Publish reviewed research staging to main

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PROVENANCE.json

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+{
+  "source": "N014_HuReT_Recovered_Evidence_20261013.zip",
+  "source_sha256": "1e335cc589635038c6a5bb31cf383eb1566df91637e61de765290f6c78196e4d",
+  "transformation": "Copied 15 original NPZ files; new standalone NumPy loader.",
+  "recovery": "CP03R1"
+}

README.md

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+---
+language:
+- en
+tags:
+- research
+- numpy
+- reproducibility
+---
+# huret-digit-classifiers
+
+Fifteen October digit classifiers: identity, parity and magnitude, five seeds each. The trained arrays are retained from the CP02 replay. Architecture: 64 -> 32 ReLU -> 16 ReLU -> task head. This is the October HuReT audit, not an MRI model and not the missing July model archive.
+
+## Local use
+```sh
+python -m pip install -r requirements.txt
+python validate.py
+```
+Import `predict` from `inference.py`; see `config.json` for input semantics. Inputs are not automatically normalized except as specified. Loaders reject wrong shapes, nonfinite values and invalid model selectors. Weight files are NumPy archives or JSON; no pickle/custom remote execution is required.
+
+## Evidence and limitations
+The validation is loader parity and input-contract testing, not independent external predictive validation. No clinical deployment, universal physics, foundation-model quality or new biological evidence is claimed. These are individual finite model families, not repackaged third-party LLMs.
+
+## Licensing and provenance
+Private staging. Author approval and redistribution rights must be confirmed before public upload. No new blanket license, completed HF upload or DOI is asserted. `PROVENANCE.json` records exact source file hashes and transformations.
+## Verified packaging revision — 2026-10-20
+
+Preserved model weights reloaded; saved or synthetic example checks passed. No training, held-out evaluation, clinical or hosted verification.
+
+Runtime state: `PASS`. Rights and release approval are separate. See `RELEASE_VERIFICATION.json`. No paper or conference submission is included as a new software result.

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RELEASE_VERIFICATION.json

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+{
+  "schema": "invariant.software_release_verification.v1",
+  "runtime_status": "PASS",
+  "scope": "Preserved model weights reloaded; saved or synthetic example checks passed. No training, held-out evaluation, clinical or hosted verification.",
+  "checked_in": "isolated Linux container; Python 3.13.5 / Node 22.16.0 / Chromium 144 where applicable",
+  "actual_server_tested": false,
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+  "evidence_file_in_delivery_audit": "model_reload_final.json"
+}

VALIDATION.json

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+{
+  "passed": true,
+  "checks": 21,
+  "weights": 15,
+  "checks_detail": [
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+    {
+      "task": "identity",
+      "seed": 4,
+      "rows": 19,
+      "max_reload_difference": 0.0
+    },
+    {
+      "task": "parity",
+      "seed": 0,
+      "rows": 19,
+      "max_reload_difference": 0.0
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+    {
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+    {
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+      "task": "parity",
+      "seed": 4,
+      "rows": 19,
+      "max_reload_difference": 0.0
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+    {
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+      "seed": 0,
+      "rows": 19,
+      "max_reload_difference": 0.0
+    },
+    {
+      "task": "magnitude",
+      "seed": 1,
+      "rows": 19,
+      "max_reload_difference": 0.0
+    },
+    {
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+      "seed": 2,
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+    },
+    {
+      "task": "magnitude",
+      "seed": 3,
+      "rows": 19,
+      "max_reload_difference": 0.0
+    },
+    {
+      "task": "magnitude",
+      "seed": 4,
+      "rows": 19,
+      "max_reload_difference": 0.0
+    }
+  ],
+  "claim": "Reload and contract checks only"
+}

config.json

@@ -0,0 +1,16 @@
+{
+  "input_features": 64,
+  "normalization": "8x8 grayscale pixels divided by 16, range [0,1]",
+  "tasks": {
+    "identity": "digit 0-9",
+    "parity": "even/odd",
+    "magnitude": "0-4 vs 5-9"
+  },
+  "seeds": [
+    0,
+    1,
+    2,
+    3,
+    4
+  ]
+}

inference.py

@@ -0,0 +1,11 @@
+from pathlib import Path
+import numpy as np
+ROOT=Path(__file__).parent
+def predict(x, task='identity', seed=0):
+    if task not in {'identity','parity','magnitude'} or type(seed) is not int or seed not in range(5): raise ValueError('Unknown task or seed')
+    x=np.asarray(x,dtype=np.float32)
+    if x.ndim!=2 or x.shape[1]!=64 or not np.isfinite(x).all() or (x<0).any() or (x>1).any(): raise ValueError('Expected finite normalized (n,64), values in [0,1]')
+    with np.load(ROOT/'weights'/f'{task}_seed{seed}.npz',allow_pickle=False) as w:
+        h=np.maximum(x@w['hidden.0.weight'].T+w['hidden.0.bias'],0)
+        h=np.maximum(h@w['hidden.2.weight'].T+w['hidden.2.bias'],0)
+        return h@w['head.weight'].T+w['head.bias']

requirements.txt

@@ -0,0 +1 @@
+numpy==2.3.5

validate.py

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+import json
+from pathlib import Path
+import numpy as np
+from inference import predict,ROOT
+if __name__=='__main__':
+    x=np.random.default_rng(13).uniform(size=(19,64)).astype(np.float32);checks=0;results=[]
+    for task in ['identity','parity','magnitude']:
+      for seed in range(5):
+        a=predict(x,task,seed);b=predict(x,task,seed)
+        assert np.array_equal(a,b) and np.isfinite(a).all() and a.shape==(19,10 if task=='identity' else 2)
+        results.append({'task':task,'seed':seed,'rows':19,'max_reload_difference':float(np.max(np.abs(a-b))) });checks+=1
+    for bad in [np.zeros((1,63)),np.full((1,64),float('nan')),np.full((1,64),2.)]:
+      try:predict(bad)
+      except ValueError:checks+=1
+      else:raise AssertionError('Invalid input accepted')
+    for task,seed in [('../x',0),('identity',True),('identity',5)]:
+      try:predict(x,task,seed)
+      except ValueError:checks+=1
+      else:raise AssertionError('Invalid selector accepted')
+    Path('VALIDATION.json').write_text(json.dumps({'passed':True,'checks':checks,'weights':15,'checks_detail':results,'claim':'Reload and contract checks only'},indent=2))
+    print('PASS',checks)

validation.log

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+PASS 21

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