ARCHIVES

1. Packer, C., Wooders, S., Lin, K., Fang, V., Patil, S. G., Stoica, I., & Gonzalez, J. E. (2023). MemGPT: Towards LLMs as Operating Systems.
arXiv:2310.08560.
2. Shinn, N., Cassano, F., Berman, E., Gopinath, A., Narasimhan, K., & Yao, S. (2023). Reflexion: Language Agents with Verbal Reinforcement
Learning. Advances in Neural Information Processing Systems 36 (NeurIPS 2023).
3. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela,
D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems 33
(NeurIPS 2020).
4. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-Thought Prompting Elicits
Reasoning in Large Language Models. Advances in Neural Information Processing Systems 35 (NeurIPS 2022).
5. Huang, X., et al. (2024). Understanding the Planning of LLM Agents: A Survey. arXiv:2402.02716.
6. Prasad, A., et al. (2023). ADaPT: As-Needed Decomposition and Planning with Language Models. arXiv:2311.05772.
7. Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of Thoughts: Deliberate Problem Solving with
Large Language Models. Advances in Neural Information Processing Systems 36 (NeurIPS 2023).
8. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing Reasoning and Acting in Language
Models. International Conference on Learning Representations (ICLR 2023)9. Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023).
Toolformer: Language Models Can Teach Themselves to Use Tools. Advances in Neural Information Processing Systems 36 (NeurIPS 2023).
10. Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., Gupta, S., Majumder,
B. P., Hermann, K., Welleck, S., Yazdanbakhsh, A., & Clark, P. (2023). Self-Refine: Iterative Refinement with Self-Feedback. Advances in
Neural Information Processing Systems 36 (NeurIPS 2023).
11. Bai, Y., et al. (2022). Constitutional AI: Harmlessness from AI Feedback. arXiv:2212.08073.
12. Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E.,
et al. (2022). Language Models (Mostly) Know What They Know. arXiv:2207.05221.
13. Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., & Mordatch, I. (2023). Improving Factuality and Reasoning in Language Models through
Multiagent Debate. arXiv:2305.14325.
14. Chen, R., Jiang, W., Qin, C., & Tan, C. (2025). Theory of Mind in Large Language Models: Assessment and Enhancement. Proceedings of
the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025).
15. Kosinski, M. (2023). Evaluating Large Language Models in Theory of Mind Tasks. arXiv:2302.02083.
16. Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). SOAR: An Architecture for General Intelligence. Artificial Intelligence, 33(1), 1–64.
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18. Newell, A., & Simon, H. A. (1972). Human Problem Solving. Prentice-Hall.