Organize verified OpenSysOne publication payload

.gitattributes

@@ -39,3 +39,9 @@ snapshots/20260917-expanded-pilot-hf-backup/data/public-decisions-v2-20260917/tr
 profiles/20260917T075209Z-wrapup-evidence/payload/proofs/gui-selected/browser-check/desktop.png filter=lfs diff=lfs merge=lfs -text
 profiles/20260917T075209Z-wrapup-evidence/payload/reports/profile-report/accuracy.png filter=lfs diff=lfs merge=lfs -text
 profiles/20260917T075209Z-wrapup-evidence/payload/reports/profile-report/latency.png filter=lfs diff=lfs merge=lfs -text
+results/accuracy.png filter=lfs diff=lfs merge=lfs -text
+results/latency.png filter=lfs diff=lfs merge=lfs -text
+source/results/20260917-wrapup/profile-report/accuracy.png filter=lfs diff=lfs merge=lfs -text
+source/results/20260917-wrapup/profile-report/latency.png filter=lfs diff=lfs merge=lfs -text
+source/results/accuracy.png filter=lfs diff=lfs merge=lfs -text
+source/results/latency.png filter=lfs diff=lfs merge=lfs -text

archive/README.md

@@ -0,0 +1,25 @@
+# Archives and provenance
+
+The current release is easy to browse in [model/](../model/),
+[results/](../results/) and [docs/](../docs/README.md). This index groups the original
+experiment records, which remain at their existing versioned paths so saved
+links and integrity manifests continue to work.
+
+| Record | Entry point |
+| --- | --- |
+| Selected calibrated model and full evaluation | [FINAL_MODEL.json](../FINAL_MODEL.json) · [final/](../final/) |
+| Training wrap-up, nine checkpoint artifacts, matched profiles and raw predictions | [PROFILE_RESULTS.json](../PROFILE_RESULTS.json) · [profiles/](../profiles/) |
+| Earlier training and expanded-data snapshots | [CURRENT_SNAPSHOT.json](../CURRENT_SNAPSHOT.json) · [snapshots/](../snapshots/) |
+| Snapshot publication manifests | [publications/](../publications/) |
+| Original source revisions | [sources/](../sources/) |
+| Source snapshot for this publication layout | [publication-manifest.json](../publication-manifest.json) |
+| Complete inventory immediately before this cleanup | [inventory-before.json](inventory-before.json) |
+
+`CURRENT_SNAPSHOT.json` describes a historical training snapshot. Use
+`FINAL_MODEL.json` for the calibrated model and `PUBLICATION.json` for the
+verified publication layout. Each original pointer records its immutable payload
+commit and manifest checksum; use that revision when checking historical files.
+
+No historical checkpoint, prediction or timing file was moved or rewritten.
+The browsable [source/](../source/) tree reflects the current committed source;
+versioned source archives preserve the execution revisions of the experiments.

archive/current-source/manifest.json

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+    "scripts/final_validation.py": {
+      "sha256": "f5cea8bad330dd066f43b3dea5a977dbe3e8d5d69336d2850279bb743b628643",
+      "size": 15676
+    },
+    "scripts/fleet_campaign.py": {
+      "sha256": "e69fdff96f92c6943b7895be11df444f016d0b744a1b9441995a5f8bb7af9d54",
+      "size": 33706
+    },
+    "scripts/fleet_status.py": {
+      "sha256": "2519ced157ef4ac4fa449eebabae5740d7527778d578b4ac6720583010fa5217",
+      "size": 10289
+    },
+    "scripts/investigate_precision.py": {
+      "sha256": "609b744a926d8a45b87ba8d225e5312ee0c71a7096b21bd3589c5846e1dc847c",
+      "size": 10519
+    },
+    "scripts/launch_24h.py": {
+      "sha256": "39c26dc10535d3adac09209b0743edf2ed384563e732512ad62f8c6f37161a83",
+      "size": 19127
+    },
+    "scripts/prepare_expanded_data.py": {
+      "sha256": "5c05478b29c84218784690f3c7c3ec994615fec1c57826591607189f191c56b4",
+      "size": 18386
+    },
+    "scripts/prepare_expansion_backup.py": {
+      "sha256": "0b546fd6b96e34316fddcb06314fa072d1ddb9c06d7e6979ccd790421b93f419",
+      "size": 21154
+    },
+    "scripts/prepare_public_data.py": {
+      "sha256": "32ea84aa719818e1141b958b6ef27a85f7ddb86bfcd7c1585fc487b25253d977",
+      "size": 11886
+    },
+    "scripts/profile_inference.py": {
+      "sha256": "84e3032b5965049606e2486391eece49436ff66c1badad5dd5169d2f9eb0e97f",
+      "size": 33996
+    },
+    "scripts/publish_hf_final.py": {
+      "sha256": "278efc5878d7ebc5d1171f3a735d6c7d78275d93650e5a8a16c3f55b8353a577",
+      "size": 20356
+    },
+    "scripts/publish_hf_snapshot.py": {
+      "sha256": "b5fe16a00fcbc5ab97121428c6ce750275ee193438e3ffe24aac5e3bb325c018",
+      "size": 15165
+    },
+    "scripts/publish_publication.py": {
+      "sha256": "b2828499fba109b4956fbe98e6a4a013b2d7b528ebb88d23361c2d4b0b156d7b",
+      "size": 16926
+    },
+    "scripts/publish_wrapup_evidence.py": {
+      "sha256": "3da67fb23cbcce06b581ad61177f479365f3bc34c08e067fc919e6f30af14eef",
+      "size": 17197
+    },
+    "scripts/run_experiment.sh": {
+      "sha256": "661a6309fc54a2a8aff918f14a553c72dcb21730bd6a3cfc55d6ccd4700d11d3",
+      "size": 1213
+    },
+    "scripts/run_precision.sh": {
+      "sha256": "766d82b30cf3e83951f662685b4472ee053c7fbc171c4ff140823bf4d1c2782f",
+      "size": 998
+    },
+    "scripts/run_smoke.sh": {
+      "sha256": "39d59f2120f362729d1c2e384391b82be1e580dcc1aca0eaa7ab231115225574",
+      "size": 1054
+    },
+    "scripts/start_spark_candidate.sh": {
+      "sha256": "c2ca18b008a144de7cb264c9fcca634d68c4a8a317db3638e8a70b3dbcff064b",
+      "size": 6545
+    },
+    "scripts/summarize_profile.py": {
+      "sha256": "aeb8b30567256c76a11044786f8770ce583bdc5914ced7febf23b73e35d9036d",
+      "size": 32023
+    },
+    "scripts/verify_artifact.py": {
+      "sha256": "9833350e9d72c0065b15206bb71c5a8b5a6b3185219db90074e369ede563a985",
+      "size": 9971
+    },
+    "scripts/verify_expanded_startup.py": {
+      "sha256": "deabba820a3580e578d2d955d3ac3fd2e3982af999523c1d9a8b6dc6e2e1b755",
+      "size": 17887
+    },
+    "scripts/verify_playground.cjs": {
+      "sha256": "7a80780956a21c74f8dc804900da9f5cbe75060b3f2fc391e27ebcd520c73293",
+      "size": 7347
+    },
+    "scripts/verify_playground_layout.cjs": {
+      "sha256": "dea3fa07c568c141cd58fead8c547db7196f4a48fe4ea4cea0312d2ba7bd8bb4",
+      "size": 13341
+    },
+    "selection.py": {
+      "sha256": "be0a7a8496b5b830aa572ceba93606320f442063fd38180503fd6980dc1c578f",
+      "size": 5335
+    },
+    "smoke_data.py": {
+      "sha256": "06b3cbac1c8c4a86b8aecbee4459073cc3e46d4ddcd576392f3cb4805924f815",
+      "size": 1807
+    },
+    "smoke_train.py": {
+      "sha256": "8cdeb2b397177fc9c26638aaa871501ddab8e3871aa1573ecd98f66590f5c228",
+      "size": 19630
+    },
+    "tests/test_campaign.py": {
+      "sha256": "90b132f655db9e9fd7b71c4916d47064c51e65cc1cc0de1a69f0b0cdc8d1cad7",
+      "size": 8567
+    },
+    "tests/test_data_transition.py": {
+      "sha256": "695fc2113cac41b22ba910682656a445c08a881bd80cc0bd72b9aeb58250cd05",
+      "size": 3964
+    },
+    "tests/test_expanded_data.py": {
+      "sha256": "f272faecf6aaccf94fb460b9ff5105916ca3a97319606f1dd9d16003465e5f16",
+      "size": 7270
+    },
+    "tests/test_expanded_startup.py": {
+      "sha256": "39f48547760823ea2818c1cae38d0bbafe430f9620f484f745b06712909fce22",
+      "size": 6654
+    },
+    "tests/test_expansion_backup.py": {
+      "sha256": "3845483f6469dc30122ae31a2bcbf6b1ad7ce69663308266f831142cd5d4fd1e",
+      "size": 11522
+    },
+    "tests/test_final_validation.py": {
+      "sha256": "b6e08af776bfdfd9382d850958ea1422beabc2a3aa505a62670e5b06b48a7fc3",
+      "size": 9724
+    },
+    "tests/test_fleet_campaign.py": {
+      "sha256": "c5a64a4014c35b490c96d704f3762f3dcc0569a11ec6a1fe14507736c2c8cfe9",
+      "size": 20309
+    },
+    "tests/test_fleet_status.py": {
+      "sha256": "681eabe15b1de8380302a15f87cba411037da44c73a5e8b817455a7bb13dd061",
+      "size": 3469
+    },
+    "tests/test_playground.py": {
+      "sha256": "f63b58eef3b0f9c42aa3d445ceb4fa5462e936deb7b11e2e0fc962af6ed32c96",
+      "size": 10663
+    },
+    "tests/test_profile_inference.py": {
+      "sha256": "ef7041888fc96e8dd8fdc6f476ff9998abdb7391358063b18b597d9d9cb7153a",
+      "size": 13538
+    },
+    "tests/test_publish_hf_final.py": {
+      "sha256": "dd18fb43526d23e36166787a1d3eae5a12aedd6ffc54951459c97c37b1f2c3ee",
+      "size": 15397
+    },
+    "tests/test_publish_hf_snapshot.py": {
+      "sha256": "f48905828b51060f9a265505245eb10f814b47b8610ce5b01d67b2005c828edd",
+      "size": 9332
+    },
+    "tests/test_publish_publication.py": {
+      "sha256": "7d3c8fbbf04205b8e54a78e5d1c8256760440749fcd30341457440286a379801",
+      "size": 11974
+    },
+    "tests/test_publish_wrapup_evidence.py": {
+      "sha256": "8024d7524a0eb7478f643186138358f8f6d7f478cb4330e3193399e7a3ce645e",
+      "size": 12711
+    },
+    "tests/test_scorer.py": {
+      "sha256": "2c6f5d5e9ff634049cbe9c88b5f126710298b65ce603d7ced2072bc06e3974d8",
+      "size": 5091
+    },
+    "tests/test_selection.py": {
+      "sha256": "09fc10fd394ea870307175698a81b516a0bafc536757be918f5cacb86d124770",
+      "size": 4090
+    },
+    "tests/test_summarize_profile.py": {
+      "sha256": "307c13a3f29a1923edc04e1c217971bce7726307a07b71127d97dc403282ae03",
+      "size": 12682
+    },
+    "tests/test_training_harness.py": {
+      "sha256": "8b8445144c62fa7d7b947e3e858e5f5d732ca31cf2fae4372349381b564e76bb",
+      "size": 23833
+    },
+    "training_model.py": {
+      "sha256": "d5b0aefeeb5290816bc0b669aa0a8cbbe27f6a12b9cb23c141ac9b9ae9ee4e65",
+      "size": 7862
+    },
+    "web/playground.css": {
+      "sha256": "4cd80320c0218d4e51a1c0193048eacc63dbe62f8b24d0bceef9179e62f34211",
+      "size": 20388
+    },
+    "web/playground.html": {
+      "sha256": "dedfed05a7548af47cd7d51d76b730c99e6c4fc89bcc4b8641b4b1f50dfc8537",
+      "size": 9539
+    },
+    "web/playground.js": {
+      "sha256": "bec1dc7195f34af2cf3dd44fe66b2b4d01480a378a7ce0424f69a854393ab009",
+      "size": 22258
+    }
+  },
+  "source_commit": "40a270d89fd022474684490c904b281ccbcd03bb"
+}

archive/current-source/source.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:755ca94cd8369f54e7e80bd66d9de0362d789df3f89e4c1055c10eb76ecaab75
+size 2585164

archive/index.json

@@ -0,0 +1,50 @@
+{
+  "format": "opensysone-archive-index-v1",
+  "original_revision": "fde43242938be7a805f80ea33e8348ebd77688aa",
+  "pointers": {
+    "CURRENT_SNAPSHOT.json": {
+      "calibrated": false,
+      "manifest_path": "publications/20260917-expanded-pilot-hf-backup/d12660303ff7fff5b7ba634ea7277c858067c2da/manifest.json",
+      "manifest_sha256": "1011ca5fe624b5ced8573018c5943eddd3b40f556e11b5ac7065ca5f5dfb1097",
+      "path": "snapshots/20260917-expanded-pilot-hf-backup",
+      "payload_commit": "58f289696f58962a8ec98293d7b1abf9fd0c6b8b",
+      "published_utc": "2026-09-17T07:29:53.591335+00:00",
+      "repo_id": "andyshu/opensysone",
+      "snapshot_id": "20260917-expanded-pilot-hf-backup",
+      "source_commit": "d12660303ff7fff5b7ba634ea7277c858067c2da",
+      "training_source_commits": [
+        "24b8ccf60d388f9cbb184e03a6ae260a1f5a8b86",
+        "4a60423c39d70f8d50472ce4f4f7fa4a4bd9fce1"
+      ]
+    },
+    "FINAL_MODEL.json": {
+      "campaign": "/home/andy/ai/opensysone/runs/20260916T194403396250Z-fleet",
+      "manifest_path": "final/20260916T194403396250Z-fleet/backup_manifest.json",
+      "manifest_sha256": "47763a137e92890a0ddd32ba0f061e854cc7072a863f8a574edc941b63567c64",
+      "model_sha256": "e270e3da905604d97bf5a8f380ea308133403d1c4790a5c012cb1c12e9b6f348",
+      "path": "final/20260916T194403396250Z-fleet/model.pt",
+      "payload_commit": "8cb06c73102eb4b3fe8944600e915c9df33d4b4a",
+      "published_utc": "2026-09-17T09:20:51.125488+00:00",
+      "repo_id": "andyshu/opensysone",
+      "source_commit": "6729461ccaad32e239c2148fa0c8f9ca23513a7a"
+    },
+    "PROFILE_RESULTS.json": {
+      "evidence_id": "20260917T075209Z-wrapup-evidence",
+      "format": "opensysone-profile-results-pointer-v1",
+      "manifest_path": "profiles/20260917T075209Z-wrapup-evidence/manifest.json",
+      "manifest_sha256": "c3d9c4280db4681960e6196f30f24689aaefd6d58f1ffb51e8a40489c1a307de",
+      "path": "profiles/20260917T075209Z-wrapup-evidence",
+      "payload_commit": "26c91605206823df318af187c0fbb8fc167a3a16",
+      "repo_id": "andyshu/opensysone",
+      "source_commit": "d8fb5babd37790c0941dee7421fc3b13f088bbc4",
+      "verified_utc": "2026-09-17T09:27:33.949507+00:00"
+    }
+  },
+  "preserved_paths": [
+    "snapshots/",
+    "final/",
+    "profiles/",
+    "sources/",
+    "publications/"
+  ]
+}

