ARCHIVES

1. Wei, J., et al. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing
Systems, 35, 24824-24837.
2. Park, J. S., et al. (2023). Generative agents: Interactive simulacra of human behavior. Proceedings of the 36th Annual ACM Symposium on
User Interface Software and Technology, 1-22.
3. Schick, T., et al. (2023). Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761.
4. Yao, S., et al. (2022). ReAct: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629.
5. Bansal, T., Kamar, M., Verma, M., Sweihahn, K., & Gupta, K. (2020). Hierarchical Multi-agent Reinforcement Learning for Semi-
cooperative Communication Games. Proceedings of the 37th International Conference on Machine Learning, 667-677.
6. Foerster, J., Foerster, J., Kapoor, A., & Schmidhuber, J. (2016). Learning to Cooperate with Hierarchy and Delegation. Proceedings of the
2016 International Conference on Autonomous Agents and Multiagent Systems, 931-939.7. Hao, J., Yao, H., Liang, P., & Sun, M. (2022). Cooperative Multi-Agent Reinforcement Learning for Team Formation with Unknown
Dynamics. Proceedings of the 36th International Conference on Machine Learning, 5073-5083.
8. Liu, Y., Li, M., Wu, Y., Li, S., Xu, L., & Zhu, S. (2023). A Survey of Multi-Agent Communication: From Theory to Practice. arXiv preprint
arXiv:2302.11223.
9. Bai, Y., et al. (2022). Constitutional AI: Harmlessness from AI feedback. arXiv preprint arXiv:2212.08073.
10. Bommasani, R., et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
11. Awesome AI Agents Repository. (2024). Retrieved from https://github.com/e2b-dev/awesome-ai-agents
12. Bansal, T., Kamar, M., Verma, M., Sweihahn, K., & Gupta, K. (2020). Hierarchical Multi-agent Reinforcement Learning for Semi-
cooperative Communication Games. Proceedings of the 37th International Conference on Machine Learning:
https://arxiv.org/abs/2004.07228 (pp. 667-677). PMLR.
13. Foerster, J., Foerster, J., Kapoor, A., & Schmidhuber, J. (2016). Learning to Cooperate with Hierarchy and Delegation. Proceedings of the
2016 International Conference on Autonomous Agents and Multiagent Systems: https://arxiv.org/abs/1602.01724 (pp. 931-939).
International Foundation for Autonomous Agents and Multiagent Systems.
14. Hao, J., Yao, H., Liang, P., & Sun, M. (2022). Cooperative Multi-Agent Reinforcement Learning for Team Formation with Unknown
Dynamics. Proceedings of the 36th International Conference on Machine Learning: https://arxiv.org/abs/2203.16783 (pp. 5073-5083).
PMLR.
15. Lewis, M., Okande, D., & Hernandez-Gardiol, P. (2016). IRL in Multiagent Systems: A Survey. Autonomous Agents and Multi-Agent
Systems: [invalid URL removed] 20(3), 685-728. Springer Science+Business Media.
16. Liu, Y., Li, M., Wu, Y., Li, S., Xu, L., & Zhu, S. (2023). A Survey of Multi-Agent Communication: From Theory to Practice. arXiv preprint
arXiv:2302.11223: https://arxiv.org/abs/2302.11223.
17. Otte, K., Ray, A., & Silver, D. (2016). Learning to Play Games with Imperfect Information. Proceedings of the 30th International Conference
on Neural Information Processing Systems: https://arxiv.org/abs/1603.01121 (pp. 3958-3966). Curran Associates, Inc.
18. Rashkin, H., Choi, E., & Jha, Y. (2018). Toward An Agenda for Deontology in Natural Language Processing. Proceedings of the 2018
Conference on Fairness, Accountability, and Transparency: https://arxiv.org/abs/1806.08770 (pp. 294-305). Association for Computing
Machinery.
19. Schulman, J., Lehman, S., & Leibo, J. Z. (2015). Intrinsically Motivated Reinforcement Learning for Sequential Decision-Making.
Proceedings of the 32nd International Conference on Machine Learning: https://arxiv.org/abs/1506.05869 (pp. 1714-1722). PMLR.
20. Serban, C., Bohnert, A., Courville, A., Lowe, R., Morin, Y., & Wu, S. (2017). A Comprehensive Survey of Deep Reinforcement Learning.
arXiv preprint arXiv:1701.07281: https://arxiv.org/abs/1701.07281.
21. Fig 2 and 3 source : https://github.com/e2b-dev/awesome-ai-agents
22. https://arxiv.org/abs/2412.20297?utm_source=chatgpt.com "FaGeL: Fabric LLMs Agent empowered Embodied Intelligence Evolution with
Autonomous Human-Machine Collaboration"
23. https://arxiv.org/abs/2504.00907?utm_source=chatgpt.com "Grounding Multimodal LLMs to Embodied Agents that Ask for Help with
Reinforcement Learning"
24. https://arxiv.org/abs/2506.07232?utm_source=chatgpt.com "Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in
Embodied Environments"
25. https://www.reuters.com/technology/artificial-intelligence/autonomous-agents-profitability-dominate-ai-agenda-2025-executives-forecast-
2024-12-12/?utm_source=chatgpt.com "Autonomous agents and profitability to dominate AI agenda in 2025, executives forecast"
26. https://en.wikipedia.org/wiki/Recursive_self-improvement?utm_source=chatgpt.com "Recursive self-improvement"