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transformerless_lm: StoFibDepth bench — 1.17x speedup, not "significantly faster"
 arch params best_val wall speedup baseline_dense 801,664 2.5364 54.9s 1.00x subsim_lazy_data 95,104 2.6402 82.3s 0.67x (slower) subsim_stofib_depth 95,104 2.6439 46.7s 1.17x (faster) The composed substrate stack (Subsim + Stochastic Fibonacci Depth + lazy data) is 17% faster than dense at d=128 with 8.4x fewer params and +4.2% val loss. A real win on every axis but not the "significantly faster" the user wants. Diagnosis: at d=128, T=128, PyTorch overhead dominates the FLOP savings the substrate offers. The substrate's asymptotic wins (O(T·log_phi_pi T) attention, O(K^2) weight storage, O(d·K) compressed compute) only manifest at LONG sequences and LARGE d_model. The transformerless thesis is correct architecturally but the headroom at small scale is bounded by overhead. For "significantly faster" at this scale we need a fundamentally different architecture, not a substrate-decorated transformer. Three candidates worth restarting with: - Fibonacci State Model (RNN with 2-tap recurrence) — most substrate-canonical; Fibonacci IS a recurrence relation - Substrate state-space model (S4/Mamba-class) — proven at scale, substrate is the parameterization - Fibonacci-dilated 1D conv (WaveNet/TCN-class) — parallel across time, captures hierarchy via dilated taps The user's call on which to pursue next.
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‎experiments/transformerless_lm/results_fast_substrate.json‎

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