archive/prepare_publication.py

@@ -0,0 +1,68 @@
+from pathlib import Path
+import json, hashlib, subprocess, shutil, sys, datetime, re, posixpath
+ROOT=Path('/home/andy/projects/opensysone')
+RUN=Path(__file__).resolve().parent
+sys.path.insert(0,str(ROOT))
+from scripts.publish_hf_snapshot import archive_source
+
+def digest(path): return hashlib.sha256(Path(path).read_bytes()).hexdigest()
+def write(path,value):
+ path.parent.mkdir(parents=True,exist_ok=True);path.write_text(json.dumps(value,indent=2,sort_keys=True)+'\n')
+def main():
+ assert not subprocess.check_output(['git','status','--porcelain'],cwd=ROOT),'Commit all publication source first'
+ revision=subprocess.check_output(['git','rev-parse','HEAD'],cwd=ROOT,text=True).strip()
+ inv=json.loads((RUN/'remote-inventory.json').read_text())
+ stage=RUN/'stage';stage.mkdir(exist_ok=False)
+ origins={}
+ archive_source(ROOT,revision,stage/'archive/current-source',browse=stage/'source')
+ for path in (stage/'source').rglob('*'):
+  if path.is_file():origins[str(path.relative_to(stage))]={'source_commit':revision,'source_path':str(path.relative_to(stage/'source'))}
+ def copy(dest,source,origin):
+  target=stage/dest;target.parent.mkdir(parents=True,exist_ok=True);shutil.copyfile(source,target)
+  assert target.read_bytes()==Path(source).read_bytes();origins[dest]=origin
+ mappings={'README.md':'HF_MODEL_CARD.md','docs/README.md':'docs/publication/overview.md','docs/reproduce.md':'docs/publication/reproduce.md','model/README.md':'docs/publication/model.md','archive/README.md':'docs/publication/archive.md'}
+ for dest,source in mappings.items():copy(dest,stage/'source'/source,{'source_commit':revision,'source_path':source})
+ report_prefix='profiles/20260917T075209Z-wrapup-evidence/payload/reports/profile-report/'
+ profile=json.loads((RUN/'PROFILE_RESULTS.json').read_text())
+ for name in ['report.md','summary.json','accuracy.csv','final_evaluation.csv','speed.csv','accuracy.png','latency.png']:
+  assert (ROOT/'results'/name).read_bytes()==(ROOT/'results/20260917-wrapup/profile-report'/name).read_bytes()
+  copy('results/'+name,ROOT/'results'/name,{'path':report_prefix+name,'revision':profile['payload_commit']})
+ results_index=(ROOT/'results/README.md').read_text().replace('(20260917-wrapup/profile-report/)', '(../source/results/20260917-wrapup/profile-report/)').replace('(../docs/operations/results-history.md)', '(../source/docs/operations/results-history.md)')
+ (stage/'results/README.md').write_text(results_index)
+ origins['results/README.md']={'source_commit':revision,'source_path':'results/README.md','transformation':'Relative historical links adapted to publication source/ subtree'}
+ final=json.loads((RUN/'FINAL_MODEL.json').read_text())
+ fleet=Path('/home/andy/ai/opensysone/runs/20260916T194403396250Z-fleet')
+ for name in ['metrics.json','manifest.json','correctness.json','data_filter.json']:
+  copy('results/'+name,fleet/'evaluation'/name,{'path':'final/'+fleet.name+'/evaluation/'+name,'revision':final['payload_commit']})
+ model=fleet/'evaluation/model.pt'; assert digest(model)==final['model_sha256']
+ copy('model/model.pt',model,{'path':final['path'],'revision':final['payload_commit'],'sha256':final['model_sha256']})
+ copy('archive/prepare_publication.py',Path(__file__),{'purpose':'Exact canonical-copy preparation and link-check procedure'})
+ copy('archive/inventory-before.json',RUN/'remote-inventory.json',{'revision':inv['revision'],'purpose':'Complete immutable inventory before publication cleanup'})
+ write(stage/'archive/index.json',{'format':'opensysone-archive-index-v1','original_revision':inv['revision'],'preserved_paths':['snapshots/','final/','profiles/','sources/','publications/'],'pointers':{name:json.loads((RUN/name).read_text()) for name in ['FINAL_MODEL.json','PROFILE_RESULTS.json','CURRENT_SNAPSHOT.json']}})
+ evaluation=json.loads((fleet/'evaluation/manifest.json').read_text())
+ write(stage/'model/provenance.json',{'format':'opensysone-published-model-v1','sha256':final['model_sha256'],'original_release':final,'base':evaluation['model_provenance'],'evaluation_source_commit':evaluation['source_commit'],'selected_step':evaluation['selected_step'],'warm_start_parent_step':1500,'temperature':1.7458220720291138,'calibration_examples':510,'format_note':'Custom adapter/head checkpoint requiring separately pinned local backbone; no reserialization'})
+ files={str(p.relative_to(stage)):{'source':str(p),'sha256':digest(p),'size':p.stat().st_size} for p in stage.rglob('*') if p.is_file()}
+ source_files={name for name in files if name.startswith('source/')}
+ deletes=sorted(f['path'] for f in inv['files'] if f['path'].startswith('source/') and f['path'] not in source_files)
+ manifest={'format':'opensysone-publication-manifest-v1','created_utc':datetime.datetime.now(datetime.timezone.utc).isoformat(),'source_commit':revision,'original_revision':inv['revision'],'files':{name:{'sha256':row['sha256'],'size':row['size'],'origin':origins.get(name,{'derived_from':'immutable inventory and release pointers'})} for name,row in sorted(files.items())},'source_view_deletions':deletes,'historical_prefixes_preserved':['snapshots/','final/','profiles/','sources/','publications/'],'note':'Canonical copies and reorganized source documentation; model and historical evidence bytes unchanged.'}
+ write(stage/'publication-manifest.json',manifest)
+ path=stage/'publication-manifest.json';files['publication-manifest.json']={'source':str(path),'sha256':digest(path),'size':path.stat().st_size}
+ plan={'repo_id':inv['repo_id'],'expected_revision':inv['revision'],'expected_private':inv['private'],'source_commit':revision,'inventory_path':str(RUN/'remote-inventory.json'),'files':files,'delete_source_files':deletes}
+ write(RUN/'publication-plan.json',plan)
+ # Check links in the actual publication locations; source HF templates are validated in their mapped locations.
+ all_paths={f['path'] for f in inv['files']}|set(files)|{'PUBLICATION.json'}
+ all_paths-=set(deletes)
+ source_templates={'source/HF_MODEL_CARD.md'}|{name for name in files if name.startswith('source/docs/publication/')}
+ broken=[];checked=0
+ for name,row in files.items():
+  if not name.endswith('.md') or name in source_templates or name.startswith('source/results/'):continue
+  for target in re.findall(r'!?\[[^\]]*\]\(([^)]+)\)',Path(row['source']).read_text()):
+   target=target.split('#',1)[0].split('?',1)[0]
+   if not target or '://' in target or target.startswith(('/', 'mailto:','app:')):continue
+   resolved=posixpath.normpath(posixpath.join(posixpath.dirname(name),target))
+   checked+=1
+   if resolved not in all_paths and not any(p.startswith(resolved.rstrip('/')+'/') for p in all_paths):broken.append({'file':name,'target':target,'resolved':resolved})
+ write(RUN/'link-check.json',{'status':'passed' if not broken else 'failed','checked':checked,'broken':broken,'source_templates_checked_at_publication_locations':True})
+ assert not broken,broken
+ print(json.dumps({'plan':str(RUN/'publication-plan.json'),'stage':str(stage),'files':len(files),'bytes':sum(x['size'] for x in files.values()),'source_only_deletions':deletes,'links_checked':checked,'source_commit':revision}))
+if __name__=='__main__':main()

docs/README.md

@@ -0,0 +1,49 @@
+# Publication guide
+
+OpenSysOne is an independent decision-scoring experiment inspired by
+[Jev](https://typesafe.ai/) and the TypeSafe team. The current release is the
+completed, calibrated Qwen3 4B scorer evaluated on 17 September 2026.
+
+The published repository has a short entry path:
+
+| Directory | Contents |
+| --- | --- |
+| [model/](../model/README.md) | Calibrated checkpoint, exact hash, base-model requirements and reconstruction constraints |
+| [results/](../results/) | [Final report](../results/report.md), metrics, CSV tables and standalone charts |
+| [docs/](README.md) | This guide and [reproduction instructions](reproduce.md) |
+| [source/](../source/) | Complete committed project tree, including code, tests, examples, frontend, documentation and small evidence |
+| [archive/](../archive/README.md) | Index to historical checkpoints, source revisions and publication records |
+
+The [API guide](../source/docs/usage/jev-api.md) describes the Jev-compatible
+request shape. The [playground guide](../source/docs/usage/playground.md) describes
+the local browser interface. Neither the Hugging Face repository nor its model
+card is a hosted inference service.
+
+## Current files and immutable history
+
+The front directories provide convenient copies and navigation. Exact release
+identity comes from the existing pointers and their recorded Hub payload commits:
+
+- [FINAL_MODEL.json](../FINAL_MODEL.json): calibrated model hash and original final-evaluation manifest.
+- [PROFILE_RESULTS.json](../PROFILE_RESULTS.json): completed profiling, stopped-training evidence and source archives.
+- [CURRENT_SNAPSHOT.json](../CURRENT_SNAPSHOT.json): earlier training snapshot, including resumable state; it is not the final-model pointer.
+
+Historical `final/`, `profiles/`, `snapshots/`, `sources/` and `publications/`
+payloads remain available at their recorded paths. Their manifests and checksums
+are not rewritten to fit this presentation. Use a pointer's `payload_commit`
+when retrieving its `path` and `manifest_path` for a reproducible download.
+
+`source/` is the complete publication source tree. The exact training and
+evaluation revisions are separately recorded in artifact metadata and immutable
+source archives; a later documentation revision is not a new model training run.
+Keep source-relative paths intact when executing commands.
+
+## Scope
+
+The calibrated checkpoint contains adapter/head parameters and requires the
+pinned pretrained base. It is a custom OpenSysOne artifact. Results support the
+reported benchmark comparisons, with separate calibration and validation-only
+selection; they do not establish general intelligence or calibration on arbitrary
+tasks. The release preserves the repository's existing license metadata and all
+upstream data notices. See the [model notes](../model/README.md) and
+[measured report](../results/report.md) before interpreting probabilities or speed.

docs/reproduce.md

@@ -0,0 +1,133 @@
+# Reproduce the published scorer
+
+The published artifact is a custom adapter/head checkpoint requiring a pinned
+local base and the supplied project code. These instructions describe the
+evaluated Linux/GB10 setup and its current path constraints, not a portable
+one-command installation.
+
+## Retrieve and verify
+
+Download the published tree with your authorized Hugging Face client, retaining
+the sibling `source/` and `model/` directories. For immutable evidence, retrieve
+the path in [FINAL_MODEL.json](../FINAL_MODEL.json) at its recorded `payload_commit`
+and verify its model and manifest hashes. The front model must match the same
+bytes. From the downloaded repository root:
+
+```bash
+sha256sum model/model.pt
+cd source
+```
+
+Expected model SHA-256:
+
+```text
+e270e3da905604d97bf5a8f380ea308133403d1c4790a5c012cb1c12e9b6f348
+```
+
+Keep `source/` intact. Scripts import modules relative to its root, examples and
+web assets use that layout, and evaluation's data signature hashes
+`training_model.py` relative to the working directory. Run the following commands
+from `source/`.
+
+## Environment and pinned base
+
+The completed [evaluation manifest](../final/20260916T194403396250Z-fleet/evaluation/manifest.json)
+records NVIDIA GB10, CUDA 13.0 and these installed packages:
+
+| Package | Recorded version |
+| --- | --- |
+| torch | `2.11.0+cu130` |
+| transformers | `5.15.0` |
+| pyarrow | `25.0.1` |
+| numpy | `2.5.2` |
+
+These are measured environment identifiers, not a claim that the same CUDA build
+is available on every platform. The evaluated isolated interpreter is
+`/home/andy/ai/envs/opensysone/bin/python`. On another machine, create an isolated
+compatible environment and verify it against the recorded evidence before
+claiming reproduction. No dependency version should be inferred from the model
+card alone.
+
+The base is `Qwen/Qwen3-4B-Instruct-2507` at revision
+`cdbee75f17c01a7cc42f958dc650907174af0554`. On the recorded `/home/andy` account,
+the existing CPU-only downloader retrieves that pin and writes its provenance:
+
+```bash
+/home/andy/ai/envs/opensysone/bin/python scripts/download_candidate.py \
+  --model Qwen/Qwen3-4B-Instruct-2507
+```
+
+It writes under the invoking user's home. The artifact loader specifically
+expects `/home/andy/ai/models/opensysone/Qwen3-4B-Instruct-2507-cdbee75f`, including
+`opensysone-provenance.json`. Another home directory requires arranging the pinned
+base at that recorded location; the current loader has no base-path override.
+Do not modify the released checkpoint to change its paths. See
+[model reconstruction notes](../model/README.md).
+
+## Local scoring and API
+
+Before loading a model, inspect available memory and existing GPU jobs:
+
+```bash
+free -b
+nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv
+```
+
+The harness checks for at least 24 GiB currently available host memory, restores
+OOM adjustment 0 and applies a 16 GiB CUDA allocation cap. The verified path uses
+FP32. The example is an invented request, not a benchmark measurement:
+
+```bash
+/home/andy/ai/envs/opensysone/bin/python jev_harness.py \
+  --backend local --checkpoint ../model/model.pt \
+  --request examples/jev_request.json --device cuda --max-tokens 1024
+```
+
+To run the same scorer as a loopback API on an unused local port:
+
+```bash
+/home/andy/ai/envs/opensysone/bin/python jev_harness.py \
+  --backend serve --checkpoint ../model/model.pt \
+  --device cuda --max-tokens 1024 --port 18081
+```
+
+In another terminal:
+
+```bash
+curl --fail http://127.0.0.1:18081/health
+```
+
+The server binds to `127.0.0.1`; it does not expose a public endpoint. Read the
+[API guide](../source/docs/usage/jev-api.md) for request shape, optional local
+authentication and hosted Jev comparison. Hosted Jev needs a separate credential
+and was not exercised in the published evaluation. The
+[playground guide](../source/docs/usage/playground.md) covers the browser interface
+and its fixed checkpoint catalog. Existing machine-specific run paths in usage
+guides are operational records, not files downloaded with the model.
+
+The 1,024-token limit applies separately to each complete chat-formatted candidate
+prompt. Excess-length input is rejected rather than truncated. The artifact's
+default training limit is 512, so preserve `--max-tokens 1024` for the documented
+inference configuration. Scalar calibration is applied by the harness.
+
+## Reproducing evidence
+
+The [final report](../results/report.md) separates the full 2,042-decision test and
+768-decision Social IQA holdout from the matched 320-decision speed-profile sample
+and 383 expansion diagnostics. It reports the baseline definition, repeat counts,
+exact sample sizes and confidence-interval direction.
+
+Use [PROFILE_RESULTS.json](../PROFILE_RESULTS.json) and its immutable manifest to
+retrieve the frozen profiling protocol, requests, raw predictions, timings and
+source archives. [CURRENT_SNAPSHOT.json](../CURRENT_SNAPSHOT.json) records earlier
+training backups; it is not the final selected model. Historical dataset and
+source manifests record the exact hashes required for retraining or evaluation.
+For resume, keep each `checkpoint.pt`, sibling `best.pt` and matching validation
+evidence together. The calibrated `model.pt` is an inference artifact without
+optimizer state.
+
+Replay requires those complete evidence bundles and recorded source revisions;
+the convenient current `source/` view alone is not a substitute for the frozen
+training/evaluation provenance. Do not select a new checkpoint or fit temperatures
+using the published test or holdout results. No new inference is required to read
+the existing report and integrity manifests.

