This dataset provides the foundational resources for evaluating and optimizing Formula L , a novel mathematical framework for semantic-driven task allocation in multi-agent systems (MAS) powered by large language models (LLM). The dataset includes Python code and both empirical and synthetic data, specifically designed to validate the effectiveness of Formula L in improving task distribution, contextual relevance, and dynamic adaptation within MAS.

The dataset comprises:

DOI Link: 
10.21227/d481-zv33
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Dataset Files

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[1] Raman Marozau, "Code and Data for Empirical and Synthetic Experiments on Formula L Optimization", IEEE Dataport, 2025. [Online]. Available: http://dx.doi.org/10.21227/d481-zv33. Accessed: Mar. 27, 2025.
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doi = {10.21227/d481-zv33},
url = {http://dx.doi.org/10.21227/d481-zv33},
author = {Raman Marozau },
publisher = {IEEE Dataport},
title = {Code and Data for Empirical and Synthetic Experiments on Formula L Optimization},
year = {2025} }
TY - DATA
T1 - Code and Data for Empirical and Synthetic Experiments on Formula L Optimization
AU - Raman Marozau
PY - 2025
PB - IEEE Dataport
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Raman Marozau. (2025). Code and Data for Empirical and Synthetic Experiments on Formula L Optimization. IEEE Dataport. http://dx.doi.org/10.21227/d481-zv33
Raman Marozau, 2025. Code and Data for Empirical and Synthetic Experiments on Formula L Optimization. Available at: http://dx.doi.org/10.21227/d481-zv33.
Raman Marozau. (2025). "Code and Data for Empirical and Synthetic Experiments on Formula L Optimization." Web.
1. Raman Marozau. Code and Data for Empirical and Synthetic Experiments on Formula L Optimization [Internet]. IEEE Dataport; 2025. Available from : http://dx.doi.org/10.21227/d481-zv33
Raman Marozau. "Code and Data for Empirical and Synthetic Experiments on Formula L Optimization." doi: 10.21227/d481-zv33