The survey organizes Context Engineering into retrieval, processing, management, and integrated systems like RAG and multi-agent setups while identifying an asymmetry where LLMs handle complex inputs well but struggle with equally sophisticated long outputs.
DOI 10.48550/arXiv.2209.03859
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A monograph develops the probabilistic and control-theoretic framework connecting multi-agent reinforcement learning to mean field control, including analyses of Q-learning, policy gradients, and numerical methods for linear-quadratic and general models.
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A Survey of Context Engineering for Large Language Models
The survey organizes Context Engineering into retrieval, processing, management, and integrated systems like RAG and multi-agent setups while identifying an asymmetry where LLMs handle complex inputs well but struggle with equally sophisticated long outputs.
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Mean Field Reinforcement Learning
A monograph develops the probabilistic and control-theoretic framework connecting multi-agent reinforcement learning to mean field control, including analyses of Q-learning, policy gradients, and numerical methods for linear-quadratic and general models.