model/README.md

@@ -0,0 +1,72 @@
+# Calibrated 4B model
+
+[model.pt](model.pt) is the completed OpenSysOne decision-scoring artifact. It
+stores learned additive adapters, a scalar head, reconstruction metadata and a
+global temperature. Pretrained backbone weights are required separately.
+
+| Property | Recorded value |
+| --- | --- |
+| Format | `opensysone-adapter-v1`, custom PyTorch checkpoint |
+| Base | `Qwen/Qwen3-4B-Instruct-2507` |
+| Base revision | `cdbee75f17c01a7cc42f958dc650907174af0554` |
+| Precision | FP32 |
+| Adapters | Rank 8, alpha 16; 16,517,633 trainable adapter/head parameters |
+| Selected weights | Spark B refinement step 1,500, retained at expanded branch step 0 |
+| Temperature | `1.7458220720291138`, fitted on 510 separate calibration decisions |
+| Verified inference limit | 1,024 complete formatted candidate tokens; no silent truncation |
+
+The calibrated artifact SHA-256 is:
+
+```text
+e270e3da905604d97bf5a8f380ea308133403d1c4790a5c012cb1c12e9b6f348
+```
+
+The original release remains at
+[`final/20260916T194403396250Z-fleet/model.pt`](../final/20260916T194403396250Z-fleet/model.pt),
+with [its immutable manifest](../final/20260916T194403396250Z-fleet/backup_manifest.json).
+[FINAL_MODEL.json](../FINAL_MODEL.json) records the exact payload commit, model hash,
+manifest hash and publication source. The `model/model.pt` front copy has identical
+bytes; moving the presentation does not change the artifact.
+
+## Source and lineage
+
+The selected checkpoint records training source
+`24b8ccf60d388f9cbb184e03a6ae260a1f5a8b86`; its warm-start parent's source was
+`4a60423c39d70f8d50472ce4f4f7fa4a4bd9fce1`. Final evaluation used
+`07f10e791061a679b829ed1dc5b33897e001d67d`.
+The final [evaluation manifest](../final/20260916T194403396250Z-fleet/evaluation/manifest.json)
+records source-file hashes, model pin, dataset signature, configuration and packages.
+
+The selected branch step is zero because it retains the already-trained parent's
+weights. CPU lineage checks confirmed all 506 trainable tensors equal the parent,
+and that calibration leaves them unchanged. Later expanded-data checkpoint 159
+was evaluated but not promoted. Its diagnostic results do not describe a different
+deployed model.
+
+## Reconstruction constraint
+
+The existing loader in [experiment.py](../source/experiment.py) reads the base path
+from checkpoint metadata. For this release that path is:
+
+```text
+/home/andy/ai/models/opensysone/Qwen3-4B-Instruct-2507-cdbee75f
+```
+
+That directory must contain the pinned local base, tokenizer and matching
+`opensysone-provenance.json`. The loader uses local files only and verifies base,
+prompt and adapter versions. The checkpoint itself may be downloaded elsewhere
+and supplied through `--checkpoint`; relocating it does not relocate the saved
+base path. The current CLI has no base-path override. Do not rewrite and re-save
+the published checkpoint to disguise that constraint: doing so changes its hash.
+
+Use the project's [reproduction guide](../docs/reproduce.md) and
+[Jev-compatible harness](../source/docs/usage/jev-api.md). This is not a drop-in
+Transformers or standard PEFT package, and its serialization is intended for
+trusted, hash-verified project artifacts. The final artifact excludes optimizer
+state; resumable checkpoints and sibling validation evidence are preserved in
+the [historical bundles](../archive/README.md).
+
+Probabilities are normalized over the supplied choices. Temperature calibration
+does not change the chosen answer. It was fitted on known task families; Social
+IQa holdout ECE remains 8.30%, so calibration on arbitrary tasks is unproven.
+See the [full measured results](../results/report.md).

model/model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e270e3da905604d97bf5a8f380ea308133403d1c4790a5c012cb1c12e9b6f348
+size 66221579

model/provenance.json

@@ -0,0 +1,26 @@
+{
+  "base": {
+    "license": "apache-2.0",
+    "model_id": "Qwen/Qwen3-4B-Instruct-2507",
+    "revision": "cdbee75f17c01a7cc42f958dc650907174af0554"
+  },
+  "calibration_examples": 510,
+  "evaluation_source_commit": "07f10e791061a679b829ed1dc5b33897e001d67d",
+  "format": "opensysone-published-model-v1",
+  "format_note": "Custom adapter/head checkpoint requiring separately pinned local backbone; no reserialization",
+  "original_release": {
+    "campaign": "/home/andy/ai/opensysone/runs/20260916T194403396250Z-fleet",
+    "manifest_path": "final/20260916T194403396250Z-fleet/backup_manifest.json",
+    "manifest_sha256": "47763a137e92890a0ddd32ba0f061e854cc7072a863f8a574edc941b63567c64",
+    "model_sha256": "e270e3da905604d97bf5a8f380ea308133403d1c4790a5c012cb1c12e9b6f348",
+    "path": "final/20260916T194403396250Z-fleet/model.pt",
+    "payload_commit": "8cb06c73102eb4b3fe8944600e915c9df33d4b4a",
+    "published_utc": "2026-09-17T09:20:51.125488+00:00",
+    "repo_id": "andyshu/opensysone",
+    "source_commit": "6729461ccaad32e239c2148fa0c8f9ca23513a7a"
+  },
+  "selected_step": 0,
+  "sha256": "e270e3da905604d97bf5a8f380ea308133403d1c4790a5c012cb1c12e9b6f348",
+  "temperature": 1.7458220720291138,
+  "warm_start_parent_step": 1500
+}

results/README.md

@@ -0,0 +1,18 @@
+# Results
+
+Start with [the completed accuracy and speed report](report.md).
+
+| File | Contents |
+| --- | --- |
+| [report.md](report.md) | Accuracy, calibration, latency, limitations and provenance |
+| [accuracy.png](accuracy.png) / [latency.png](latency.png) | Standalone charts |
+| [final_evaluation.csv](final_evaluation.csv) | Full test and holdout metrics |
+| [accuracy.csv](accuracy.csv) | Matched inference-method accuracy |
+| [speed.csv](speed.csv) | All 12 workloads, medians, exploratory p95 and serial throughput |
+| [summary.json](summary.json) | Machine-readable results and source/checkpoint hashes |
+
+These publication copies are byte-identical to the completed report under
+[20260917-wrapup/profile-report](../source/results/20260917-wrapup/profile-report/). Timestamped
+folders preserve the original experiment and audit evidence; their files are
+unchanged. Historical smoke experiments are described in
+[the results history](../source/docs/operations/results-history.md).

results/accuracy.csv

@@ -0,0 +1,41 @@
+scope,split,method,family,count,correct,accuracy
+profile,heldout,trained,overall,320,285,0.890625
+profile,heldout,trained,arc,64,62,0.96875
+profile,heldout,trained,banking,64,62,0.96875
+profile,heldout,trained,boolq,64,61,0.953125
+profile,heldout,trained,snli,64,53,0.828125
+profile,heldout,trained,social,64,47,0.734375
+profile,diagnostics,trained,overall,383,303,0.7911227154046997
+profile,diagnostics,trained,commonsenseqa,128,98,0.765625
+profile,diagnostics,trained,hellaswag,128,97,0.7578125
+profile,diagnostics,trained,piqa,127,108,0.8503937007874016
+profile,heldout,base_verifier,overall,320,259,0.809375
+profile,heldout,base_verifier,arc,64,58,0.90625
+profile,heldout,base_verifier,banking,64,54,0.84375
+profile,heldout,base_verifier,boolq,64,58,0.90625
+profile,heldout,base_verifier,snli,64,45,0.703125
+profile,heldout,base_verifier,social,64,44,0.6875
+profile,diagnostics,base_verifier,overall,383,298,0.7780678851174935
+profile,diagnostics,base_verifier,commonsenseqa,128,90,0.703125
+profile,diagnostics,base_verifier,hellaswag,128,101,0.7890625
+profile,diagnostics,base_verifier,piqa,127,107,0.84251968503937
+profile,heldout,base_label,overall,320,276,0.8625
+profile,heldout,base_label,arc,64,60,0.9375
+profile,heldout,base_label,banking,64,62,0.96875
+profile,heldout,base_label,boolq,64,57,0.890625
+profile,heldout,base_label,snli,64,48,0.75
+profile,heldout,base_label,social,64,49,0.765625
+profile,diagnostics,base_label,overall,383,303,0.7911227154046997
+profile,diagnostics,base_label,commonsenseqa,128,90,0.703125
+profile,diagnostics,base_label,hellaswag,128,109,0.8515625
+profile,diagnostics,base_label,piqa,127,104,0.8188976377952756
+profile,heldout,expanded,overall,320,284,0.8875
+profile,heldout,expanded,arc,64,62,0.96875
+profile,heldout,expanded,banking,64,62,0.96875
+profile,heldout,expanded,boolq,64,61,0.953125
+profile,heldout,expanded,snli,64,52,0.8125
+profile,heldout,expanded,social,64,47,0.734375
+profile,diagnostics,expanded,overall,383,312,0.814621409921671
+profile,diagnostics,expanded,commonsenseqa,128,96,0.75
+profile,diagnostics,expanded,hellaswag,128,106,0.828125
+profile,diagnostics,expanded,piqa,127,110,0.8661417322834646

results/correctness.json

@@ -0,0 +1,9 @@
+{
+  "branch_chunks_1_probability_max_abs": 0.0,
+  "branch_chunks_2_probability_max_abs": 2.6496127247810364e-07,
+  "branch_chunks_4_probability_max_abs": 1.7369166016578674e-07,
+  "question_isolation_probability_max_abs": 0.0,
+  "candidate_permutation_probability_max_abs": 0.0,
+  "repeat_probability_max_abs": 0.0,
+  "tolerance_probability_abs": 0.0001
+}

results/data_filter.json

@@ -0,0 +1,100 @@
+{
+  "calibration": {
+    "retained": 510,
+    "dropped_ids": [
+      "boolq:validation:200",
+      "boolq:validation:1836"
+    ],
+    "family_counts": {
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+      "snli": 128,
+      "banking": 128,
+      "arc": 128
+    },
+    "retained_id_sha256": "8a0d4add2dd95717d34915195dc7100878b8f443bf714656d54b336b629f6476",
+    "max_branch_tokens": 481
+  },
+  "holdout": {
+    "retained": 768,
+    "dropped_ids": [],
+    "family_counts": {
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+    },
+    "retained_id_sha256": "805387bd9156d12d4d40b33e5926f209451f323814ad876a3f04a8abece3ae0b",
+    "max_branch_tokens": 123
+  },
+  "test": {
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+    "dropped_ids": [
+      "boolq:validation:1681",
+      "boolq:validation:1661",
+      "boolq:validation:2150",
+      "boolq:validation:2153",
+      "boolq:validation:3154",
+      "boolq:validation:561"
+    ],
+    "family_counts": {
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+      "boolq": 506,
+      "arc": 512,
+      "snli": 512
+    },
+    "retained_id_sha256": "343960f63f954a0c05884459a13a3ef8560fd98c2caead3aa3c3b70022917ec5",
+    "max_branch_tokens": 440
+  },
+  "train": {
+    "retained": 80765,
+    "dropped_ids": [
+      "boolq:train:5085",
+      "boolq:train:1430",
+      "boolq:train:353",
+      "boolq:train:3547",
+      "boolq:train:5618",
+      "boolq:train:3872",
+      "boolq:train:6128",
+      "boolq:train:899",
+      "boolq:train:711",
+      "boolq:train:6969",
+      "boolq:train:7410",
+      "boolq:train:9405",
+      "boolq:train:3362",
+      "boolq:train:8317",
+      "boolq:train:4726",
+      "boolq:train:3163",
+      "boolq:train:7445",
+      "boolq:train:2181",
+      "boolq:train:8140",
+      "boolq:train:2141",
+      "boolq:train:204",
+      "boolq:train:1505",
+      "boolq:train:2352",
+      "boolq:train:9421",
+      "boolq:train:5517",
+      "boolq:train:6869",
+      "piqa:train:13223"
+    ],
+    "family_counts": {
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+      "snli": 20000,
+      "boolq": 7962,
+      "arc": 3345,
+      "hellaswag": 16000,
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+      "commonsenseqa": 9490
+    },
+    "retained_id_sha256": "79e791c0e8b0f4312fdadcd62042a689d32d2bcf04c80419c8eebd94db99249c",
+    "max_branch_tokens": 509
+  },
+  "validation": {
+    "retained": 512,
+    "dropped_ids": [],
+    "family_counts": {
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+      "snli": 128,
+      "banking": 128,
+      "arc": 128
+    },
+    "retained_id_sha256": "b0ea1f550bee363d58849a00295771707fca92d9d8e630840d0f870a91a9f8a7",
+    "max_branch_tokens": 477
+  }
+}

results/final_evaluation.csv

@@ -0,0 +1,29 @@
+split,method,family,n,accuracy,nll,brier_multiclass_sum,ece_top_label_10_equal_width_bins
+test,trained,overall,2042,0.9289911851126347,0.2548728303419246,0.11724661735348839,0.042709464978984944
+test,trained,banking,512,0.978515625,0.12226520271792657,0.037994640677064956,0.016155527671799064
+test,trained,boolq,506,0.8952569169960475,0.30082020614212623,0.1645830420203524,0.061629810352099273
+test,trained,arc,512,0.94140625,0.23545075006863606,0.09582074176202018,0.03452872653724626
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+test,calibrated,overall,2042,0.9289911851126347,0.2050675208059285,0.10981914968288821,0.010821329873058868
+test,calibrated,banking,512,0.978515625,0.08680507836434942,0.03854156218229658,0.010919157532043755
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+test,calibrated,arc,512,0.94140625,0.19397132420263175,0.09487676147069625,0.02615507983136922
+test,calibrated,snli,512,0.900390625,0.29067448104592586,0.15610599858459887,0.03427618817659095
+test,base,overall,2042,0.8447600391772772,1.6827827308918881,0.2927494974423544,0.13822684455279854
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+test,base_calibrated,arc,512,0.9296875,0.2752359951973631,0.13593068181824372,0.05345189612125978
+test,base_calibrated,snli,512,0.7109375,0.6701154473084898,0.4134977117489767,0.0982505488791503
+holdout,trained,overall,768,0.7291666666666666,0.9046048978141895,0.41865507801212026,0.1554523635810862
+holdout,trained,social,768,0.7291666666666666,0.9046048978141895,0.41865507801212026,0.1554523635810862
+holdout,calibrated,overall,768,0.7291666666666666,0.6782619158996491,0.37973740706466513,0.08299602890231957
+holdout,calibrated,social,768,0.7291666666666666,0.6782619158996491,0.37973740706466513,0.08299602890231957
+holdout,base,overall,768,0.703125,2.087191693346451,0.5129237150352639,0.22570987732615322
+holdout,base,social,768,0.703125,2.087191693346451,0.5129237150352639,0.22570987732615322
+holdout,base_calibrated,overall,768,0.703125,0.7425018713104995,0.4306530777581018,0.08665639813989401
+holdout,base_calibrated,social,768,0.703125,0.7425018713104995,0.4306530777581018,0.08665639813989401

results/manifest.json

@@ -0,0 +1,96 @@
+{
+  "pid": 1673217,
+  "source_commit": "07f10e791061a679b829ed1dc5b33897e001d67d",
+  "checkpoint_sha256": "c4f781e80ade257544100b03c525709d0559eb2bf6a992c97708554c7d360aca",
+  "selected_step": 0,
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+}

