Seminar will run in English and it will be organized as a series of presentations of research papers (50 mimnutes) followed by Q&A.

To get the credit, student is supposed to give one presentation, and to actively participate in at least 10 seminars (one more seminar may be missed in exchange for a written report).

This is a preliminary program of the seminar.

Some examples of papers for presentation:

1. Chad Hogg, Hector Munoz-Avila, and Ugur Kuter: HTN-Maker: Learning HTNs with Minimal Additional Knowledge Engineering Required. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08). AAAI Press.
2. Pat Langley: Learning Hierarchical Problem Networks for Knowledge-Based Planning. ILP 2022: 69-83
3. Songtuan Lin, Daniel Höller, Pascal Bercher: Modeling Assistance for Hierarchical Planning: An Approach for Correcting Hierarchical Domains with Missing Actions. SOCS 2024: 55-63
4. Songtuan Lin, Alban Grastien, Pascal Bercher: Towards Automated Modeling Assistance: An Efficient Approach for Repairing Flawed Planning Domains. AAAI 2023: 12022-12031
5. D. Nau, T.-C. Au, O. Ilghami, U. Kuter, W. Murdock, D. Wu, and F.Yaman: SHOP2: An HTN Planning System. JAIR, volume 20, pp. 379-404, 2003.
6. Robert P. Goldman, Ugur Kuter, and Richard G. Freedman. Stable plan repair for state-space HTN planning. HPlan Workshop 2020
7. Pascal Bercher, Ron Alford, Daniel Höller: A Survey on Hierarchical Planning - One Abstract Idea, Many Concrete Realizations. IJCAI 2019: 6267-6275
8. Keisuke Okumura: LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding. AAAI 2023: 11655-11662
9. J. Li, Z. Chen, Y. Zheng, S.-H. Chan, D. Harabor, P. Stuckey, H. Ma and S. Koenig. Scalable Rail Planning and Replanning: Winning the 2020 Flatland Challenge. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), 477-485, 2021.
10. Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, Subbarao Kambhampati: On the Planning Abilities of Large Language Models -- A Critical Investigation. NeurIPS 2023.
11. Karthik Valmeekam, Kaya Stechly, Subbarao Kambhampati: LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench, Preprint on Arxiv, Sept 2024.
12. Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, Mehrdad Farajtabar: GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models. arXiv:2410.05229
13. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin: Attention Is All You Need. CoRR abs/1706.03762 (2017)
14. Yi Tay, Mostafa Dehghani, Dara Bahri, Donald Metzler: Efficient Transformers: A Survey. CoRR abs/2009.06732 (2020)
15. Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang: Sparks of Artificial General Intelligence: Early experiments with GPT-4. CoRR abs/2303.12712 (2023)
16. Yann LeCun: A Path Towards Autonomous Machine Intelligence, OpenReview.net, 2022
17. Xu Huang, Weiwen Liu, Xiaolong Chen, Xingmei Wang, Hao Wang, Defu Lian, Yasheng Wang, Ruiming Tang, Enhong Chen: Understanding the planning of LLM agents: A survey. CoRR abs/2402.02716 (2024)
18. Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Banghua Zhu, Hao Zhang, Michael I. Jordan, Joseph E. Gonzalez, Ion Stoica: Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. ICML 2024: 8359-8388
19. Llama Team: The Llama 3 Herd of Models. CoRR abs/2407.21783 (2024)
20. Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, Liwei Wang: Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction. NeurIPS 2024
21. Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, Max Tegmark: KAN: Kolmogorov-Arnold Networks. CoRR abs/2404.19756 (2024)
22. Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y. K. Li, Fuli Luo, Yingfei Xiong, Wenfeng Liang: DeepSeek-Coder: When the Large Language Model Meets Programming - The Rise of Code Intelligence. CoRR abs/2401.14196 (2024)
23. Adam Tauman Kalai, Ofir Nachum, Santosh S. Vempala, Edwin Zhang: Why Language Models Hallucinate. CoRR abs/2509.04664 (2025)