chore: refactoring, proper citation and updating on data and refs and apendices

paper/src/bib/references.bib

@@ -645,3 +645,26 @@ What might be more surprising is that even when we adjust the temperature down t
 	year = {2025},
 	file = {Snapshot:/home/velocitatem/Zotero/storage/U5JG4CNM/defeating-nondeterminism-in-llm-inference.html:text/html},
 }
+
+@misc{moritz_ray_2018,
+	title = {Ray: {A} {Distributed} {Framework} for {Emerging} {AI} {Applications}},
+	shorttitle = {Ray},
+	url = {http://arxiv.org/abs/1712.05889},
+	doi = {10.48550/arXiv.1712.05889},
+	abstract = {The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this paper, we consider these requirements and present Ray---a distributed system to address them. Ray implements a unified interface that can express both task-parallel and actor-based computations, supported by a single dynamic execution engine. To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state. In our experiments, we demonstrate scaling beyond 1.8 million tasks per second and better performance than existing specialized systems for several challenging reinforcement learning applications.},
+	urldate = {2026-03-13},
+	publisher = {arXiv},
+	author = {Moritz, Philipp and Nishihara, Robert and Wang, Stephanie and Tumanov, Alexey and Liaw, Richard and Liang, Eric and Elibol, Melih and Yang, Zongheng and Paul, William and Jordan, Michael I. and Stoica, Ion},
+	month = sep,
+	year = {2018},
+	note = {arXiv:1712.05889 [cs]},
+	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing},
+	file = {Preprint PDF:/home/velocitatem/Zotero/storage/SUTDF5BP/Moritz et al. - 2018 - Ray A Distributed Framework for Emerging AI Applications.pdf:application/pdf;Snapshot:/home/velocitatem/Zotero/storage/5GV2DUAA/1712.html:text/html},
+}
+
+@misc{biewald_experiment_2020,
+	title = {Experiment {Tracking} with {Weights} and {Biases}},
+	url = {https://www.wandb.com/},
+	author = {Biewald, Lukas},
+	year = {2020},
+}