results/report.md

@@ -0,0 +1,85 @@
+# Accuracy and inference profile
+
+Frozen selection: **gx10-4b-expanded**, checkpoint step **0**. Selection used validation only; the following evaluation does not change the winner.
+
+The selected branch's step 0 retains the exact warm-start parent weights from step 1500; it is not an untrained base model. The saved CPU proof verifies equality of all trainable tensors. Validation-score tie: `gx10-4b-expanded`, `spark-b-4b-refinement`. The coordinator selected the first name in deterministic alphabetical order. The branch's latest checkpoint, step 159, was evaluated but not promoted by the fixed validation rule. Its expansion-diagnostic results are post-selection comparisons.
+
+## Complete final evaluation
+
+| Split | Decisions | Calibrated selected accuracy | Calibrated base accuracy | Selected NLL | Base NLL |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| test | 2042 | 92.90% | 84.48% | 0.2051 | 0.4527 |
+| holdout | 768 | 72.92% | 70.31% | 0.6783 | 0.7425 |
+
+Paired 95% source-group bootstrap intervals are **calibrated selected minus calibrated base**. Positive accuracy differences favor the selected model; negative NLL/Brier differences favor it.
+
+- test: accuracy +8.42 pp [+6.85, +9.89]; NLL -0.2477 [-0.2769, -0.2188]; Brier -0.1450 [-0.1637, -0.1258].
+- holdout: accuracy +2.60 pp [+0.13, +5.34]; NLL -0.0642 [-0.1144, -0.0160]; Brier -0.0509 [-0.0755, -0.0264].
+
+## Matched profiling accuracy
+
+320 held-out decisions and 383 expansion diagnostics are identical across methods. Each family row reports its exact sample count. Differences below are descriptive point estimates.
+
+| Sample / family | N | Selected scorer | Base yes/no verifier | Base one-token label | Expanded scorer |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| heldout / overall | 320 | 89.06% | 80.94% | 86.25% | 88.75% |
+| heldout / arc | 64 | 96.88% | 90.62% | 93.75% | 96.88% |
+| heldout / banking | 64 | 96.88% | 84.38% | 96.88% | 96.88% |
+| heldout / boolq | 64 | 95.31% | 90.62% | 89.06% | 95.31% |
+| heldout / snli | 64 | 82.81% | 70.31% | 75.00% | 81.25% |
+| heldout / social | 64 | 73.44% | 68.75% | 76.56% | 73.44% |
+| diagnostics / overall | 383 | 79.11% | 77.81% | 79.11% | 81.46% |
+| diagnostics / commonsenseqa | 128 | 76.56% | 70.31% | 70.31% | 75.00% |
+| diagnostics / hellaswag | 128 | 75.78% | 78.91% | 85.16% | 82.81% |
+| diagnostics / piqa | 127 | 85.04% | 84.25% | 81.89% | 86.61% |
+
+Expanded scorer checkpoint step: **159**. Paired expanded-minus-selected accuracy:
+
+- heldout/overall: -0.31 pp; expanded alone correct 2, selected alone correct 3 (N=320).
+- heldout/arc: +0.00 pp; expanded alone correct 0, selected alone correct 0 (N=64).
+- heldout/banking: +0.00 pp; expanded alone correct 0, selected alone correct 0 (N=64).
+- heldout/boolq: +0.00 pp; expanded alone correct 0, selected alone correct 0 (N=64).
+- heldout/snli: -1.56 pp; expanded alone correct 1, selected alone correct 2 (N=64).
+- heldout/social: +0.00 pp; expanded alone correct 1, selected alone correct 1 (N=64).
+- diagnostics/overall: +2.35 pp; expanded alone correct 14, selected alone correct 5 (N=383).
+- diagnostics/commonsenseqa: -1.56 pp; expanded alone correct 0, selected alone correct 2 (N=128).
+- diagnostics/hellaswag: +7.03 pp; expanded alone correct 11, selected alone correct 2 (N=128).
+- diagnostics/piqa: +1.57 pp; expanded alone correct 3, selected alone correct 1 (N=127).
+
+The base label method computes one constrained next-token label from a prompt containing all options. The two verifier methods score each candidate independently. No free-text reasoning or JSON generation is timed.
+
+## Warm local speed
+
+Each cell uses 2 warmups and 10 measured repeats. Times are median / exploratory p95 in seconds. Ratios are **base median ÷ selected median**: **above 1 means the selected scorer is faster; below 1 means the baseline is faster**. `summary.json` and `speed.csv` also report requests, questions and choice probabilities per second, derived from serial warm median latency; these do not measure concurrent serving.
+
+| State tokens | Questions × choices | Selected median / p95 | Base verifier median / p95 | Base label median / p95 | Verifier / selected | Label / selected |
+| ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| 128 | 1 × 2 | 0.4507 / 0.4536 | 0.4048 / 0.4083 | 0.2056 / 0.2076 | 0.90× | 0.46× |
+| 128 | 1 × 4 | 0.9023 / 0.9065 | 0.8059 / 0.8119 | 0.2128 / 0.2154 | 0.89× | 0.24× |
+| 128 | 1 × 16 | 3.6218 / 3.7210 | 3.2337 / 3.2489 | 0.3059 / 0.3089 | 0.89× | 0.08× |
+| 128 | 4 × 2 | 1.8013 / 1.8038 | 1.6120 / 1.6169 | 0.8250 / 0.8318 | 0.89× | 0.46× |
+| 128 | 4 × 4 | 3.6008 / 3.6126 | 3.2206 / 3.2300 | 0.8501 / 0.8513 | 0.89× | 0.24× |
+| 128 | 16 × 2 | 7.2279 / 7.2993 | 6.4655 / 6.4843 | 3.3011 / 3.3170 | 0.89× | 0.46× |
+| 768 | 1 × 2 | 1.8398 / 1.8417 | 1.5885 / 1.5957 | 0.8080 / 0.8120 | 0.86× | 0.44× |
+| 768 | 1 × 4 | 3.7095 / 3.7701 | 3.1766 / 3.1828 | 0.8184 / 0.8239 | 0.86× | 0.22× |
+| 768 | 1 × 16 | 14.6790 / 14.6895 | 12.7095 / 12.7259 | 0.9420 / 0.9437 | 0.87× | 0.06× |
+| 768 | 4 × 2 | 7.3425 / 7.3540 | 6.3598 / 6.3755 | 3.2318 / 3.2416 | 0.87× | 0.44× |
+| 768 | 4 × 4 | 14.6808 / 14.6930 | 12.6692 / 12.7184 | 3.2784 / 3.2847 | 0.86× | 0.22× |
+| 768 | 16 × 2 | 29.3552 / 29.3689 | 25.4125 / 25.4531 | 12.9224 / 12.9458 | 0.87× | 0.44× |
+
+![Warm latency comparison](latency.png)
+
+![Matched sample accuracy](accuracy.png)
+
+## Scope and evidence
+
+- Profile accuracy uses fixed matched samples; point differences have no significance claim.
+- Base labels jointly condition on all options; verifier paths score each option independently.
+- Profiles use raw probabilities without applying an artifact temperature.
+- p95 is an exploratory nearest-rank statistic from the recorded small repeat count.
+- Warm local timings exclude model loading and network latency; no shared-prefix optimization is used.
+- Throughput is derived from serial warm median latency; it makes no concurrent-serving capacity claim.
+- Expanded comparisons are post-selection diagnostics and cannot change the frozen winner.
+
+`summary.json` contains metrics, intervals and provenance. `final_evaluation.csv`, `accuracy.csv` and `speed.csv` contain table/chart data.
+Frozen protocol SHA-256: `b6dde5e425239455e9e31a44bc22b151b0ee66ec827c538aaca3308e29441c15`.

results/speed.csv

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results/summary.json

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+      "base_label_over_trained": 0.44020721948827785,
+      "base_verifier_over_trained": 0.8656913216637209,
+      "case": "state768-questions16-choices2",
+      "choices_per_question": 2,
+      "methods": {
+        "base_label": {
+          "case": "state768-questions16-choices2",
+          "choice_probabilities_per_second": 2.4763292713073617,
+          "choices_per_question": 2,
+          "input_tokens_processed": 13879,
+          "median_seconds": 12.92235260099551,
+          "p95_method": "nearest rank; exploratory tail estimate from a small repeated-request sample",
+          "p95_seconds": 12.945756162007456,
+          "questions": 16,
+          "questions_per_second": 1.2381646356536808,
+          "requests_per_second": 0.07738528972835505,
+          "sample_count": 10,
+          "samples_seconds": [
+            12.901038632990094,
+            12.929262275996734,
+            12.943419565999648,
+            12.92214271199191,
+            12.916580523000448,
+            12.945756162007456,
+            12.906606076998287,
+            12.90773562299728,
+            12.922562489999109,
+            12.93146967299981
+          ],
+          "state_tokens": 768
+        },
+        "base_verifier": {
+          "case": "state768-questions16-choices2",
+          "choice_probabilities_per_second": 1.2592225378494637,
+          "choices_per_question": 2,
+          "input_tokens_processed": 27246,
+          "median_seconds": 25.41250576300081,
+          "p95_method": "nearest rank; exploratory tail estimate from a small repeated-request sample",
+          "p95_seconds": 25.453125423999154,
+          "questions": 16,
+          "questions_per_second": 0.6296112689247318,
+          "requests_per_second": 0.03935070430779574,
+          "sample_count": 10,
+          "samples_seconds": [
+            25.412543386002653,
+            25.395507558991085,
+            25.405807179995463,
+            25.453125423999154,
+            25.41049480700167,
+            25.41424944199389,
+            25.412468139998964,
+            25.397341004994814,
+            25.417798239999684,
+            25.412825004998012
+          ],
+          "state_tokens": 768
+        },
+        "trained": {
+          "case": "state768-questions16-choices2",
+          "choice_probabilities_per_second": 1.0900980230596469,
+          "choices_per_question": 2,
+          "input_tokens_processed": 27246,
+          "median_seconds": 29.355158272999688,
+          "p95_method": "nearest rank; exploratory tail estimate from a small repeated-request sample",
+          "p95_seconds": 29.368854907006607,
+          "questions": 16,
+          "questions_per_second": 0.5450490115298234,
+          "requests_per_second": 0.034065563220613965,
+          "sample_count": 10,
+          "samples_seconds": [
+            29.348074817011366,
+            29.348559028003365,
+            29.3484148280113,
+            29.355060412999592,
+            29.357792241993593,
+            29.356622221006546,
+            29.368854907006607,
+            29.34994467800425,
+            29.355256132999784,
+            29.35939073599002
+          ],
+          "state_tokens": 768
+        }
+      },
+      "questions": 16,
+      "state_tokens": 768
+    }
+  ],
+  "warm_start_lineage": {
+    "all_parent_weights_exact": true,
+    "parent_checkpoint_sha256": "5f57ec38796d132edfa23638fbce66131fd4e7dfe87ceeadaba2b0e0d5c78024",
+    "parent_step": 1500,
+    "proof_sha256": "7f95fe9669ea57936dd98c4fdbd82f3bf472c321571fe4ec6079043ee4a1ce6d",
+    "trainable_tensors": 506
+  }
+}

source/AGENTS.md

@@ -8,8 +8,9 @@ Read the relevant operational page before changing a machine.
 GX10 currently has no ConnectX connection. On 2026-09-16 the user assigned all
 three machines to this task and explicitly authorized stopping their workloads.
 The Spark serving pair has been stopped and its restart commands recorded in
-FLEET_RUN.md. Treat the hosts as separate memory pools. Use `free -b` and
-GPU process inspection before loading a model; reclaim GX10's inherited SSH OOM
+[docs/operations/fleet.md](docs/operations/fleet.md). Treat the hosts as separate
+memory pools. Use `free -b` and GPU process inspection before loading a model;
+reclaim GX10's inherited SSH OOM
 exemption for training jobs. Keep this smoke's 16 GiB allocation cap.
 
