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English Information

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Authors
# Name
1 Renato Miyaji(re.miyaji@usp.br)
2 Vitor Bedin(vitor.bedin@visagio.com)
3 Renato Moulin(renato.moulin@visagio.com)
4 Leonardo Machado(leonardo.machado@visagio.com)

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Reference
# Reference
1 Gao, S., Zhao, S., Jiang, X., Duan, L., Chng, Y. X., Chen, Q.-G., Luo, W., Zhang, K., Bian, J.-W., and Gong, M. (2025). Scaling beyond context: A survey of multimo- dal retrieval-augmented generation for document understanding. arXiv preprint ar- Xiv:2510.15253.
2 Inaba, T., Kiyomaru, H., Cheng, F., and Kurohashi, S. (2023). Multitool-cot: Gpt-3 can use multiple external tools with chain of thought prompting. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023), pages 1522–1532. Association for Computational Linguistics.
3 Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux
4 Lin, Y., Cheng, Z., Zhao, A., Chen, S., Fu, G., Zhang, S., and Wu, D. (2023). Swiftsage: A modular agent with swift and sage components for adaptive and efficient action. In Advances in Neural Information Processing Systems (NeurIPS 2023).
5 Liu, Y., Iter, D., Xu, Y., Wang, S., Xu, R., and Zhu, C. (2023). G-eval: Nlg evaluation using gpt-4 with better alignment than human annotation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023), pages 10654–10671. Association for Computational Linguistics.
6 Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, W., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., Gupta, S., Majumder, B. P., Lapania, G., Welleck, S., and Bhadauria, T. (2023). Self-refine: Iterative refinement with self-feedback. In Advances in Neural Information Processing Systems (NeurIPS 2023).
7 Mekala, D., Weston, J., Lanchantin, J., Raileanu, R., Lomeli, M., Shang, J., and Dwivedi- Yu, J. (2024). Toolverifier: Generalization to new tools via self-verification. In Fin- dings of the Association for Computational Linguistics: EMNLP 2024, pages 5026– 5041, Miami, Florida, USA. Association for Computational Linguistics.
8 Shinn, N., Cassano, F., Berman, E., Gopinath, A., Narasimhan, K., and Yao, S. (2023). Reflexion: Language agents with verbal reinforcement learning. In Advances in Neural Information Processing Systems (NeurIPS 2023).
9 Sun, W., Yan, L., Ma, X., Wang, S., Ren, P., Chen, Z., Yin, D., and Ren, Z. (2023). Is chatgpt good at search? investigating large language models as re-ranking agents. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023).
10 Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D. (2023). Self-consistency improves chain of thought reasoning in language models. In International Conference on Learning Representations (ICLR 2023).
11 Xu, B., Peng, Z., Lei, B., Mukherjee, S., Liu, Y., and Xu, D. (2023). Rewoo: Decou- pling reasoning from observations for efficient augmented language models. In Proce- edings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023).
12 Zheng, H. S., Mishra, S., Chen, X., Cheng, H.-T., Chi, E. H., Le, Q. V., and Zhou, D. (2024). Take a step back: Evoking reasoning via abstraction in large language models. In International Conference on Learning Representations (ICLR).