1% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_flows_yearly.csv2\begin{longtable}{lrrrrrrrrr}3\caption{Destination of the in-corpus references of the nine AI categories in 2019, 2022 and 2025 (per cent of in-corpus references of the citing category).}\label{tab:ai_flows}\\4\toprule5\textbf{Category} & \textbf{Year} & \textbf{Self} & \textbf{LLM} & \textbf{NLP} & \textbf{Vision} & \textbf{HCI} & \textbf{Robotics} & \textbf{Softw. eng.} & \textbf{AI safety} \\6\midrule7\endfirsthead8\toprule9\textbf{Category} & \textbf{Year} & \textbf{Self} & \textbf{LLM} & \textbf{NLP} & \textbf{Vision} & \textbf{HCI} & \textbf{Robotics} & \textbf{Softw. eng.} & \textbf{AI safety} \\10\midrule11\endhead12Large language models & 2019 & 3.8 & 3.8 & 69.6 & 4.7 & 0.7 & 0.2 & 0.6 & 0.2 \\13Large language models & 2022 & 7.5 & 7.5 & 68.6 & 4.5 & 0.9 & 0.2 & 1.2 & 0.4 \\14Large language models & 2025 & 28.4 & 28.4 & 34.2 & 4.5 & 4.3 & 1.0 & 3.4 & 2.4 \\15Foundation models & 2019 & 1.2 & 0.0 & 18.7 & 31.3 & 0.0 & 2.0 & 0.0 & 0.0 \\16Foundation models & 2022 & 2.9 & 1.5 & 10.5 & 37.4 & 2.9 & 2.1 & 0.3 & 0.4 \\17Foundation models & 2025 & 3.6 & 4.1 & 6.7 & 43.8 & 1.9 & 1.0 & 0.6 & 0.6 \\18Multimodal AI & 2019 & 12.9 & 0.2 & 15.7 & 51.1 & 1.0 & 1.5 & 0.2 & 0.3 \\19Multimodal AI & 2022 & 15.2 & 1.0 & 18.5 & 50.3 & 0.7 & 0.9 & 0.2 & 0.1 \\20Multimodal AI & 2025 & 14.3 & 3.7 & 16.1 & 46.2 & 1.5 & 1.7 & 0.3 & 0.3 \\21Generative AI & 2019 & 13.3 & 0.5 & 3.0 & 53.6 & 0.5 & 0.6 & 0.3 & 0.3 \\22Generative AI & 2022 & 10.7 & 0.2 & 2.1 & 57.9 & 1.3 & 0.6 & 0.4 & 0.3 \\23Generative AI & 2025 & 13.6 & 13.7 & 3.5 & 33.8 & 6.6 & 1.5 & 1.3 & 3.7 \\24AI agents & 2019 & 45.5 & 0.1 & 2.7 & 1.7 & 1.8 & 7.5 & 2.3 & 0.9 \\25AI agents & 2022 & 39.3 & 0.2 & 2.1 & 1.6 & 2.9 & 8.8 & 1.5 & 0.9 \\26AI agents & 2025 & 30.7 & 4.2 & 3.8 & 1.9 & 4.3 & 12.0 & 1.3 & 1.5 \\27Reinforcement learning & 2019 & 47.2 & 0.0 & 2.2 & 5.0 & 0.8 & 14.4 & 0.4 & 0.3 \\28Reinforcement learning & 2022 & 43.5 & 0.1 & 1.2 & 5.0 & 1.0 & 19.2 & 0.6 & 0.3 \\29Reinforcement learning & 2025 & 39.0 & 0.5 & 0.9 & 4.4 & 0.8 & 23.1 & 0.5 & 0.3 \\30ML theory & 2019 & 44.1 & 0.2 & 3.0 & 13.7 & 0.8 & 1.1 & 1.2 & 1.1 \\31ML theory & 2022 & 40.8 & 0.2 & 3.6 & 15.5 & 1.3 & 1.0 & 1.5 & 1.6 \\32ML theory & 2025 & 38.5 & 0.7 & 2.8 & 15.4 & 1.3 & 1.1 & 1.6 & 1.7 \\33DL architectures & 2019 & 36.9 & 0.2 & 5.1 & 21.8 & 0.7 & 1.5 & 1.2 & 0.2 \\34DL architectures & 2022 & 35.1 & 0.4 & 5.4 & 22.1 & 1.0 & 1.4 & 1.0 & 0.3 \\35DL architectures & 2025 & 35.3 & 1.0 & 4.3 & 21.8 & 1.0 & 1.4 & 0.7 & 0.3 \\36AI safety and alignment & 2019 & 27.1 & 0.1 & 7.0 & 6.6 & 9.1 & 3.2 & 4.1 & 27.1 \\37AI safety and alignment & 2022 & 34.4 & 0.8 & 8.2 & 7.3 & 10.0 & 2.3 & 3.6 & 34.4 \\38AI safety and alignment & 2025 & 28.9 & 10.0 & 8.1 & 5.2 & 11.5 & 2.5 & 2.7 & 28.9 \\39\bottomrule40\multicolumn{10}{p{0.95\linewidth}}{\footnotesize Row shares of category-to-category citation flows (Part IV, eq.~\ref{eq:flow}); references outside the eligible corpus are excluded. `Self' is the share going to the citing category itself (for LLM, the Self and LLM columns coincide). Categories are multi-label, so a reference can count under several destination categories.}\\41\end{longtable}42