Exp 3B: Embedded topology in trained recurrent operators A 14,553 configuration computational sweep of recurrent network architectures trained on dynamical systems. Each configuration's hidden state activations were measured for topological fidelity to the driving system using persistent homology and Gauss linking integrals. Six post hoc analyses on saved checkpoints probe the operator properties the embedding theorems describe abstractly. This dataset is the empirical companion to the manuscript "Topological reconstruction in trained recurrent operators: a 14,553 configuration empirical sweep" on arXiv to follow). Scope 7 architecture families : VanillaRNN, GRU, LSTM, ESN, Mamba, RWKV, Transformer. 9 hidden dimensions : 3, 5, 8, 16, 32, 64, 128, 256, 512. 3 depths : 1, 2, 4. 7 tasks : circle ($S^1$), torus ($T^2$), torus3 ($T^3$), scalar circle, scalar torus, Lorenz, four dimensional Qi system. 11 random seeds per cell. Total: 14,553 trained configurations. Layout Hostnames in source data have been mapped to opaque labels ( host A a2000 , host B rtx4000 , etc.) so the deposit does not leak machine identity. Reproducibility Source code for the training and analysis pipeline is unde…
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