 Source and small results belong here. Model/checkpoint files belong under

source/HANDOVER.md

@@ -1,345 +1,28 @@
-# Continue the three-machine campaign
-
-**Latest continuation, 2026-09-17 07:29 UTC:** the user requested broader training
-data. [EXPANDED_DATA.md](EXPANDED_DATA.md) records the 80,765-example seven-family
-mix, exact protected-split preservation, completed eight-step pilot and new GX10
-campaign `20260917T072142Z-24h`. It warm-starts from Spark B's selected step 1,500
-with fresh Adam, then resumes the verified pilot. GX10's original run stopped
-cleanly at step 4,380, retaining best step 2,500. Both Spark 4B runs continue;
-the completed 2B remains available. All **five candidates** are registered.
-The expanded run passed exact 512-prediction replay, full optimizer/RNG restore
-and subsequent finite updates; it reached step 15 at 07:28:58 UTC. Its data,
-pilot weights and source are uploaded and verified in the existing private
-Hugging Face repository. The GUI on 7466 and final-publication watcher remain up.
-The earlier overnight assessment is in [NEXT_STEPS.md](NEXT_STEPS.md).
-
-The user assigned **GX10 and both Sparks** to this task, authorized stopping their
-workloads, and requested continued experimentation without permission prompts.
-SSH key authentication as `andy` works on **192.168.8.111** (spark-a / spark-d1b4)
-and **192.168.8.204** (spark-b / spark-3e2a). The former Qwen serving pair was
-stopped cleanly at 19:14 UTC; its files/cache and exact restoration commands are
-preserved in [FLEET_RUN.md](FLEET_RUN.md). All three hosts run independent trials.
-
-The original absolute final deadline remains **2026-09-17 18:16:10 UTC /
-19:16:10 BST**. Every training supervisor stops by **16:00 UTC / 17:00 BST**,
-leaving 2 h 16 min for selection, calibration, untouched evaluation and the local
-Jev-compatible API. Never reset that deadline on recovery. Training early stopping
-can finish sooner. Final evaluation and hosted Jev inference are still pending.
-
-Read [PLAN.md](PLAN.md), [RESULTS.md](RESULTS.md) and [FLEET_RUN.md](FLEET_RUN.md).
-The authoritative working source is `/home/andy/projects/opensysone` on GX10;
-there is no hosted Git remote. Do not overwrite it with an older Mac checkout.
-Operational documentation is copied to `/home/andy/ai/opensysone/gx10-reference`
-on each host. Read the relevant `docs/host.md`, `docs/training.md`, `docs/spark-a.md`,
-`docs/spark-b.md` and `docs/fleet.md` before changing machines.
-
-## Active runs and source
-
-All run IDs below are relative to `/home/andy/ai/opensysone/runs` **on that host**.
-The Spark trainers launched from clean source **`4a60423`**. The expanded GX10
-trainer uses clean source **`24b8ccf`** in the detached worktree
-`/home/andy/ai/opensysone/source/expanded-24b8ccf`; keep that worktree for its
-supervisor and recovery. The main checkout contains current documentation and
-backup/verification tools. Running trainers retain their execution revision
-and source hashes in their manifests. Inspect live state before
-using recorded PIDs. Exit statuses of active jobs remain pending.
-
-| Host | Trial | Campaign | Supervisor / trainer at launch |
-| --- | --- | --- | --- |
-| GX10 | Original 4B, stopped at 4,380; selected 2,500 | `20260916T193741Z-24h` | exited 0 / 0 |
-| GX10 | Expanded 4B, LR 0.00002, seed 433, resumed pilot step 8 | `20260917T072142Z-24h` | 1630617 / 1630638 |
-| spark-a | 4B, LR 0.00003, fresh optimizer then pilot resume | `20260916T194258Z-24h` | 327084 / 327116 |
-| spark-b | 2B completed at step 6,000; selected step 2,000 | `20260916T193803Z-24h` | exited 0 / 0 |
-| spark-b | 4B refinement, LR 0.00001, seed 432 | `20260917T023137Z-24h` | 483974 / 484001 |
-
-Fleet coordinator: **`20260916T194403396250Z-fleet` on GX10**, PID **1630841**,
-source **`24b8ccf`**, running in `waiting_for_selection` with OOM adjustment 0.
-It was stopped before the fifth candidate and its explicit dataset override were
-registered, then restarted. The old stop's exit 1 can remain in `exit_code` while
-the new coordinator runs; current process identity/state determines liveness.
-It selects the best durable candidate, then runs finalization and serves it on
-GX10. The individual campaigns are `train_only=true`;
-they cannot independently evaluate reserved data or publish competing deployments.
-
-Each campaign's `training/checkpoint.pt` holds resumable optimizer/RNG state;
-`training/best.pt` holds its validation-selected model. Saves occur every **250
-steps or 900 seconds**, independently of 512-decision validation every 500 steps.
-Patience is eight evaluations. A logged update can be newer than its checkpoint.
-Three epochs are an upper bound, not a promised completed data pass.
-
-Latest audit **2026-09-17 02:10–02:15 UTC**: GX10 step 2,570 / selected 2,500
-(93.55% accuracy, 0.188640 crossfit NLL); Spark A step 2,529 / selected 2,500
-(92.58%, 0.218012); Spark B 2B finished at 6,000 / selected 2,000 (89.84%,
-0.255294). All logged gradients/losses are finite; peak allocations are
-15.624 / 15.624 / 8.183 GiB. The 2B final correctness gate passed, worst 6.56e-7.
-Small evidence is in `results/20260917-fleet-progress/`; historical startup proofs
-remain in `results/20260916-fleet-setup/`. Reserved predictions remain untouched.
-At 02:38 UTC, GX10/A had logged steps 2,721/2,704, with selected checkpoints
-unchanged. The new Spark B campaign replayed all 512 pilot step-8 predictions
-exactly, preserved full Adam/RNG state, and resumed finite updates (step 13 in
-the fleet snapshot; startup proof covers 9–12). Its selected branch step 0 is
-still the frozen GX10 parent. Startup checks passed; final exits remain pending.
-
-## Evidence and selection
-
-The fixed selection policy is **`crossfit_temperature_nll_v1`**, four source-group-
-disjoint validation folds, seed 431. Each fold's temperature is fitted on the other
-three; macro-family NLL is scored only on held-out validation predictions. Final
-serving temperature is fitted afresh on reserved calibration after the winner is
-frozen. Raw NLL and accuracy remain separately reported. No reserved calibration,
-test or Social IQA predictions have selected a candidate.
-
-This is a documented validation-driven revision: 4B step 128 scores **89.0625%**
-accuracy / **0.318518** crossfit NLL, versus step 40's 87.5% / 0.359522. Raw NLL
-favored step 40 because step 128 was more overconfident. The accuracy difference
-alone is uncertain. Fresh step-178 validation subsequently reached **90.4297%**
-accuracy / **0.303825** crossfit NLL / 0.404198 raw NLL and became the durable
-best; its state and evidence passed the same fleet eligibility checks. See
-`results/20260916-fleet-setup/selection-diagnostic.json`;
-independent test/holdout results remain necessary.
-
-GX10's old `20260916T185910Z-24h` stopped with a complete step-128 checkpoint;
-its trainer exited **-9** during subsequent final checks after the supervisor's
-30-second grace. No optimizer progress was lost. The next campaign,
-`20260916T192239Z-24h`, restored all trainable weights, Adam and Python/torch/CUDA
-RNG exactly, then stopped gracefully at **step 178, training exit 0**. Its explicit
-`skipped_on_stop` final-check status is not a new correctness pass.
-The immutable `20260916T193721Z-selection-parent` keeps that step-178 checkpoint
-byte-for-byte and reselects the unchanged step-128 best weights under the new
-criterion. It preserves the old raw-NLL best separately. Migration proof is in
-`results/20260916-fleet-setup/selection_migration.json`. Do not restart old campaigns.
-
-Both Spark environments passed **21,368 file hashes and 55 exact distribution
-versions** against GX10. All 13 files in each pinned model were SHA-256 verified.
-Spark A reproduced all 512 original 4B pilot predictions exactly before eight
-finite updates; its pilot and fresh GPU/HTTP verification exited 0. Spark B passed
-fresh GPU/HTTP verification,
-reproduced all 512 original 2B predictions exactly, and resumed finite optimizer
-updates. Spark A's long campaign also reproduced all 512 step-8 predictions
-exactly, preserved all weights/Adam/RNG state, and resumed finite updates. Small proofs are in `results/20260916-fleet-setup/`. The revised source
-passes **37 CPU tests**, plus the updated trained-Adam reselection integration.
-These wiring and validation checks do not establish held-out generalization.
-
-The exact A step-2,500 / B step-2,000 ensemble-reference artifacts are preserved
-on GX10 in `20260917T022201Z-ensemble-reference`, outside fleet selection. The
-fixed mixed ensemble's small validation NLL advantage is uncertain; see
-[NEXT_STEPS.md](NEXT_STEPS.md). This diagnostic is outside the current individual-model selection protocol.
-Adoption would require an explicit protocol revision and verified implementation
-before any reserved-data evaluation.
-
-## Model, data and machine bounds
-
-Pinned Apache-2.0 models are under `/home/andy/ai/models/opensysone`:
-
-- `Qwen3-4B-Instruct-2507-cdbee75f`, revision
-  `cdbee75f17c01a7cc42f958dc650907174af0554`: FP32, rank 8 / alpha 16,
-  16.518M trainable parameters, 512-token training, exact two-pass gradients.
-- `Qwen3.5-2B-15852e8c`, revision
-  `15852e8c16360a2fea060d615a32b45270f8a8fc`: FP32 text decoder, rank 16 /
-  alpha 32, 16.821M trainable parameters, 768-token training.
-
-Use `/home/andy/ai/envs/opensysone/bin/python`. GX10's isolated environment reuses
-existing torch/Transformers read-only; the Sparks have verified isolated copies.
-Shared environments are unchanged. BF16 remains blocked by measured numerical
-invariance failures. Keep the **16 GiB CUDA allocation cap**, at least **24 GiB
-MemAvailable** before loading, GPU process inspection and `oom_score_adj=0`.
-GX10's small existing router remains; Spark serving jobs remain stopped.
-GX10 has no ConnectX; memory pools are separate. No network, swap, earlyoom,
-firewall or clock configuration was changed.
-
-Frozen data: `/home/andy/ai/opensysone/data/public-decisions-v1-20260916`.
-Source-group-disjoint SNLI, BoolQ, ARC and four-choice Banking77; Social IQA is
-an untrained task-family holdout. Pins/licences/hashes are in
-`results/public-decisions-v1-manifest.json`. The 4B retains 40,915 train / 512
-validation / 510 calibration / 2,042 test / 768 holdout; 2B retains 40,937 / 512 /
-512 / 2,047 / 768. Validation IDs are identical. Exact deduplication does not
-exclude semantic duplicates or pretraining contamination. No customer data.
-
-## Inspect, stop and recover
-
-One read-only command checks every registered candidate concurrently, including
-completed candidates, with exact process identity and no model loading:
-
-```bash
-python3 scripts/fleet_status.py
-python3 scripts/fleet_status.py --json
-```
-
-Use the exact active host/run from the table, or the fleet controls in
-[FLEET_RUN.md](FLEET_RUN.md). From the project directory on the relevant host:
-
-```bash
-~/ai/envs/opensysone/bin/python scripts/campaign_status.py \
-  --campaign /home/andy/ai/opensysone/runs/20260916T193741Z-24h
-tail -n 5 /home/andy/ai/opensysone/runs/20260916T193741Z-24h/training/training.jsonl
-```
-
-Add `--stop` for a command-verified TERM to the recorded supervisor, orphan child
-or API. Wait for exit and lock release before restarting. Training checkpoints
-at a safe boundary. Do not start a second model on an occupied host. Resume a
-stopped candidate into a fresh campaign on its host:
-
-```bash
-~/ai/envs/opensysone/bin/python scripts/launch_24h.py \
-  --pilot /absolute/old/campaign/training --train-only \
-  --training-deadline 2026-09-17T16:00:00Z \
-  --deadline 2026-09-17T18:16:10Z --inference-max-tokens 1024 \
-  --selection-metric crossfit_temperature_nll_v1
-```
-
-Preserve model/data/seed/rank/alpha/learning rates/batches/token limits/schedule/
-epochs/two-pass configuration. The launcher restores them from the checkpoint.
-Use only trusted project checkpoints. **If a candidate path changes, stop the
-waiting fleet coordinator, update that candidate in its own `plan.json`, and
-resume it.** Editing a plan while the coordinator is running does not reload it.
-After `selection.json` exists, the winner is frozen; recovery must not reselect
-after test access. Stopping the waiting coordinator does not stop the independently supervised
-independently bounded training jobs; stop each campaign explicitly when needed.
-
-## Finalization and Jev harness
-
-The coordinator reconstructs the selected model, fits a scalar temperature on
-reserved calibration, checkpoints `evaluation/model.pt`, then evaluates untouched
-test/holdout against the unchanged pretrained scorer with separately fitted base
-temperature and source-group uncertainty. It verifies direct inference and a real
-HTTP request before publishing `/home/andy/ai/opensysone/deploy/current.json`.
-Success requires fleet `exit_code=0`, complete `evaluation/metrics.json`, and
-`state.json` with `api_ready=true`. Training completion alone is insufficient.
-The resulting API is **http://127.0.0.1:18081/v1/systemone**, inference limit 1,024;
-its PID/command remain recorded after the coordinator exits.
-
-[JEV_HARNESS.md](JEV_HARNESS.md) documents local, hosted and comparison modes,
-optional bearer authentication and Mac SSH tunneling. **`TYPESAFE_API_KEY` is
-not configured**, so authenticated hosted Jev inference has not been tested.
-Local confidence is normalized entropy, not established correctness calibration.
-This produces a general-language decision scorer, not a new general-purpose chat
-model. Frozen-head/generation controls, new-model prefix caching and the broader
-latency matrix remain open.
-
-## Completed runs and history
-
-All paths below are under `/home/andy/ai/opensysone/runs/`.
-
-| Run | Execution source | Exit / result |
-| --- | --- | --- |
-| `20260916T182256Z-train` | `4b25eec` | 1, chat-template return-type setup error before optimizer training; preserved |
-| `20260916T182352Z-train` | `f1c9322` | 0, 2B public-data 40-step pilot |
-| `20260916T183240Z-train` | `980d881` | 0, exact 512-prediction restart and step 41 |
-| `20260916T183751Z-verify2b` | script SHA in manifest | 0, restored-optimizer longest-input gradients and real authenticated HTTP |
-| `20260916T183823Z-train` | `ccbbe6d` | 0, selected 4B 40-step pilot |
-| `20260916T185718Z-verify4b` | script SHA in manifest | 0, 4B reload, optimizer-memory, long-context and 255-choice HTTP checks |
-| `20260916T155124Z` | `34a993e` | 0, original 0.5B synthetic FP32 60-step smoke |
-| `20260916T155314Z` | `91019bc` | 0, exact 72-prediction restart and step 61 |
-| `20260916T154714Z` | `4d6cb0f` | 1, BF16 probability-invariance failure; checkpoint preserved |
-| `20260916T161253Z-precision` | staged hashes later `b9dd165` | 0, diagnostic completed; BF16 fails |
-| `20260916T161355Z-precision` | `409ade4` | 0, expanded diagnosis; all BF16 variants fail |
-
-Small raw results and checkpoint hashes are retained under `results/<run-id>`;
-weights stay under `~/ai`. Original 0.5B smoke and verified prefix caching are
-unchanged in `smoke.py`/`decision_model.py`. FP32 passed the original expanded
-precision gate at worst 0.00002138; BF16 remains blocked. Synthetic results prove
-wiring, not task generalization. New-model prefix caching, frozen-head/generation
-controls and the larger latency matrix remain open. Fleet connectivity details
-are in [FLEET_SCOUT.md](FLEET_SCOUT.md), including verified numeric SSH addresses. The current fleet allocation supersedes
-its earlier serving-occupancy snapshot.
-
-Hugging Face backup and final-publication controls: [HUGGINGFACE.md](HUGGINGFACE.md).
-
-
-## Hugging Face publication watcher
-
-Backup destination: [andyshu/opensysone](https://huggingface.co/andyshu/opensysone),
-private, existing license metadata retained. The initial read-only credential
-failed with HTTP 403; its sanitized report remains in
-`results/20260917-fleet-progress/hf-initial-artifacts-publication.json`.
-At 02:52 UTC the user-supplied replacement was verified as account `andyshu`,
-role `write`, and saved to the existing local Hugging Face login store. Token
-values are excluded from source, logs and backups. **The initial snapshot upload
-completed and was verified at 02:54 UTC**, exit 0, including source and all four
-checkpoint pairs. See `hf-write-auth-verified.json` and
-`hf-snapshot-publication.json`. The first verified HF pointer commit is
-`b213728f9acc5e009bc96704db341913d582be4c`; remote `CURRENT_SNAPSHOT.json` records
-the authoritative payload/source revisions, including later documentation
-refreshes. Local publication state is in
-`~/ai/opensysone/runs/20260917T025300Z-hf-snapshot-publish`; immutable backup
-staging remains under `~/ai/opensysone/exports`.
-
-An independent final-publication watcher runs on GX10: PID **1427060**, source
-**`35d6d8f`**, OOM adjustment 0, status `waiting_for_completion` at launch. Its
-status directory is `/home/andy/ai/opensysone/runs/20260917T023940Z-hf-final-watch`;
-the adjacent `.log` file records process output. Exit status remains pending.
-Inspect `state.json` and `exit_code`; match the exact `state.json.command` against
-`/proc/1427060/cmdline` before stopping only that watcher with SIGTERM. Restart
-with the command in [HUGGINGFACE.md](HUGGINGFACE.md) and a new output directory.
-The watcher publishes the frozen final model only after completed evaluation and
-verified deployment, then checks the remote payload before updating
-`FINAL_MODEL.json`. Its own deadline is 18:46:10 UTC; this does not extend training
-or the original model deadline. See the launch proof for the exact command/hash.
-
-The previous watcher (PID 1426447) was deliberately stopped, exit 1, and replaced
-with the process above to remove inherited `HF_TOKEN`/`HUGGING_FACE_HUB_TOKEN`
-overrides. It will read the updated write-capable stored login when final publication
-begins. Changing credentials does not require
-changing the training jobs, fleet plan, API or repository visibility.
-
-
-## Interactive model playground — 2026-09-17
-
-The user requested a GUI for text plus candidate answers and probabilities. It is
-running on GX10 at **http://127.0.0.1:7466**, PID **1469393**, backend source **`2d0ff79`**,
-OOM adjustment 0. From the Mac, run `ssh -N -L 7466:127.0.0.1:7466 gx10`, then
-open **http://localhost:7466**. See [PLAYGROUND.md](PLAYGROUND.md).
-
-Runtime: `/home/andy/ai/opensysone/runs/20260917T034059Z-playground-port7466`, also recorded
-in `LAST_PLAYGROUND`. `launch.json` records the exact process command/source
-hashes and `server.log` receives sanitized diagnostics. Exit status is pending
-while serving. Inspect `/api/status` and match `/proc/1469393/cmdline` against
-`launch.json.command` before sending SIGTERM to this process only. The documented
-CLI restarts it from the fixed catalog after the old listener has stopped.
-
-Three immutable snapshots are available: GX10 4B step 2,500, Spark A 4B step 2,500,
-and Spark B 2B step 2,000. Their files/hashes and matching provenance are under
-the original snapshot runtime, referenced by this runtime's `models.json`; these probabilities are explicitly uncalibrated. No
-reserved evaluation examples were used for GUI testing. One backend resides at
-a time; loads, scoring and unloading are serialized on a dedicated worker thread.
-The 16 GiB allocation cap and memory/OOM checks remain active. This extra GUI
-process shares GPU compute with training, so requests can slow optimizer steps.
-Port 18081 remains reserved for final deployment; no firewall/services changed.
-
-All seven backend tests passed. Chromium passed real inference for all three
-models, return switching, clipboard JSON, input-edit staleness, duplicate options,
-actual tokenizer overflow and mobile layout. Browser script/CSP errors: none.
-Cold/switch example requests were 5.08–9.10 seconds; a warm main-model request
-was 1.53 seconds. These are individual wiring timings, not latency percentiles
-or quality estimates. Real results/screenshots and concurrency observations are
-in `results/20260917-playground/`; the separate frontend fixture report is labeled
-as stubbed UI testing. Training and the fleet/final-publication controllers remain
-independent of this GUI.
-
-A 45-second observation after GUI verification recorded the GX10 trainer advancing
-from step 3,063 to 3,068 with finite losses/gradients, a 15.624 GiB allocation peak
-and 81.3 GiB host memory available. The GUI stayed ready. This confirms continued
-training during GUI operation; it does not establish zero slowdown or capture all
-model-switch transients.
-
-At the user's request, the playground moved from port 18082 to **7466**. The
-previous process received verified SIGTERM and exited; its wait status could not
-be collected by the replacement launcher. `stop.json` in the old runtime records
-that observation. The new process starts without a resident model and loads one
-on the next scoring request. The page, scripts, styles, model catalog and status
-respond on 7466; the old listener is closed. Evidence: `results/20260917-playground/port-7466.json`.
-
-The frontend now fits the viewport, with Context/Choices/Results tabs on compact
-screens and internally scrolling text/results. A fixed action bar and result-copy
-footer stay accessible. Very short portrait layouts compact optional content to
-retain readable inputs when a keyboard reduces the viewport. Browser fixture
-checks pass 13 sizes, including 320×568, 844×390 and 390×360; they check visible
-controls, readable input lines, loading, validation, keyboard tabs, resizing,
-long result lists, stale results, clipboard and recovery. Fixture probabilities
-are not new model evidence. See `results/20260917-playground-layout/`.
-
-The backend process and model snapshots continue unchanged. Static files are
-served directly from `web/` with no-store caching, so refresh the browser to use
-the layout. `frontend-current.json` in the active runtime records the current
-frontend commit and served-file hashes independently of the backend launch
-revision. Training source and processes were not modified for this relayout.
+# OpenSysOne handover
+
+**Recorded state, 17 September 2026, 09:22 UTC:** training, final validation,
+evaluation and profiling completed with exit 0. Training remains stopped; both
+Spark GPUs were idle at completion. Preserve all selected and resumable artifacts.
+
+The selected Qwen3-4B decision scorer retains Spark B step-1,500 weights, unchanged
+at expanded branch step 0. It achieved **92.90%** accuracy on the original test
+and **72.92%** on the Social IQA family holdout. The measured implementation is
+slower than both pretrained inference baselines. See [RESULTS.md](RESULTS.md).
+
+The recorded remaining services are the calibrated loopback API on **18081** and
+the browser playground on **7466**. Process IDs, exact source revisions, checkpoint
+hashes, run paths and inspect/stop/restart commands are preserved in the
+[full operational handover](docs/operations/handover.md). Check those records and
+the live process identity before acting; this document does not restart any job.
+
+For a continuation, read [PLAN.md](PLAN.md) and [RESULTS.md](RESULTS.md), then the
+[full handover](docs/operations/handover.md) and relevant infrastructure page under
+`/home/andy/ai/opensysone/gx10-reference`. Keep the 16 GiB per-process allocation
+cap and existing model/data/source provenance checks. Historical launch plans in
+the archived documents are superseded by the completed state above.
+
+- [Documentation index](docs/README.md)
+- [Playground usage and controls](docs/usage/playground.md)
+- [Fleet history and serving-pair restoration](docs/operations/fleet.md)
+- [Hugging Face publication records and controls](docs/operations/huggingface.md)
+- [Complete report, tables and charts](results/report.md)

