docs: record sha256 for checkpoint_n50_unconstrained_best.pt and add retrain provenance note

README.md

@@ -21,16 +21,15 @@ regulatory rules from perturbation-state transition data, with
 support-conditional uniqueness certificates and active experiment
 planning. This repo hosts the paper's released checkpoints. The public
 code lives in a companion package (`able-public`); see the
-[reproducibility README](https://github.com/) for install and
-reproduction commands.
+reproducibility README there for install and reproduction commands.
 
 ## Contents
 
-| File | Size | SHA-256 | Purpose |
+| File | Size (bytes) | SHA-256 | Purpose |
 |---|---:|---|---|
-| `checkpoint_n50_ncf_best.pt` | ~24 MB | `57c968490a2f1535582cc009fc38f659b6fe4b56f89bf72c9bcfb285640a0c8d` | Main 50-variable NCF-pointer proposer. Used for BBM (Table 2, Figs. 2/3/4/6), Ablation A (Table 9 row), and all default evaluation commands in the public README. |
-| `checkpoint_n15_ncf_best.pt` | ~24 MB | `26cdef1bb4bfb39fbb4c278d2f40528c1328664a80c22c97ee99a901fe4a34f0` | 15-variable NCF-pointer proposer used for Table 1 (four curated biological networks). |
-| `checkpoint_n50_unconstrained_best.pt` | ~24 MB | *(to be added)* | Ablation B: 50-variable NCF-free decoder variant (unconstrained truth-table head), used only for Appendix Table 9 / Ablation B. |
+| `checkpoint_n50_ncf_best.pt` | 24,097,458 | `57c968490a2f1535582cc009fc38f659b6fe4b56f89bf72c9bcfb285640a0c8d` | Main 50-variable NCF-pointer proposer. Used for BBM (Table 2, Figs. 2/3/4/6), Ablation A (Table 9 row), and all default evaluation commands in the public README. |
+| `checkpoint_n15_ncf_best.pt` | 23,965,466 | `26cdef1bb4bfb39fbb4c278d2f40528c1328664a80c22c97ee99a901fe4a34f0` | 15-variable NCF-pointer proposer used for Table 1 (four curated biological networks). |
+| `checkpoint_n50_unconstrained_best.pt` | 25,312,058 | `03510ef826edce9a53cfa87049abf77cd17ea564e87ef4f06167d19e5b952f83` | Ablation B: 50-variable NCF-free decoder variant (unconstrained truth-table head), used only for Appendix Table 9 / Ablation B. See provenance note below. |
 
 All three are plain PyTorch state dicts saved via
 `torch.save({"model_state_dict": ..., "optimizer_state_dict": ...,
@@ -52,13 +51,30 @@ All three are plain PyTorch state dicts saved via
 Exact configs are embedded in each `.pt` under the `"config"` key, and
 are also committed alongside the public training scripts.
 
+## Provenance note for `checkpoint_n50_unconstrained_best.pt`
+
+The original post-paper checkpoint for the Ablation B (`unconstrained`)
+variant was unrecoverable at release time. The file in this repo is a
+**retrain** produced from the same committed training script and
+configuration (seed 42, same `DEFAULT_CONFIG`). It reproduces the
+paper's expected ablation regime on the synthetic held-out eval
+(`transition_acc` bouncing in `[0.014, 0.022]`, `tt_bit_acc ~= 0.836`,
+`regulator_set_f1 ~= 0.60`, `functional_agreement ~= 0.92`) but will
+**not be byte-identical** to the artifact that originally produced the
+paper's Appendix Table 9 / Ablation B numbers, because synthetic data
+streaming is sensitive to dataloader-order PRNG draws. Downstream BBM
+Lift-Cert numbers are expected to be statistically equivalent but
+may differ within run-to-run noise. If bit-exact reproduction of the
+paper table is required, rerun the Lift-Cert pipeline against this
+checkpoint and report the refreshed numbers.
+
 ## Intended use
 
 - Reproduction of the ICML-2026 AI4Science paper numbers. The companion
   CLI `able-download-checkpoints` consumes this repo.
-- Research extensions on k-junta Boolean-network recovery from perturbation
-  transitions (neuro-symbolic, active-learning, and certificate-style
-  work).
+- Research extensions on k-junta Boolean-network recovery from
+  perturbation transitions (neuro-symbolic, active-learning, and
+  certificate-style work).
 
 ## Limitations