TH04: Foundation Models meet Embodied Agents: https://foundation-models-meet-embodied-agents.github.io
TH07: Concept-based Interpretable Deep Learning: https://conceptlearning.github.io
TH08: Evaluating Large Language Models: Challenges and Methods: https://llm-understand.github.io/
TH10: Neurosymbolic AI for EGI: Explainable, Grounded, and Instructable Generations: https://nesy-egi.github.io/
TH12: Advancing Brain-Computer Interfaces with Generative AI for Text, Vision, and Beyond: https://sites.google.com/view/aaai2025bci4genai/home
TH13: AI for Science in the Era of Large Language Models: https://xuanwang91.github.io/2025-02-25-aaai25-tutorial
TH15: Graph Neural Networks: Architectures, Fundamental Properties and Applications: https://gnn.seas.upenn.edu/aaai-2025/
TH17: The Lifecycle of Knowledge in Large Language Models: Memorization, Editing, and Beyond: https://llmknowledgelifecycle.github.io/AAAI2025_Tutorial_LLMKnowledge/
TH18: Thinking with Functors — Category Theory for A(G)I: https://people.cs.umass.edu/~mahadeva/papers/aaai2025-tutorial-th18.pdf
TH19: User-Driven Capability Assessment of Taskable AI Systems: https://aair-lab.github.io/aia2025-tutorial
TH23: Inferential Machine Learning: Towards Human-collaborative Vision and Language Models: https://alregib.ece.gatech.edu/courses-and-tutorials/aaai-2025-tutorial/
TH24: Machine Learning for Solvers: https://ml-for-solvers.github.io
TH26: Symbolic Regression: Towards Interpretability and Automated Scientific Discovery: https://symbolicregression2025.github.io/
TH27: Tutorial: Multimodal Artificial Intelligence in Healthcare: https://sites.google.com/view/aaai-multimodal-ai-health/home
TQ01: Advancing Offline Reinforcement Learning: Essential Theories and Techniques for Algorithm Developers: https://fengdic.github.io/offlineRL/
TQ07: From Tensor Factorizations to Circuits (and Back): https://april-tools.github.io/aaai25-tf-pc-tutorial
TQ08: KV Cache Compression for Efficient Long Context LLM Inference: Challenges, Trade-Offs, and Opportunities: https://github.com/henryzhongsc/longctx_bench
TQ09: Supervised Algorithmic Fairness in Distribution Shifts: https://sites.google.com/view/aaai25-tutorial-algor-fair/home
TQ10: Artificial Intelligence Safety: From Reinforcement Learning to Foundation Models: https://sites.google.com/view/aisafety-aaai2025
TQ12: Graph Machine Learning under Distribution Shifts: Adaptation, Generalization and Extension to LLM: https://ood-generalization.com/aaai2025Tutorial.htm
TQ14: Hypergraph Neural Networks: An In-Depth and Step-by-Step Guide: https://sites.google.com/view/hnn-tutorial
TQ15: The Quest for A Science of Language Models: https://glaciohound.github.io/Science-of-LLMs-Tutorial
LQ02: Continual Learning on Graphs: Challenges, Solutions, and Opportunities: https://queuq.github.io/CGL_TUT_AAAI2025/
LQ03: Developing explainable multimodal AI models with hands-on lab on the life-cycle of rare event prediction in manufacturing: https://aiisc.ai/aaai25/AAAI-Lab-Rare-Event-Prediction/