lifelong Leaning and Continual Adaptation

Given the recent tremendous success of large language models (LLMs), there has been an increasing trend in applying them to several down-stream tasks via fine-tuning (FT). However, there persist two significant challenges in FT - 1) curation of high-quality task-specific data and 2) expensive time-consuming model adaptation via gradient descent optimization. To mitigate these limitations, we leverage prior works in large-scale parameter generation for LLMs and propose Self-Optimizing Lifelong Autonomous Reasoner (SOLAR) to open up a new paradigm of parameter-level meta learning, thereby serving a critical advance in the AI Scientist domain. SOLAR is an open-ended autonomous foundation-model-based agent which is capable of self-improvement by discovering and learning the rich meta-knowledge information present in large neural network weights, thereby enabling efficient adaptation of LLMs by parameter-level weight modifications to unseen domains as well. To achieve this, SOLAR has a multi-level reinforcement learning approach to train models for efficient understanding of parameter space and offers higher degree of flexibility compared to prior works on self-evolution by providing freedom to the model for choosing its own adaptation strategy thereby breaking the scaling solely through data regime. Early experiments demonstrate the superior performance of SOLAR in the common-sense reasoning domain as it outperforms task-specific FT by 23.6% on average and even some of the most recent works in parameter generation (by 10.4%), model merging (by 24.3%) and test-time learning (by 25.2%) as well out-of-domain tasks such as coding, social, logical and mathematical reasoning as well.

SOLAR : A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation

Earth Observation (EO) systems generate continuous multimodal data streams at unprecedented scales. However, in this context, the literature offers solutions based on foundation models that operate within static training paradigms, which limit their effectiveness. Trained once on historical datasets and deployed without further learning, these models face critical issues when confronted with the dynamic nature of the environment, which includes emerging phenomena, sensor degradation, and evolving environmental patterns. This vision paper identifies three fundamental gaps: (1) the absence of memory-efficient anti-forgetting mechanisms at the foundation scale, (2) static cross-modal fusion strategies that cannot adapt to changing observational contexts, and (3) temporal representations that fail to distinguish cyclical patterns from distributional drift. Addressing these limitations requires convergence of foundation models, Continual Learning, and Streaming Machine Learning. This work envisions three key research directions: efficient model updating through selective replay and parameter regularization, explicit drift detection mechanisms, and context-dependent fusion strategies. These directions aim to enable EO systems that continuously learn from terabyte-per-day satellite streams while maintaining transfer learning capabilities and computational feasibility essential for operational deployment.

Towards Streaming Continual Learning for Earth Observation Multimodal Foundation Models

In streaming scenarios, models must learn continuously, detect concept drifts, and adapt without erasing previously acquired knowledge. However, existing research communities address these challenges in isolation. Continual Learning (CL) focuses on long-term retention and mitigating catastrophic forgetting, often without strict real-time constraints. Stream Learning (SL) emphasizes rapid, efficient adaptation to high-frequency data streams, but typically neglects forgetting. Recent efforts have tried to combine these paradigms, yet no clear algorithmic overlap exists. We argue that large in-context tabular models (LTMs) provide a natural bridge for Streaming Continual Learning (SCL). In our view, unbounded streams should be summarized on-the-fly into compact sketches that can be consumed by LTMs. This recovers the classical SL motivation of compressing massive streams with fixed-size guarantees, while simultaneously aligning with the experience-replay desiderata of CL. To clarify this bridge, we show how the SL and CL communities implicitly adopt a divide-to-conquer strategy to manage the tension between plasticity (performing well on the current distribution) and stability (retaining past knowledge), while also imposing a minimal complexity constraint that motivates diversification (avoiding redundancy in what is stored) and retrieval (re-prioritizing past information when needed). Within this perspective, we propose structuring SCL around two core principles of data selection for in-context learning: (1) distribution matching, which balances plasticity and stability, and (2) distribution compression, which controls memory size through diversification and retrieval mechanisms.

Bridging Streaming Continual Learning via In-Context Large Tabular Models

Modern industrial monitoring systems must detect anomalies in real time under evolving operating conditions and without reliance on labeled data. Traditional online anomaly detectors offer fast adaptation but struggle when normal behavior shifts or when rare anomalies are unintentionally learned as normal. On the other side, recently introduced foundation models for time series capture richer structure but are computationally expensive for continuous deployment. We propose a dual-learner anomaly detection framework that bridges a fast online learner based on Half-Space Trees with a time-series foundation model (MOMENT) acting as a background learner. A confidence-based routing mechanism determines, for each incoming instance, whether to trust the online model, defer to the foundation model, or combine both through confidence-weighted ensembling. The confidence estimation method is fully unsupervised and robust to drift, requiring no labels or sliding windows. We validate the approach on two real-world elevator (hoist) installations, demonstrating that the system operates efficiently in streaming conditions and matches or surpasses strong online baselines. Furthermore, we show that fine-tuning the foundation model on one installation provides measurable performance gains when transferred to a different installation, indicating that foundation-model adaptation can support cross-site knowledge transfer in industrial monitoring. The results highlight the promise of integrating online learning with foundation models to achieve both responsiveness and robustness in long-term industrial anomaly detection.

Online Learning Supported by Foundation Models for Anomaly Detection in Industrial Settings

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

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.

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Rethinking Reward Models! A Conceptual Framework for Enhancing LLM Reasoning through Intrinsic Traits

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