Card: final numbers before publishing

README.md

@@ -158,7 +158,7 @@ Training: 7,000 steps at batch 4 on one Apple M2 (24 GB) — 2,000 readout-only
 | `bridges/gemma-4-12b-4bit-mlx-multimode/` | 1D Burgers, multi-mode initial conditions (modes 1–2, mixed amplitudes) + generalization families | `physics/fno_burgers_multimode.safetensors` | 25.5M | **iid 100%** @±0.05, MAE 0.009 (n=48); combination 25.0%, amplitude extrapolation 52.1% | **released** |
 | `bridges/gemma-4-12b-4bit-mlx-2d-dpot/` | 2D Fisher–KPP, replicated-IC history → u(0.4), value at (x₀, y₀) | `physics/dpot_tiny_fisher2d_finetuned.safetensors` (DPOT-Tiny, 7.5M, fine-tuned) | 13.8M | **100%** @±0.05, MAE 0.0096 (n=60; backbone 10.0%, oracle 96.7%) | **released** |
 
-The multi-mode bridges are released: they reach 100% in-distribution (n=48, MAE 0.009) and do **not** transfer to the two generalization families — 25.0% on the held-out mode combination and 52.1% on amplitude extrapolation, against a backbone of 16.7% / 10.4% and an oracle of 100% on both. The physics model is exact on those families (MAE 0.0008), so the gap is in the readout: 19 of 48 combination answers match a *single*-mode field value, and on extrapolation the implied amplitude is below the true one for 71% of items. A second run with a span readout and mode-shared heads is training and will replace this directory if it does better. The 2D bridges are released too, and they carry the campaign's strongest result: **100%** at MAE 0.0096 (n=60) against 10.0% for the backbone alone and **96.7% for the oracle** — the only arm in this work where the latent channel beats the text ceiling, because the oracle has to copy a number out of the prompt and sometimes mis-rounds it while the bridge reads the field value exactly. The 0.5B backbone reached 95.0% on the same task. Training: 7,500 steps (2,000 readout-only, 4,000 coupled, 1,500 no-harm), the coupled phase ending at 93.8% and the no-harm phase at 97.9 / 95.8 / 100.
+The multi-mode bridges are released: they reach 100% in-distribution (n=48, MAE 0.009) and do **not** transfer to the two generalization families — 25.0% on the held-out mode combination and 52.1% on amplitude extrapolation, against a backbone of 16.7% / 10.4% and an oracle of 100% on both. The physics model is exact on those families (MAE 0.0008), so the gap is in the readout: 19 of 48 combination answers match a *single*-mode field value, and on extrapolation the implied amplitude is below the true one for 71% of items. A span readout with mode-shared heads was validated at 0.5B for exactly this gap; the 12B run has not been started. The 2D bridges are released too, and they carry the campaign's strongest result: **100%** at MAE 0.0096 (n=60) against 10.0% for the backbone alone and **96.7% for the oracle** — the only arm in this work where the latent channel beats the text ceiling, because the oracle has to copy a number out of the prompt and sometimes mis-rounds it while the bridge reads the field value exactly. The 0.5B backbone reached 95.0% on the same task. Training: 7,500 steps (2,000 readout-only, 4,000 coupled, 1,500 no-harm), the coupled phase ending at 93.8% and the no-harm phase at 97.9 / 95.8 / 100.
 
 ## How it works, in one paragraph
 
@@ -202,6 +202,9 @@ The bridges here couple a frozen language model to a frozen *physics* model, but
 psilm_infer.py          the one-command CLI (PsiLM / backbone alone / physics model)
 requirements.txt        pip dependencies, including the psilm package from GitHub
 bridges/gemma-4-12b-4bit-mlx-1d-value-selective/
+bridges/gemma-4-12b-4bit-mlx-multimode/
+bridges/gemma-4-12b-4bit-mlx-2d-dpot/
+MANIFEST.md
     bridges.safetensors     the trained bridges (25.5M params, fp32, 97 MB)
     config.json             construction, coupling depths, training record, per-chunk held-out scores
 physics/fno_burgers_singlemode.safetensors   the frozen FNO (torch key names; complex spectral weights as .real/.imag)