Referencias

Trabajos citados a lo largo del manual. Para los estándares y marcos que se mencionan sin cita (OWASP, OpenTelemetry, MCP, A2A, ISO/IEC 42001, NIST AI RMF y el propio Reglamento europeo de IA), la referencia útil es su documentación oficial, que además es la única versión que estará al día cuando leáis esto.

Ainslie, Joshua, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai. 2023. “GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.” Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.

Anthropic. 2024. Building Effective Agents. Https://www.anthropic.com/engineering/building-effective-agents.

Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2015. “Neural Machine Translation by Jointly Learning to Align and Translate.” International Conference on Learning Representations.

Beurer-Kellner, Luca, Beat Buesser, Ana-Maria Creţu, et al. 2025. “Design Patterns for Securing LLM Agents Against Prompt Injections.” arXiv Preprint arXiv:2506.08837.

DeepSeek-AI. 2025. “DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.” arXiv Preprint arXiv:2501.12948.

Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics.

Dosovitskiy, Alexey, Lucas Beyer, Alexander Kolesnikov, et al. 2021. “An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale.” International Conference on Learning Representations.

Edge, Darren, Ha Trinh, Newman Cheng, et al. 2024. “From Local to Global: A Graph RAG Approach to Query-Focused Summarization.” arXiv Preprint arXiv:2404.16130.

Geirhos, Robert, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel. 2019. “ImageNet-Trained CNNs Are Biased Towards Texture; Increasing Shape Bias Improves Accuracy and Robustness.” International Conference on Learning Representations.

Grootendorst, Maarten. 2025. A Visual Guide to LLM Agents. Https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents.

Hochreiter, Sepp, and Jürgen Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–80.

Hoffmann, Jordan, Sebastian Borgeaud, Arthur Mensch, et al. 2022. “Training Compute-Optimal Large Language Models.” Advances in Neural Information Processing Systems 35.

Hu, Edward J., Yelong Shen, Phillip Wallis, et al. 2022. “LoRA: Low-Rank Adaptation of Large Language Models.” International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2106.09685.

Huyen, Chip. 2022. Designing Machine Learning Systems. O’Reilly Media.

Huyen, Chip. 2025. AI Engineering: Building Applications with Foundation Models. O’Reilly Media.

Kaplan, Jared, Sam McCandlish, Tom Henighan, et al. 2020. “Scaling Laws for Neural Language Models.” arXiv Preprint arXiv:2001.08361.

Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” Advances in Neural Information Processing Systems 25.

LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. “Gradient-Based Learning Applied to Document Recognition.” Proceedings of the IEEE 86 (11): 2278–324.

Lewis, Patrick, Ethan Perez, Aleksandra Piktus, et al. 2020. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” Advances in Neural Information Processing Systems 33.

Liu, Nelson F., Kevin Lin, John Hewitt, et al. 2024. “Lost in the Middle: How Language Models Use Long Contexts.” Transactions of the Association for Computational Linguistics 12: 157–73.

Mikolov, Tomas, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. “Distributed Representations of Words and Phrases and Their Compositionality.” Advances in Neural Information Processing Systems 26.

Ouyang, Long, Jeffrey Wu, Xu Jiang, et al. 2022. “Training Language Models to Follow Instructions with Human Feedback.” Advances in Neural Information Processing Systems 35.

Radford, Alec, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language Models Are Unsupervised Multitask Learners. OpenAI.

Raschka, Sebastian. 2025. Beyond Standard LLMs. Https://magazine.sebastianraschka.com/p/beyond-standard-llms.

Reimers, Nils, and Iryna Gurevych. 2019. “Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks.” Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing.

Shazeer, Noam, Azalia Mirhoseini, Krzysztof Maziarz, et al. 2017. “Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.” International Conference on Learning Representations.

Su, Jianlin, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. 2024. “RoFormer: Enhanced Transformer with Rotary Position Embedding.” Neurocomputing 568.

Vaswani, Ashish, Noam Shazeer, Niki Parmar, et al. 2017. “Attention Is All You Need.” Advances in Neural Information Processing Systems 30.

Wei, Jason, Xuezhi Wang, Dale Schuurmans, et al. 2022. “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.” Advances in Neural Information Processing Systems 35.

Willison, Simon. 2025. The Lethal Trifecta for AI Agents: Private Data, Untrusted Content, and External Communication. Https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/.

Yao, Shunyu, Jeffrey Zhao, Dian Yu, et al. 2023. “ReAct: Synergizing Reasoning and Acting in Language Models.” International Conference on Learning Representations (ICLR).

Zhang, Biao, and Rico Sennrich. 2019. “Root Mean Square Layer Normalization.” Advances in Neural Information Processing Systems 32.