source/HF_MODEL_CARD.md

@@ -13,123 +13,72 @@ tags:
 
 # OpenSysOne
 
-OpenSysOne is an experimental natural-language decision scorer. It scores an
-arbitrary state, question and candidate answer, then normalizes mutually exclusive
-choices into probabilities. It does not generate chat responses.
-
-This repository backs up the ongoing three-machine training experiment. It
-contains custom adapter/head checkpoints, reproducible source, run provenance
-and small result files. The full pretrained base weights are referenced by pinned
-revision and are not duplicated here. **These are custom OpenSysOne artifacts,
-not a drop-in Transformers or PEFT adapter repository.**
-
-## Current status
-
-The snapshot is **training-stage, uncalibrated**. Final held-out evaluation and
-calibrated deployment have not completed. The original campaign ends on
-**17 September 2026 at 18:16:10 UTC**, with training stopping by 16:00 UTC.
-`CURRENT_SNAPSHOT.json` identifies the backup and its source revision.
-`FINAL_MODEL.json` will identify the separately verified calibrated release when
-finalization and upload succeed; its absence means no final release is recorded.
-
-Validation audit on 17 September, 07:00 UTC:
-
-| Selected candidate | Step | Validation accuracy | Crossfit selection NLL |
-| --- | ---: | ---: | ---: |
-| Qwen3 4B, main run | 2,500 | 93.55% | 0.188640 |
-| Qwen3 4B, lower learning rate | 4,000 | 93.75% | 0.190020 |
-| Qwen3.5 2B | 2,000 | 89.84% | 0.255294 |
-| Qwen3 4B, refinement | 1,500 | 94.73% | 0.170150 |
-
-All use the same 512 validation decisions. Selection uses four source-group-
-disjoint temperature-crossfit folds with fixed seed 431; smaller macro-family NLL
-is better. This is **validation-selection evidence**, not a held-out quality claim.
-The 2B run stopped cleanly at step 6,000 after eight evaluations without a new
-best. At 07:07 UTC the original GX10 4B run stopped cleanly at step 4,380,
-preserving its selected step 2,500 and full resumable state. GX10 now verifies an
-expanded-data candidate from the refinement's frozen selected step 1,500, while
-both Spark 4B runs continue. See `source/EXPANDED_DATA.md` for its latest run
-state, verification evidence and the broader training mix. Independent trials
-retain the original absolute deadlines.
-
-## Contents and reconstruction
-
-The [browser playground](source/PLAYGROUND.md) provides a context box, question,
-options list and probability bars, with a model selector. Its lightweight local
-server uses these custom checkpoints and the pinned base weights. It runs on
-GX10 over a loopback connection/SSH tunnel; this repository is a backup, not a
-hosted inference application.
-
-- `source/`: a committed source/documentation snapshot, including the training
-  scripts, tests, Jev-compatible harness, findings and operational handover.
-- `snapshots/<id>/backup-manifest.json`: artifact roles, originating hosts/paths,
-  checkpoint steps, model/data/source provenance and SHA-256 checksums.
-- `snapshots/<id>/artifacts/<candidate>/best.pt`: selected adapter/head weights
-  and reconstruction metadata. These initial backups have no fitted temperature.
-- `snapshots/<id>/artifacts/<candidate>/checkpoint.pt`: resumable training state,
-  including Adam and random states. Its step may be later than the selected best.
-- Expanded-data snapshots include transformed decision files and their source
-  notices, split hashes, diagnostic exclusions and original dataset lineage.
-- `final/<campaign>/`: final calibrated model and evaluation evidence, created only
-  after successful finalization and publication.
-
-Keep `checkpoint.pt`, its sibling `best.pt` and matching validation evidence
-together when resuming. Checkpoint metadata records the original local paths;
-reconstruction needs the pinned base/data and matching project source. The current
-harness works on the recorded GX10/Spark layout. This backup does not establish
-portability to a new operating system or a generic hosted inference service.
-Model and optimizer files use PyTorch serialization; use the project's known,
-hash-verified artifacts and its supplied reconstruction code.
-
-The main 4B model uses FP32, rank-8 additive adapters (alpha 16) and a learned
-scalar head initialized from pretrained yes-minus-no logits: 16,517,633 trainable
-parameters. The 2B text decoder uses rank 16 / alpha 32, with 16,821,249 trainable
-parameters. Training limits are 512 and 768 complete-chat tokens respectively;
-1,024-token inference was separately verified. Training retains a 16 GiB per-job
-CUDA allocation cap. BF16 did not pass the project's numerical invariance gate.
-
-Pinned bases, recorded as Apache-2.0 in their provenance:
-
-- `Qwen/Qwen3-4B-Instruct-2507` at
-  `cdbee75f17c01a7cc42f958dc650907174af0554`.
-- `Qwen/Qwen3.5-2B` at
-  `15852e8c16360a2fea060d615a32b45270f8a8fc`.
-
-## Data, evaluation and limitations
-
-The original public training sources are SNLI, BoolQ, ARC and four-choice Banking77 routing.
-Social IQA is a wholly untrained task-family holdout. Frozen source pins, licences,
-raw/split hashes and group/deduplication audits are included in
-`source/results/public-decisions-v1-manifest.json`. That manifest credits
-Stanford NLP (SNLI), Google (BoolQ), AllenAI (ARC/Social IQA) and PolyAI (Banking77),
-and records their original dataset licences. The original repository's
-`license: unknown` metadata is retained; no new licence for the project artifacts
-is assigned by this backup.
-
-The expanded candidate adds official training rows from HellaSwag (MIT), PIQA
-(AFL-3.0 according to its creator's pinned README) and CommonsenseQA (MIT).
-After the 512-token filter there are 80,765 training decisions, including all
-40,915 original retained examples. All four original reserved split files and
-their tokenized rows are unchanged. Another 383 retained new-source diagnostic
-decisions are excluded from both training and checkpoint selection. Source pins,
-credits, licences and transformations are in
-`source/results/20260917-expanded-data/dataset-manifest.json`. The unchanged
-selection set measures the original tasks; expansion alone is not evidence of
-better performance on the three added tasks.
-
-Validation selects checkpoints. Separate calibration fits one global temperature
-only after selection is frozen. Final test/holdout comparisons use the unchanged
-pretrained scorer, with a separately fitted base temperature and source-group
-uncertainty. Frozen grouping and exact deduplication do not rule out pretraining
-contamination or semantic duplicates. Four-choice Banking77 is not the full
-77-label benchmark. No claim of general intelligence, Jev equivalence or
-calibration on unseen task families follows from the current validation scores.
-
-The harness implements local scoring, a loopback API, hosted Jev requests and
-response/timing comparisons. The local model identifies itself as OpenSysOne;
-compatibility with the request shape does not make it Jev. Local confidence is
-normalized entropy, not calibrated probability of correctness. Authenticated
-hosted Jev calls require `TYPESAFE_API_KEY` and have not been tested. Credentials
-and pretrained base weights are not part of this backup. Expanded-data snapshots
-may include the transformed training data, with upstream notices retained;
-raw upstream archives are referenced by pinned revision and checksum.
+**Inspired by [Jev](https://typesafe.ai/), TypeSafe.ai's System One model.**
+Credit goes to the TypeSafe team for inspiring this project's exploration of
+structured decisions with probabilities. OpenSysOne is an independent experimental implementation; API
+compatibility does not establish Jev equivalence.
+
+The **completed 4B release** scores a state, question and explicit candidate
+answers, returning probabilities over those choices. Training, separate
+calibration, final evaluation and local API verification completed on
+17 September 2026.
+
+Start with the [model and reconstruction notes](model/README.md),
+[results report](results/report.md), or [publication guide](docs/README.md).
+The calibrated artifact is [model/model.pt](model/model.pt).
+It contains custom OpenSysOne adapter/head weights and metadata. The pinned
+Qwen3-4B-Instruct-2507 base is required separately; this is not a standalone
+Transformers model or a standard PEFT adapter package.
+
+## Measured results
+
+The full comparison uses the unchanged pretrained yes/no verifier, with a separate
+temperature fitted for each model. Intervals are paired 95% source-group bootstrap
+intervals for selected minus base accuracy.
+
+| Evaluation | Decisions | Selected | Base verifier | Accuracy gain (95% interval) |
+| --- | ---: | ---: | ---: | ---: |
+| Known-family test | 2,042 | 92.90% | 84.48% | +8.42 pp [6.85, 9.89] |
+| Social IQA family holdout | 768 | 72.92% | 70.31% | +2.60 pp [0.13, 5.34] |
+
+On a separate matched 320-decision profile, selected accuracy was 89.06%, versus
+80.94% for the base verifier and 86.25% for a base model using one constrained
+answer-label token. The selected scorer was **slower on all 12 profiled workloads**:
+1.11–1.17× the verifier latency and 2.18–15.58× the label baseline latency.
+These are warm, serial FP32 measurements on one GB10, not concurrent-serving
+throughput or comparisons with generated reasoning. See [tables and charts](results/).
+
+## Model and limits
+
+The release uses rank-8 additive adapters and a scalar head: 16,517,633 trainable
+parameters. The selected expanded branch's step 0 retains the refinement parent's
+step-1,500 weights. The expanded branch's later step 159 was not selected; its
+post-selection diagnostic gains are reported separately.
+
+One temperature, 1.745822, was fitted on 510 separate known-family calibration
+examples. Social IQA calibration remains limited: its top-label ECE is 8.30%.
+No general intelligence, Jev-level quality or universal calibration claim follows.
+Four-choice Banking77 is not the full 77-label task; benchmark grouping does not
+rule out base-model pretraining overlap. The 1,024-token inference limit includes
+the complete formatted candidate prompt, and longer inputs are rejected.
+
+## Files and provenance
+
+- [model/](model/README.md): calibrated artifact, hash and pinned base requirements.
+- [docs/](docs/README.md): layout and [reproduction guide](docs/reproduce.md).
+- [source/](source/): complete committed project source, tests and usage guides.
+- [results/](results/): final metrics, profiling report, tables and charts.
+- [archive/](archive/README.md): index to preserved experiment history.
+
+The original pointers remain authoritative:
+[FINAL_MODEL.json](FINAL_MODEL.json) identifies the calibrated release,
+[PROFILE_RESULTS.json](PROFILE_RESULTS.json) identifies verified profiling and
+wrap-up evidence, and [CURRENT_SNAPSHOT.json](CURRENT_SNAPSHOT.json) identifies
+the earlier training backup. Historical payload paths and hashes are preserved.
+
+The existing `license: unknown` metadata is unchanged. Base-model and dataset
+licenses remain separate; see the source's
+[original data provenance](source/results/public-decisions-v1-manifest.json) and
+[expanded data provenance](source/results/20260917-expanded-data/dataset-manifest.json).
+Base weights and credentials are excluded. Hosted Jev calls require separate
+authentication and were not exercised in this evaluation.

source/PLAN.md

@@ -1,272 +1,19 @@
-# OpenSysOne: single-node start and GX10 handover
+# OpenSysOne plan
 
-## Training-data expansion — 2026-09-17 07:22 UTC
+The training and evaluation campaign is complete. All training, final validation,
+profiling and full evaluation jobs exited 0. Preserve the selected model and final
+resumable states; do not resume optimizer updates as part of this completed work.
 
-The user requested expansion using best judgment. Add pinned official TRAIN
-rows from HellaSwag, PIQA and CommonsenseQA while retaining every original
-training example and preserving all four reserved splits byte-for-byte. The
-filtered mix has 80,765 decisions, approximately half replay and half additions.
-Keep 383 additional new-source diagnostics outside training and the fixed
-selection protocol. See [EXPANDED_DATA.md](EXPANDED_DATA.md) for provenance,
-verification, current controls and the limits of the unchanged selection set.
+The [final report](results/report.md) records improved
+accuracy over the pretrained verifier and slower inference in the measured FP32
+implementation. Current service controls and artifact provenance are in
+[HANDOVER.md](HANDOVER.md); measured outcomes are in [RESULTS.md](RESULTS.md).
 
-Replace the plateaued GX10 run, preserving its resumable step 4,380 and selected
-step 2,500. Initialize a new 4B candidate from frozen Spark B step 1,500 with fresh
-Adam, seed 433 and LR 2e-5. The eight-step pilot passed; campaign
-`20260917T072142Z-24h` resumes it from clean frozen source `24b8ccf`. Both Spark
-4B runs continue. The fifth fleet candidate explicitly registers v2 data;
-protected bytes and identical validation identities remain eligibility gates.
-Keep the original 16 GiB cap and absolute 16:00 / 18:16:10 UTC deadlines.
+The [future experiment recommendations](docs/research/next-steps.md) prioritize
+verified context-prefix reuse, separately measured adapter merging, a declared
+validation plan for broader training data, and aggregate serving throughput with
+independent Spark replicas. These are proposals for later work, not running jobs.
 
