transformerless_lm: 100x compression RE-VERIFIED on fresh run

Independent re-run confirms the original v2 result:

   arch                    params    compression    val      vs dense
   dense_crt              801,664        1.0x      2.5602      -
   fibgen_K16_separable     8,064      100.4x      2.9020    +13.3%
   fibgen_K32_separable     9,216       87.9x      2.7282     +6.6%

Both compressed variants are substantially below the uniform-random
floor of 4.17 (ln(65)), confirming they LEARN the corpus structure
despite having 100x and 88x fewer stored parameters than the dense
baseline.

This is THE validated headline result for the substrate framework:
  - 100x weight compression
  - +13% val loss penalty (single digit at 88x)
  - 90-93% of dense throughput at inference (validated separately)
  - 10-37x less RAM at deployment (grows with d_model)

The training-speed claims for the various substrate OPERATORS
(Subsim L1-distance, FSM recurrence, etc.) remain scale-bound to
larger T or d than fits in our CPU bench budget. The training-speed
substrate wins exist asymptotically but are not realized in pure
PyTorch on CPU at our test scale.

The deployment-side compression story stands as the substrate
framework's most concretely validated result.

Author: claude

experiments/transformerless_lm/results_fibgen.json

@@ -0,0 +1,176 @@
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+}