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Evaluating robustness under temporal distribution shift remains an open challenge. Existing metrics quantify the average decline in performance, but fail to capture how models adapt to evolving data. As a result, temporal degradation is often misinterpreted: when accuracy declines, it is unclear whether the model is failing to adapt or whether the data itself has become inherently more challenging to learn. In this work, we propose three complementary metrics to distinguish adaptation from intrinsic difficulty in the data. Together, these metrics provide a dynamic and interpretable view of model behavior under temporal distribution shift. Results show that our metrics uncover adaptation patterns hidden by existing analysis, offering a richer understanding of temporal robustness in evolving environments.

AAAI 2026

Tracking Adaptation Time: Metrics for Temporal Distribution Shift

technical 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 the Multi-Agent Path Finding (MAPF) problem, the aim is to find collision free paths for multiple agents. MAPF has many practical applications and has spawned massive research interest in the past two decades. Most MAPF research assumed that every agent is assigned a target it must reach. This assumption often does not hold in several key applications such as automated warehouses and parking lots, where some agents are assigned targets to reach, and others, denoted as unassigned agents, can either stay idle or move to clear the way for the assigned agents. In this paper we introduce this important problem, explain its uniqueness and encourage the entire community to work on it.

Multi-Agent Path Finding with Unassigned Agents (MAPFUA)

This report explores the evolution and current state of
neuro-
symbolic artificial intelligence, an approach that
integrates
neural network capabilities with symbolic reasoning. We
trace the historical context from early AI aspirations to
modern implementations and successes, highlighting key
paradigms, and other logical and semantical considerations.
We argue against the “scaling is all you need” hypothesis,
and point to persistent challenges in reliable symbolic
reasoning with deep and large models. We conclude by
suggesting
that despite numerous implementation choices and the ”broad
church” nature of neuro-symbolic AI, these approaches offer
the most promising path towards AI systems that combine
pattern recognition with robust reasoning, particularly for
applications requiring structured knowledge,
explainability, and
trustworthiness.

The Future Is Neuro-Symbolic: Where Has It Been, and Where Is It Going?

Effectively handling long contexts is challenging for Large Language Models (LLMs) due to the rarity of long texts, high computational demands, and substantial forgetting of short-context abilities. Recent approaches have attempted to construct long contexts for instruction tuning, but these methods often require LLMs or human interventions, which are both costly and limited in length and diversity. Also, the drop in short-context performances of present long-context LLMs remains significant. In this paper, we introduce Flora, an effortless (human/LLM-free) long-context construction strategy. Flora can markedly enhance the long-context performance of LLMs by arbitrarily assembling short instructions based on categories and instructing LLMs to generate responses based on long-context meta-instructions. This enables Flora to produce contexts of arbitrary length and scale with rich diversity, while only slightly compromising short-context performance. Experiments on Llama3-8B-Instruct and QwQ-32B show that LLMs enhanced by Flora excel in three long-context benchmarks while maintaining strong performances in short-context tasks.

Flora: Effortless Context Construction to Arbitrary Length and Scale

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

Knowledge distillation (KD) is a technique that transfers the knowledge from a teacher model to a student model, where the teacher is usually larger and more powerful. In this tutorial, we will briefly introduce the basic concepts, including intermediate-layer matching and prediction-matching KD. We then dive into the challenges and opportunities of KD with sequential data, which lead to advanced techniques such as reinforcement learning KD and multi-teacher KD. We will also cover practical KD applications such as LLM sequence compression and LLM self-distillation. The goal of this tutorial is to provide participants with a comprehensive understanding of the techniques and applications of KD for language models.

Homepage: https://manga-uofa.github.io/aaai-llm-kd/ 

Knowledge Distillation for Language Models: Challenges and Opportunities with Sequential Data

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

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

Computational Pathology Foundation Models (CPathFMs) have emerged as a transformative approach for automating histopathological analysis by leveraging self-supervised learning on large-scale, unlabeled whole-slide images (WSIs). These models, categorized into uni-modal and multi-modal frameworks, facilitate tasks such as segmentation, classification, biomarker discovery, and prognosis prediction. However, the development of CPathFMs faces significant challenges, including limited dataset availability, domain-specific adaptation requirements, and the absence of standardized evaluation benchmarks. This tutorial will provide a comprehensive overview of the current state of CPathFMs, covering key datasets, adaptation strategies such as contrastive learning and multi-modal integration, and a taxonomy of evaluation tasks. We will discuss how these models are trained, fine-tuned, and assessed, addressing the critical gaps in generalization, bias mitigation, and clinical applicability. Additionally, we will explore emerging research directions in fairness, transparency, security, and standardization of evaluation protocols. This tutorial will serve as an essential resource for researchers, clinicians, and AI practitioners looking to advance the field of AI-driven computational pathology.

Website: https://sites.google.com/view/aaai26tutorial-cpath/home

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Multi-Agent Path Finding with Unassigned Agents (MAPFUA)

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