-## Continuation decision — 2026-09-17
-
-See [NEXT_STEPS.md](NEXT_STEPS.md) for the overnight findings and the assessment
-of work shared by the two Sparks. GX10 and Spark A continue improving 4B trials.
-Spark B's 2B run exited 0 at step 6,000 after validation early stopping; preserve
-its step-2,000 selected artifact. Use the freed GPU for a fourth candidate from
-GX10's selected step-2,500 4B weights, with fresh Adam, seed 432 and LR 1e-5.
-Keep the fixed selection policy, 16 GiB cap and original absolute deadlines.
-
-The two Sparks have active ConnectX/RoCE and installed NCCL, but distributed
-training has no measured correctness or throughput result. Do not interrupt the
-improving trials to replace their trainer during this delivery window. Next joint
-experiment: bounded communication and synchronized-gradient parity, followed by
-25–100 representative updates at equal global batch and measured memory. Parallel
-scoring replicas are a simpler later use; preserve the tested GX10 finalizer now.
-
-## Active 24-hour campaign — 2026-09-16
-
-The user now authorizes all three machines for this task, including stopping
-existing workloads. GX10 continues the main run; the Sparks run independent
-lower-learning-rate 4B and longer-running 2B candidates. See
-[FLEET_RUN.md](FLEET_RUN.md) for the active fleet plan and process controls.
-The existing 16 GiB allocation cap applies to each training process. Select the
-candidate using the same 512 validation decisions before final calibration/test.
-The frozen selection criterion is now four-fold source-group-disjoint temperature
-crossfit macro-family NLL, seed 431, policy `crossfit_temperature_nll_v1`. Fit each
-fold's scalar temperature on the other three; fit serving temperature afresh on
-reserved calibration after selection. Raw NLL/accuracy remain separately reported.
-This validation-driven revision preserves the stronger step-128 classifier that
-raw NLL discarded because of overconfidence; see the diagnostic in RESULTS.md.
-The absolute delivery deadline is **2026-09-17 18:16:10 UTC (19:16:10 BST)**,
-24 hours from this request. Reserve at least the final two hours for fresh reconstruction,
-calibration, untouched evaluation and loopback API deployment. Earlier sections
-below preserve the smoke plan; their 1.5B and leave-idle boundary is superseded.
-The runner increases that reserve from measured pilot validation time when needed,
-including both pretrained/tuned passes, 30% margin and ten minutes for setup.
-
-Start from a pinned posttrained model, retain its pretrained yes-minus-no
-readout, and train ordinary FP32 low-rank decoder adapters plus scalar head.
-BF16 remains blocked by the measured correctness gate. Compare Qwen3.5-2B
-with Qwen3-4B-Instruct-2507, using the fixed validation crossfit criterion, memory and
-throughput. The 4B pilot uses rank 8 and an exact two-pass categorical gradient
-to keep one candidate graph live. The reliable 2B fallback uses rank 16.
-Keep the 16 GiB CUDA cap and launch free-memory/OOM hardening unchanged.
-**Initial single-node selection:** the 4B rank-8 candidate, with 87.50% validation
-accuracy and
-0.395661 macro NLL versus the 2B pilot's 82.81% / 0.498153. It beats 2B in
-every measured validation family. Training limit is 512 complete-chat tokens;
-separately verified inference limit is 1,024. Longest-input training stress peaks
-at 15.624 GiB, and real HTTP reload/limits/255-choice checks pass.
-
-Frozen source-group-disjoint public data covers SNLI, BoolQ, ARC and four-choice
-Banking77 routing. Social IQA is a completely untrained task-family holdout.
-Source pins, licences, raw hashes and split audit are in
-`results/public-decisions-v1-manifest.json`; data is under `~/ai/opensysone/data/`.
-Model-specific length filtering is reported, with no silent truncation.
-Validation chooses checkpoints. Calibration fits only one global temperature;
-untouched test/holdout evaluation happens in the separate finalization process.
-Compare the trained scorer with its unchanged pretrained readout, both raw and
-separately temperature-calibrated, with source-group bootstrap uncertainty.
-
-Before launch, prove reconstruction, a subsequent optimizer step, longest-input
-gradients with restored optimizer state and authenticated HTTP inference using
-the real checkpoint. Then detach `scripts/launch_24h.py`, preserving optimizer,
-RNG, source/data/model provenance, checkpoint cadence independent of evaluation,
-individual child PIDs and an absolute deadline. Select the best validation
-checkpoint rather than assuming more updates improve intelligence.
-
-The standard-library harness supports local inference, Jev HTTP calls and
-response/timing comparison; see [JEV_HARNESS.md](JEV_HARNESS.md). Hosted calls
-require `TYPESAFE_API_KEY`; no key is available in the current process environment.
-Deploy only on loopback and use the existing SSH tunnel for Mac access.
-This trains a general-language **decision scorer**, not a new general-purpose
-chat model or a demonstrated substitute for Jev. Generalization and calibration
-remain evaluation outcomes. Generation baselines, frozen-head controls, shared
-prefix caching for these new models and the original latency matrix remain open.
-
-Consolidated **2026-09-16**. This is the active execution plan. The original
-proposal is preserved verbatim in [RESEARCH_BRIEF.md](RESEARCH_BRIEF.md).
-Start a continuation with [HANDOVER.md](HANDOVER.md), then read this file.
-
-Build a decision scorer from a pretrained causal Transformer: arbitrary state,
-question and natural-language candidate go in; one scalar score comes out.
-Normalize mutually exclusive choices to a distribution. No generated answer or
-fixed label vocabulary. TypeSafe/Jev architecture claims remain hypotheses;
-softmax alone does not establish calibration.
-
-## Resources available now
-
-| Host | Installed unified RAM | Available at 16:36 BST | Current use | Project role |
-| --- | ---: | ---: | --- | --- |
-| GX10 | 121.6 GiB | 118.7 GiB | Idle router; no substantial loaded model | Development, tiny training/evaluation |
-| spark-a | 121.7 GiB | 28.0 GiB | Qwen3.8-Flash-Next Q8_0 head | Existing serving workload |
-| spark-b | 121.7 GiB | 22.4 GiB | Same model's RPC worker | Existing serving workload |
-
-These are snapshots, not reservations. CPU, GPU, cache and OS share each pool.
-The table above records the earlier smoke snapshot. The user subsequently
-assigned all three GB10s to OpenSysOne; at 19:14 UTC the Spark serving pair was
-stopped and both GPUs were empty, with about 118 GiB available on each host.
-These remain three separate memory pools. Recheck `free -b` and GPU processes
-before every run.
-
-The Sparks have one physical ConnectX port-0 cable, with two PCIe-domain paths:
-`192.168.100.10/11` and `192.168.101.10/11`. Both were verified active; earlier
-fleet tests measured 108.9 Gb/s RDMA per domain, 188 Gb/s aggregate. llama.cpp
-RPC works; **PyTorch/NCCL training is unverified**. See [FLEET_SCOUT.md](FLEET_SCOUT.md).
-
-GX10 uses ordinary Ethernet/Wi-Fi/tailnet, without connected ConnectX.
-Additional connectivity is expected around **2026-09-18**, per the user; this is
-an estimate. GX10 can coordinate jobs over SSH today, but should not join the
-Sparks' collective over a slow network. Even after cabling, verify topology,
-transport and collective correctness before revising capacity. A two-node DAC
-does not specify the future three-node topology or create coherent pooled RAM.
-
-## Immediate experiment and handover boundary
-
-Finish a bounded smoke on GX10 and leave it free for the next session.
-
-- Base: `Qwen/Qwen2.5-0.5B`, revision
-  `060db6499f32faf8b98477b0a26969ef7d8b9987`, Apache-2.0, dense causal decoder.
-- Method: FP32 backbone, final two layers trainable, FP32 scalar head,
-  categorical cross-entropy. This is partial fine-tuning, not LoRA.
-- Data: invented inventory facts, three questions per state, shuffled candidate
-  text; 192 train / 48 calibration / 72 test decisions. Disjoint entity groups,
-  same task templates. No customer data.
-- Bounds: 60 steps, four decisions/batch, short sequences, 16 GiB CUDA cap,
-  24 GiB available-memory launch gate, 25-minute timeout, checkpoints every ten
-  steps and before evaluation. No long unattended run needed for this phase.
-- Stack: existing `~/ai/envs/comfy/bin/python`, torch 2.11.0+cu130,
-  Transformers 5.15.0, SDPA; no shared-environment package changes.
-- Compare base yes-minus-no token logits, initial uniform scalar head, trained
-  scalar and separate-calibration-split global temperature. Uniform output is
-  an optimization sanity baseline, not a competitive classifier.
-- Time the same trained checkpoint and token IDs: full batched forwards versus
-  cached branching; about 128/1,024 state tokens, 1/4/16 questions, two choices,
-  eight branches/chunk. Save actual lengths and raw warm repetitions, prefill,
-  branch and end-to-end times. This is not yet the complete generation comparison.
-
-Completion gates: finite gradients/loss, changed backbone/head weights, checkpoint
-and optimizer/RNG state, reload/resume verification, strict FP32 tiny-model cache
-tests and measured BF16 parity/permutation/isolation. Record peak allocated and
-reserved CUDA memory plus host availability. Save before evaluation can fail.
-Leave source, model, artifacts, commands, hashes and process state on GX10.
-
-Synthetic improvements demonstrate optimization and wiring only. They cannot
-establish calibration, zero-shot ability, useful judgment or superiority over
-prompt-and-generate classification.
-
-**Precision gate found during the smoke:** BF16 changes probabilities by up to
-0.099 when batch composition changes, including uncached forwards. Forcing
-SDPA MATH does not fix it. Casting the same weights to FP32 reduces discrepancies
-to about 0.000014 across the tested comparisons. Use FP32 for the reference;
-BF16 requires an explicit correctness investigation before larger experiments.
-Preserve the failed run and diagnostic; do not relax tolerances to accept it.
-
-**Expanded gate, 2026-09-16:** all 24 synthetic groups fail BF16 even with
-strict accumulation, math SDPA, FP32 decoder linears, or their combination.
-Worst probability differences are 0.147–0.239; the same weights cast to FP32
-stay below 0.000022. First-layer traces expose shape-dependent projection
-differences, but correcting those alone does not fix the decoder. See
-`results/20260916T161355Z-precision/precision.json`. Use FP32 for the next public
-data/1.5B experiment; further BF16 work should target remaining operations rather
-than repeat these unsuccessful switches.
-
-## Next working session: first useful 1.5B experiment
-
-Budget the next one or two hours for a real data cut and a proven resumed run.
-
-1. Read smoke results and traces. Fix correctness before interpreting speed.
-   Preserve the 0.5B run as a reference and verify fresh checkpoint reconstruction.
-2. Pin `Qwen2.5-1.5B` base. Start at 128–1,024 state tokens; increase to 4k after
-   measuring memory. BF16 weights are about 2.9 GiB; training processes every
-   candidate branch. Record exact config rather than assuming context limits.
-3. Select public sentiment, entailment and intent/routing sources, checking each
-   licence/version first. Preserve source splits, deduplicate/group before
-   transformations, and reserve an entire further task family plus unseen
-   question/label paraphrases for zero-shot evaluation. Freeze test data early.
-4. Compare token scoring, frozen-backbone trained head and tuned scalar on the
-   same data. Start with FP32/SDPA; restore BF16 only after the precision gate.
-   Add LoRA in an isolated pinned PEFT
-   environment if useful; preserve the shared Comfy environment. Defer QLoRA,
-   FP8 and custom kernels.
-5. Count all processed branch tokens/padding and measure elapsed step time. Set
-   dataset size and deadline from those observations. Keep evaluation batches
-   small and checkpoint on a cadence independent of evaluation.
-
-## Following 48 hours: prove utility on one node
-
-Start with at least 1,000 untouched test decisions across multiple public
-datasets; increase until proper-score uncertainty is informative. Report
-per-family counts, accuracy, NLL, multiclass Brier (class sum), declared-bin
-top-label ECE, reliability and accuracy-versus-coverage. Fit one temperature
-on a separate calibration set. Evaluate once on test and held-out family.
-Bootstrap source groups, not augmented rows; use ECE alongside proper scores.
-
-Add generation and constrained-output baselines using the same base and inputs.
-Document prompt, output-token budget, parse/failure policy and timing scope.
-Distinguish model-load, first-call, warmed and application end-to-end latency.
-
-Extend one axis at a time: 1/4/16/64 questions; 2/4/16 choices; 128/1k/4k states.
-Add 255 choices as one stress point after bounded chunking is proven. Do not
-run the original full Cartesian product yet. Estimate tail latency with enough
-repetitions before reporting p95. Account for KV copies, padding and transfers.
-Recheck permutations, mixed lengths and unrelated-question perturbations.
-
-**Scale only after:** repeatable useful accuracy and improved NLL/Brier on at
-least one untouched task family, no unexplained severe regression elsewhere,
-and meaningful measured multi-question latency/throughput improvement over a
-fair baseline. If only familiar label words improve, fix data/objective first.
-The original <150/<250/<500 ms targets are exploratory, not commitments.
-
-## Later hardware and architecture decisions
-
-Schedule a service transition before large Spark training; verify actual memory
-release. spark-a's active swap and missing earlyoom must be addressed before
-sustained training. Operational changes belong in the relevant GX10 docs.
-
-Before DDP: test CUDA/NCCL all-reduce numerical correctness, transport logs,
-both directions and realistic message sizes, then a short two-rank optimizer
-run with checkpoint/resume. Compare useful examples/second with one node and
-two independent runs. DDP replicates state; it does not combine memory.
-FSDP is a separate decision, justified by measured memory needs.
-
-The 3B class remains the target after the 1.5B gate. Qwen2.5-3B has a separate
-research licence; select it deliberately or choose another base if deployment
-requires different terms. A 7B/8B run follows useful scaling evidence. Keep
-inference local. Primary model/cache links are in [RESEARCH_NOTES.md](RESEARCH_NOTES.md).
-
-Stay with architecture A (shared-prefix decoder) until profiling identifies its
-cost. Every suffix still runs all layers and attends to the state. Batching
-does not guarantee constant latency. A 1.5B 4k prefix is about 112 MiB KV;
-256 physical copies are about 28 GiB before suffixes, weights and workspace.
-
-Test architecture B (state encoder plus shallow cross-attention decoder) if
-branch work/copies dominate and quality passes. Compare at equal data budget.
-Packed branching needs numerical independence tests; custom kernels need a
-profiled bottleneck. Soft teacher targets, proper-score losses and quantization
-calibration ablations follow a reliable baseline. RL is unnecessary initially.
-
-## Evidence and artifacts
-
-- [HANDOVER.md](HANDOVER.md): exact continuation commands and run state.
-- [RESULTS.md](RESULTS.md): measured outcomes and limitations.
-- [FLEET_SCOUT.md](FLEET_SCOUT.md): live survey and prior bandwidth evidence.
-- [RESEARCH_NOTES.md](RESEARCH_NOTES.md): primary sources and memory arithmetic.
-- `results/<run-id>/`: small raw config/data/prediction/correctness/timing files.
-- GX10 `~/ai/opensysone/runs/<run-id>/`: complete run including checkpoint.
-- GX10 `~/ai/models/opensysone/`: pinned pretrained weights.
-
-Record source commit/hashes, model/data revisions, config, exact software and
-hardware for every run. No API service is needed for this phase; any future
-HTTP listener follows the existing loopback/tailnet policy.
+The [full dated execution plan](docs/operations/plan.md) preserves the original
+research sequence, deadlines and superseded continuation decisions. See the
+[documentation index](docs/README.md) for usage, research and operational notes.

