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We present a novel reasoning approach called Flow-of-Options (FoO), designed to address intrinsic biases in Large Language Models (LLMs).
Analyzing the held-karp tsp bound: A monotonicity property with application
Shmoys, D. B. and Williamson, D. P · 1990
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An introduction to case-based reasoning
Kolodner, J. L · 1992
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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Sequence transduction with recurrent neural networks
Graves, A · 2012
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Brockman, G · 2016
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Language and the flow of thought
Chafe, W · 2017
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Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J · 2017
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Marian: Fast neural machine translation in C++
Junczys-Dowmunt, M., Grundkiewicz, R., Dwojak, T., Hoang, H., Heafield, K., Neckermann, T., Seide, F., Germann, U., Aji, A. F., Bogoychev, N., et al · 2018
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Perception of chemical bonds via machine learning
Loschen, C · 2018
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Automated machine learning: methods, systems, challenges
Hutter, F., Kotthoff, L., and Vanschoren, J · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Roberts, A., Raffel, C., Lee, K., Matena, M., Shazeer, N., Liu, P. J., Narang, S., Li, W., and Zhou, Y · 2019
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Autogluon-tabular: Robust and accurate AutoML for structured data
Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., and Smola, A · 2020
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Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics
Warnat-Herresthal, S., Perrakis, K., Taschler, B., Becker, M., Baßler, K., Beyer, M., Günther, P., Schulte-Schrepping, J., Seep, L., Klee, K., et al · 2020
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Prottrans: towards cracking the language of life’s code through self-supervised learning
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al · 2021
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Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., and Zitnik, M · 2021
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al · 2021
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Sample efficient reinforcement learning with reinforce
Zhang, J., Kim, J., O’Donoghue, B., and Boyd, S · 2021
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LangChain, October 2022
Chase, H · 2022
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Deepcore: A comprehensive library for coreset selection in deep learning
Guo, C., Zhao, B., and Bai, Y · 2022
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Democratizing machine translation with Opus-mt
Tiedemann, J., Aulamo, M., Bakshandaeva, D., Boggia, M., Grönroos, S.-A., Nieminen, T., Raganato, A., Scherrer, Y., Vazquez, R., and Virpioja, S · 2022
Cited alongside, same era.
Deepmol: an automated machine and deep learning framework for computational chemistry
Correia, J., Capela, J., and Rocha, M · 2024
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Creative beam search
Franceschelli, G. and Musolesi, M · 2024
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Large language models orchestrating structured reasoning achieve kaggle grandmaster level
Grosnit, A., Maraval, A., Doran, J., Paolo, G., Thomas, A., Beevi, R. S. H. N., Gonzalez, J., Khandelwal, K., Iacobacci, I., Benechehab, A., et al · 2024
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Gu, J., Jiang, X., Shi, Z., Tan, H., Zhai, X., Xu, C., Li, W., Shen, Y., Ma, S., Liu, H., et al · 2024
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DS-agent: Automated data science by empowering large language models with case-based reasoning
Guo, S., Deng, C., Wen, Y., Chen, H., Chang, Y., and Wang, J · 2024
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Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
Cited alongside, same era.
Grounding large language models in interactive environments with online reinforcement learning
Carta, T., Romac, C., Wolf, T., Lamprier, S., Sigaud, O., and Oudeyer, P.-Y · 2023
Cited alongside, same era.
Pangu-agent: A fine-tunable generalist agent with structured reasoning
Christianos, F., Papoudakis, G., Zimmer, M., Coste, T., Wu, Z., Chen, J., Khandelwal, K., Doran, J., Feng, X., Liu, J., et al · 2023
Cited alongside, same era.
Benchmarking large language models as AI research agents
Huang, Q., Vora, J., Liang, P., and Leskovec, J · 2023
Cited alongside, same era.
AutoGPT, 2023
Significant Gravitas · 2023
Cited alongside, same era.
Baek, J., Jauhar, S. K., Cucerzan, S., and Hwang, S. J · 2024
Cited alongside, same era.
Graph of thoughts: Solving elaborate problems with large language models
Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Podstawski, M., Gianinazzi, L., Gajda, J., Lehmann, T., Niewiadomski, H., Nyczyk, P., et al · 2024
Cited alongside, same era.
Data interpreter: An LLM agent for data science
Hong, S., Lin, Y., Liu, B., Liu, B., Wu, B., Zhang, C., Wei, C., Li, D., Chen, J., Zhang, J., et al · 2024
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Hurst, A., Lerer, A., Goucher, A. P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., Hayes, A., Radford, A., et al · 2024
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Drugagent: Automating ai-aided drug discovery programming through LLM multi-agent collaboration
Liu, S., Lu, Y., Chen, S., Hu, X., Zhao, J., Fu, T., and Zhao, Y · 2024
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LLM reflection
Microsoft · 2024
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Learning to reason with LLMs
OpenAI · 2024
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Clustering techniques with gene expression data for acute myeloid leukemia
Raieli, S · 2024
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Autogen: Enabling next-gen LLM applications via multi-agent conversation framework
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Zhang, S., Zhu, E., Li, B., Jiang, L., Zhang, X., and Wang, C · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., and Narasimhan, K · 2024
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Exact: Teaching AI agents to explore with reflective-mcts and exploratory learning
Yu, X., Peng, B., Vajipey, V., Cheng, H., Galley, M., Gao, J., and Yu, Z · 2025
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