Update README.md

README.md

@@ -654,6 +654,24 @@ A geometric field modulator trained on one VAE can be ROTATED to work on another
 
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+## XVI. Scripts Reference
+
+| Script | Purpose | Key Outputs |
+|---|---|---|
+| `probe_t5_small_terrain.py` | T5-Small embedding + layer geometry | PR, CV, digit manifold, layer evolution |
+| `probe_t5_wordnet_summarize.py` | T5-Small × WordNet relational alignment | Pearson, Spearman, distance bands, hypernym decay |
+| `probe_t5_wordnet_50seeds.py` | 50-seed stability test (GPU-accelerated) | Confidence intervals for all relational metrics |
+| `probe_t5_inactive_weights.py` | T5-Small/Base inactive weight topology | SVD, sparsity, QK manifold, dead neurons |
+| `cross_architecture_weight_battery.py` | BERT + CLIP + DINOv2 battery | Cross-model comparison table |
+| `probe_flux_t5_g4.py` | T5-v1.1-XXL (Flux encoder) full battery | All layers, encoder + decoder + cross-attn |
+| `geometric_residual_modulator.py` | LERP modulator + training utilities | Modulator class + measurement tools |
+| `geometric_field_modulator.py` | Multi-expert field modulator | KSimplex experts + multiplicative gating |
+| `geometric_modulator_full_pipeline.py` | Self-contained T5 + WordNet + modulator | End-to-end pipeline |
+| `train_modulator.py` | Training loop for alpha convergence | Freeze T5, train modulator, track alpha |
+| `probe_t5gemma2.py` | T5Gemma2 battery (both scales) | GQA handling, adapted enc-dec topology |
+| `probe_unet_geometry.py` | SD 1.5 / SDXL UNet battery | U-path QK gradient, cross-attn lock |
+| `probe_vae_geometry.py` | All four VAE battery | Conv reshape, bottleneck attention, latent comparison |
+| `procrustes_vae_analysis.py` | Pairwise Procrustes on 4 VAEs | Distance matrices, depth profiles, rotation gain |
 
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