What’s coming up at #AAAI2026?

by Lucy Smith

14 January 2026

Invited talks

The invited talks this year are as follows:

Peter Stone – From how to learn to what to learn in multiagent systems and robotics
Bowen Zhou – Specialized generalist: Towards high-efficiency AGI
Yolanda Gill – From workflows to water coolers: AI that can navigate human nature
Daniel Whiteson – Fundamental physics and science communication
Katerina Fragkiadaki – Learning world simulators from data
Isabelle Guyon – AI in science and technology: the future in our hands
Ece Kamar – Navigating the AI horizon: Promises, perils, and the power of collaboration
Derek Haoyang Li – Small data: A new paradigm for the next generation of AI
AAAI Robert S. Engelmore Memorial Lecture Award: Ashok Goel – AI for reskilling, upskilling, and workforce development
Patrick Henry Winston Outstanding Educator Award: Alan Mackworth and David Poole – The essence of intelligence is appropriate action (not thinking, reasoning, learning or language) and other things every student of AI should know

Science communication for AI researchers – an introduction

We (AIhub) will be running a short course on science communication on Wednesday 21 January, from 13:00 – 14:30. In this brief tutorial, science communication experts will teach you how to clearly and concisely explain your research to non-specialists.

Tutorial and lab forum

The tutorial and lab forum will be held at the beginning of the conference, on Tuesday 20 and Wednesday 21 January.

TH01: A Decade of Sparse Training: Why Do We Still Stick to Dense Training?
TH02: Brain-Inspired AI 2.0: Aligning Language Models Across Languages and Modalities
TH03: Handling Out-of-Distribution Data in the Open World: Principles and Practice for Reliable AI
TH04: LLMs for Optimization: Modeling, Solving, and Validating with Generative AI
TH05: Plan, Activity, and Intent Recognition (PAIR)
TH06: Hyperbolic Geometry for Foundation Models: A Tutorial
TH07: Uncertainty Quantification for Large Language Models
LH02: Developing AI Agents for IT Automation Tasks with ITBench
TQ01: Deep Representation Learning for Tabular Data
TQ02: From Underspecification to Alignment: Breaking the One-Model Mindset for Reliable AI
TQ03: Bridging Healthcare and AI: EHR- Enhanced Clinical Conversational Systems with LLMs: A Comprehensive Tutorial
LQ01: SOFAI-LM: A Cognitive Architecture for Building Efficient and Reliable Reasoning Systems with LLMs
TH08: Algorithms and Systems for Efficient Inference in Generative AI
TH09: Multimodal Foundation Models in Modern Healthcare: Principles, Practices, and Beyond
TH10: Trustworthy Machine Reasoning with Foundation Models
TH11: Multi-modal Time Series Analysis: Methods, Datasets, and Applications
TH12: Large Language Models meet Logical Reasoning
TH13: Towards Trustworthy and Socially Responsible Generative Foundation Models
TH14: Structured Representation Learning: Interpretability, Robustness and Transferability for Large Language Models
TH15: Tutorial on LLM-based Multi-Agent Systems: From Foundations to Frontiers
TQ04: Evolution of Neural Networks
TQ05: The Many Faces of Multiplicity in Machine Learning
TQ06: Auto-Formalization in Large Language Models era: From Mathematical Proofs to Verifying LLM Reasoning
TQ07: Beyond Graph Distribution Shifts: LLMs, Adaptation, and Generalization
TQ08: Rule Learning in the LLM Era: Foundations, Techniques, and Applications
TH16: Bandits, LLMs, and Agentic AI
TH17: Domain Model Learning for Automated Planning
TH18: Foundations of Interpretable Deep Learning
TH19: When AI “Forgets” for Good: The Science and Practice of Machine Unlearning for AI Safety — Progress, Pitfalls, and Prospects
TH20: Model Reuse in the LLM Era: Leveraging Pre-Trained Resources with Classical and Modern Approaches
TH21: Modern Methods in Associative Memory
TH22: Optimal Transport-Driven Machine Learning: Techniques and Applications
LH03: The Verification of Neural Networks Competition (VNN-COMP): A Lab for Benchmark Proposers,
Verification Tool Participants, and the Broader AI Community
TQ09: The Application of Generative AI and Intelligent Agents in Low-level Vision
TQ10: Black-box Optimization from Offline Datasets
TQ11: Knowledge Distillation for Language Models: Challenges and Opportunities with Sequential Data
TQ12: Clustering High-dimensional Data: Balancing Abstraction and Representation
TH23: Human centered AI: challenges and opportunities
TH24: Foundation Models for Time Series Analysis: A Tutorial
TH25: Agentic AI for Scientific Discovery: Benchmarks, Frameworks, and Applications
TH26: Generative AI in Healthcare: Causality, Decision, and Real-world Case Study
TH27: Discrete Choice and Applications
TH28: Neural Network Reprogrammability: A Unified Framework for Parameter-Efficient Foundation Model Adaptation
TH29: Toward Foundation Models for Detecting Abnormal Activities on Graphs
LH04: Learning to Steer Large Language Models
TQ13: Recent Advances in Multi-Objective Search
TQ14: Computational Optimization in LLM Inference: Reuse and Delegation
TQ15: Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluations
LQ02: From Inception to Productization: Hands-on Lab for the Lifecycle of Multimodal Agentic AI in Industry 4.0

Find out more about the tutorials and labs here.

Bridge programme

The bridge programme is designed to bring together two or more communities from different AI disciplines to foster collaborations. There are eleven different sessions this year, and these will be held on Tuesday 20 and Wednesday 21 January.

B1: AI for Medicine and Healthcare
B2: Logical and Symbolic Reasoning in Language Models
B3: Combining AI and OR/MS for Better, Trustworthy Decision Making
B4: Knowledge-guided Machine Learning: Bridging Scientific Knowledge and AI
B5: Advancing Large Language Models and Multi-Agent Systems
B6: AI and Wildlife Conservation
B7: Artificial Intelligence for Scholarly Communication (AI4SC)
B8: Bridging AI and Behavior Change (ABC)
B9: Bridging Planning and Reasoning in Natural Languages with Foundational Models
B10: Making Embodied AI Reliable with Testing and Formal Verification
B11: Streaming Continual Learning Bridge
B12: Trustworthy AI for Legal and Law Enforcement Applications: Foundations, Challenges, and the Path Forward