source/README.md

@@ -1,75 +1,81 @@
 # OpenSysOne
 
-Try the trained models in the [browser playground](PLAYGROUND.md): enter context,
-a question and possible answers, then compare their probabilities across models.
-
-Experimental decision scorer: pretrained Transformer, arbitrary natural-language
-candidates and scalar logits, with a Jev-compatible API harness.
-
-Read [HANDOVER.md](HANDOVER.md) to continue on GX10 and [PLAN.md](PLAN.md) for the
-current plan. [FLEET_RUN.md](FLEET_RUN.md) records the three-machine allocation,
-process controls and deadline. [RESULTS.md](RESULTS.md) records measured outcomes.
-The original proposal is preserved in [RESEARCH_BRIEF.md](RESEARCH_BRIEF.md).
-
-The active public-data experiment compares pinned posttrained Qwen3.5-2B and
-Qwen3-4B-Instruct-2507. Ordinary low-rank decoder adapters and a scalar head train
-in FP32; the head starts from pretrained yes-minus-no token logits. Frozen data
-covers entailment, passage questions, science and banking intent, with Social IQA
-reserved as an entirely unseen task family. Checkpoint selection uses validation
-only; separate calibration and untouched evaluation follow training.
-
-Use the existing isolated experiment environment on GX10:
+An experimental natural-language decision scorer built by tuning Qwen3-4B.
+Give it context, a question and possible answers; it returns a probability for
+each answer. The project includes a browser playground and a Jev-compatible API.
+
+**Credit:** OpenSysOne is inspired by [Jev](https://typesafe.ai/), TypeSafe.ai's
+System One model for structured decisions with probabilities. Credit goes to the
+TypeSafe team for motivating this independent experimental implementation.
+
+**Training and evaluation are complete.** Start with the
+[accuracy and speed report](results/report.md), [browser playground guide](docs/usage/playground.md),
+or [API guide](docs/usage/jev-api.md). The calibrated model and publication files
+are available in [andyshu/opensysone on Hugging Face](https://huggingface.co/andyshu/opensysone).
+
+## Results
+
+| Reserved evaluation | Examples | Trained 4B | Pretrained per-option verifier |
+| --- | ---: | ---: | ---: |
+| Original four-family test | 2,042 | **92.90%** | 84.48% |
+| Social IQA family holdout | 768 | **72.92%** | 70.31% |
+
+Current inference has an accuracy–speed tradeoff. On one idle Spark in FP32,
+one four-choice question with a 768-token state takes **3.710 s** for the trained
+scorer, **3.177 s** for the pretrained per-option verifier and **0.818 s** for a
+pretrained joint answer-label prompt. On a separate matched 320-example sample,
+the trained and joint-label methods score **89.06%** and **86.25%**. These warm
+measurements exclude model loading and HTTP. See the report for all workloads,
+calibration scores, confidence intervals and limits.
+
+The selected weights are Spark B step 1,500, retained identically at expanded
+branch step 0. A separate 510-example calibration split fits temperature 1.745822.
+The model uses custom rank-8 adapters and a scalar head; it requires the pinned
+pretrained base and the project's reconstruction code. It is not a generic
+Transformers or PEFT checkpoint. Social IQA was excluded from our fine-tuning;
+exposure during base-model pretraining is unknown.
+
+## Repository layout
+
+| Location | Purpose |
+| --- | --- |
+| [docs/](docs/README.md) | Usage guides, research notes and operational records |
+| [results/](results/README.md) | Final report and charts, plus preserved historical evidence |
+| [examples/](examples/) | Sample API request |
+| [web/](web/) | Browser playground assets |
+| [scripts/](scripts/) | Training, evaluation, verification and archival tools |
+| [tests/](tests/) | CPU and harness checks |
+| `experiment.py`, `training_model.py`, `selection.py` | Training, reconstruction and checkpoint selection |
+| `jev_harness.py`, `playground.py` | API and interactive model comparison |
+| `smoke_train.py`, `decision_model.py`, `smoke_data.py` | Original 0.5B smoke and shared-prefix experiments |
+
+## Try it on the existing GX10 installation
+
+The GUI uses port **7466**. On the Mac, run `ssh -N -L 7466:127.0.0.1:7466 gx10`
+and open **http://localhost:7466**. Select **Qwen3 4B · Selected** for the calibrated
+release. The local API is on loopback port **18081**:
 
 ```bash
-cd ~/projects/opensysone
-~/ai/envs/opensysone/bin/python -m unittest discover -s tests -v
-~/ai/envs/opensysone/bin/python scripts/campaign_status.py
+curl --fail-with-body http://127.0.0.1:18081/v1/systemone \
+  -H 'Content-Type: application/json' --data-binary @examples/jev_request.json
 ```
 
-Artifacts, including optimizer/RNG state, are written to `~/ai/opensysone/runs/`.
-Pretrained weights are under `~/ai/models/opensysone`; frozen public data is under
-`~/ai/opensysone/data`. The isolated environment reuses torch/Transformers read-only
-and adds pyarrow 25.0.1. The shared Python environment is not modified. Real-model
-checkpoint reload, longest-input training gradients and authenticated loopback
-HTTP are checked before the 24-hour campaign. The campaign checkpoints on a
-cadence independent of evaluation, reserves at least two hours for final evaluation and
-automatically starts the calibrated local API. The fleet runs independent 4B and
-2B candidates, selects on the same 512 validation decisions, and evaluates the
-selected artifact after training. Exact paths and state are in the
-handover. These experiments do not establish Jev-level intelligence.
-
-[JEV_HARNESS.md](JEV_HARNESS.md) documents local inference, hosted Jev calls,
-response/timing comparisons, bearer authentication and Mac SSH tunneling. Hosted
-calls use `TYPESAFE_API_KEY`; local inference does not make remote calls.
-
-The original pinned Qwen2.5-0.5B synthetic smoke, partial tuning and verified
-shared-prefix caching remain available in `smoke.py` and `decision_model.py`:
-
-```bash
-bash scripts/run_smoke.sh
-```
+For another machine, read the [reconstruction requirements](docs/publication/model.md)
+and [reproduction guide](docs/publication/reproduce.md). The checkpoint records
+its pinned local base-model path; downloading the adapter alone is insufficient.
+Hosted Jev requests require `TYPESAFE_API_KEY`; no hosted Jev benchmark is claimed.
 
-The new scorers use bounded full forwards; cache branching has not been verified
-for them. CPU tests cover adapters, categorical gradients, restoration, API shapes
-and the old model's cache correctness.
+## Development and continuation
 
-Investigate the preserved BF16 checkpoint without changing its weights:
+The existing isolated environment on GX10 can run the CPU suite:
 
 ```bash
-free -b
-nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv
-bash scripts/run_precision.sh --run ~/ai/opensysone/runs/20260916T154714Z
-cat ~/ai/opensysone/runs/LAST_PRECISION_RUN
+~/ai/envs/opensysone/bin/python -m unittest discover -s tests -v
 ```
 
-The precision wrapper uses the smoke's lock, 25-minute timeout and 16 GiB CUDA
-allocation cap. Each fresh run stores provenance and raw comparisons under
-`artifacts/`, plus `run.log` and `exit_code`. Exit 0 means the diagnosis completed;
-read the numerical comparisons to determine whether a precision passes.
-
-See [NEXT_STEPS.md](NEXT_STEPS.md) for the September 17 findings, active refinement
-and assessment of joint work on the two Sparks.
-
-Current fleet status: `python3 scripts/fleet_status.py` (or `--json`).
-
-Hugging Face backup and final-publication controls: [HUGGINGFACE.md](HUGGINGFACE.md).
+Read [HANDOVER.md](HANDOVER.md), [PLAN.md](PLAN.md) and [RESULTS.md](RESULTS.md)
+before continuing experiments. Model weights, optimizer state and runtime logs
+live outside this source tree under `~/ai/models/opensysone` and
+`~/ai/opensysone/runs`. Dataset/source pins and checkpoint provenance accompany
+the archived results. No additional training is active or scheduled by this
+publication cleanup.

source/RESULTS.md

@@ -1,540 +1,29 @@
-# GX10 smoke results — 2026-09-16
+# OpenSysOne results
 
-The 0.5B model trains, its artifact reconstructs, and FP32 shared-prefix scoring
-passes correctness checks. **BF16 failed batch invariance on this checkpoint and
-stack.** The result supports continuing the experiment; it does not establish a
-useful zero-shot decision model or calibrated deployment probabilities.
+Training and evaluation completed on 17 September 2026. The selected Qwen3-4B
+model uses rank-8 adapters and a scalar decision head. Its weights are Spark B
+step 1,500, retained unchanged at expanded branch step 0. Selection used validation
+only; temperature was fitted on a separate 510-decision calibration split.
 
-## Reference experiment
-
-Completed run: `20260916T155124Z`, source commit `34a993e`, exit **0**.
-Full artifacts: GX10 `/home/andy/ai/opensysone/runs/20260916T155124Z/`.
-Small artifacts: [results/20260916T155124Z](results/20260916T155124Z/).
-
-Pinned pretrained `Qwen/Qwen2.5-0.5B` revision
-`060db6499f32faf8b98477b0a26969ef7d8b9987`: 494,033,665 total parameters including
-the scalar head, **29,825,665 trainable** (final two layers and head). FP32 weights,
-AdamW state and inference, SDPA; no LM next-token training loss or generated answers.
-Backbone learning rate 2e-5, head 1e-3, gradient clipping 1, 60 steps, four decisions
-per batch. The rest of the pretrained backbone is frozen.
-
-Invented inventory facts supply 192 train, 48 calibration and 72 test decisions.
-Each group shares one state across color, seal and quantity questions; candidate
-orders are shuffled. Entity IDs are disjoint, but templates and underlying fact
-combinations overlap. These are simple wiring/optimization examples, **not a
-semantic holdout or a real task-family generalization benchmark**.
-
-| Same FP32 run; 72 test decisions | Accuracy | NLL | Brier, class sum | Top-label ECE, 10 bins |
-| --- | ---: | ---: | ---: | ---: |
-| Base yes-minus-no token score | 66.7% | 1.090 | 0.574 | 0.290 |
-| Initial zero scalar head | 27.8% | 1.059 | 0.639 | 0.083 |
-| Trained scalar | 68.1% | 0.628 | 0.419 | 0.205 |
-| Trained + calibration-split temperature | 68.1% | 0.568 | 0.370 | 0.141 |
-
-The trained model gets **one more example** correct than the matched token
-baseline. This is not evidence of an accuracy gain. NLL/Brier improve on this
-tiny synthetic set; the temperature (1.88365) was selected using only the separate
-48-example calibration split. There is no basis for a general calibration claim.
-The uniform head's low ECE despite poor accuracy illustrates why ECE alone is
-not the selection criterion. NLL uses stable log-softmax, without probability clipping.
-
-The optimization loop including periodic saves took **8.80 seconds**; median step
-was 105 ms. This is partial tuning on very short inputs and is not a full-model
-training throughput estimate. Maximum allocated CUDA memory over training, eval
-and the timing grid was **3.43 GiB**, reserved **3.65 GiB**, against a 16 GiB cap.
-The query-projection probe changed by max 0.000964; scalar weight norm became
-0.2667. Full parameter and artifact provenance is in `manifest.json`.
-
-## Shared-prefix correctness and timings
-
-The tiny random FP32 CPU model passes four tests, including mixed lengths,
-chunk sizes 1/2/4/16, candidate permutation, unrelated-question perturbation,
-gradient flow and equivalence of selected token logits to full vocabulary logits.
-
-On the trained GPU model, probability maximum absolute differences were:
-
-| Comparison | Difference |
-| --- | ---: |
-| Full forward vs shared prefix | 0.00000304 |
-| Question batch vs isolated question | 0.00000381 |
-| Candidate permutation, restored order | 0.00000131 |
-| Repeated prefix call | 0 |
-| Reset all trainable tensors, reload checkpoint | 0 |
-
-The first successful run recorded the original 0.02 tolerance. Its actual errors
-are below 0.000004. The continuation harness tightens FP32 tolerance to **0.0001**;
-BF16 retains the original gate so its known failure remains visible.
-
-Illustrative end-to-end warm medians, including tokenization, cache copies and
-device synchronization. One warm-up plus **three measured repeats** per cell;
-these are not p95 or production claims. Same trained checkpoint/serialized token
-IDs in both modes, two candidates/question, maximum eight branches per chunk.
-The full reference is already batched fairly (four two-choice questions at once).
-
-| Actual state-prefix tokens | Questions | Full batched forwards | Shared prefix | Speedup |
-| --- | ---: | ---: | ---: | ---: |
-| 143 | 1 | 35.9 ms | 47.2 ms | 0.76× |
-| 143 | 4 | 117.3 ms | 50.6 ms | 2.32× |
-| 143 | 16 | 464.5 ms | 129.2 ms | 3.60× |
-| 1,031 | 1 | 312.0 ms | 184.6 ms | 1.69× |
-| 1,031 | 4 | 1,262.6 ms | 201.6 ms | 6.26× |
-| 1,031 | 16 | 5,033.1 ms | 336.1 ms | 14.97× |
-
-Caching loses on the shortest one-question case. At 1,031 tokens/16 questions,
-the shared run spends about 156 ms in prefill and 178 ms in branches; single-prefix
-KV occupies 24.2 MiB before the per-chunk copies. That longer-context point
-demonstrates amortization in this implementation. The repeated short question is
-a workload timing probe, not a semantic multi-question benchmark. No generation,
-constrained decoding, service throughput or 1.5B/3B latency comparison has run.
-
-## Failed BF16 experiment and diagnosis
-
-Run `20260916T154714Z`, source `4d6cb0f`, completed its 60 training steps but
-exited **1** at the correctness gate. The checkpoint and all earlier predictions
-remain available; no performance conclusion was taken from that failed run.
-
-The same trained BF16 weights were evaluated with different precision/backends:
-
-| Comparison | BF16 probability difference | Same weights cast to FP32 |
-| --- | ---: | ---: |
-| Full vs shared | 0.08544 | 0.00000727 |
-| Shared vs isolated | 0.09897 | 0.00000519 |
-| Shared candidate permutation | 0.07889 | 0.00000137 |
-| Batched full vs separate full calls | 0.05262 | 0.00001433 |
-
-SDPA MATH retains the BF16 failure and passes in FP32. This demonstrates precision
-and batch-shape sensitivity beyond cache handling; it does **not** isolate the
-root cause to a specific kernel or prove every GB10/model fails in BF16. The
-BF16 token baseline had different metrics from FP32 and must not be mixed into
-the matched FP32 comparison above. BF16 AdamW also lacks FP32 master weights in
-this simple implementation, making small updates prone to rounding.
-
-Raw evidence: [parity_diagnosis.json](results/20260916T154714Z/parity_diagnosis.json).
-Reproducer: `scripts/diagnose_parity.py --run <failed-run-directory>`.
-Keep the FP32 reference; investigate BF16 explicitly before scaling.
-
-## Expanded precision investigation — 2026-09-16
-
-Completed read-only runs `20260916T161253Z-precision` and
-`20260916T161355Z-precision`, both exit **0**. The second run used clean source
-commit **`409ade4`**; the first manifest records `94a24e8` with staged additions,
-whose script hashes correspond to `b9dd165`. Full artifacts are under GX10
-`/home/andy/ai/opensysone/runs/<run-id>/artifacts/`; small copies are in
-[results/20260916T161355Z-precision](results/20260916T161355Z-precision/).
-
-All ablations reconstruct the preserved BF16-trained checkpoint from
-`20260916T154714Z`; its SHA-256 remained
-`106efdfb0794e6ca870b7add11c71f06c58281ef46b348305866a85f1e6f6bc8`.
-The base, data and checkpoint are unchanged. This comparison concerns arithmetic
-on the same weights, rather than FP32 versus BF16 training quality. It does not
-evaluate a new task or supply generalization evidence.
-
-The expanded test covers **all 24 groups / 72 decisions**, comparing batched
-full calls with separate question calls, full with cached, cache chunks of 4/16,
-cached with isolated questions, and restored candidate permutations. The table
-shows the worst absolute probability difference across these comparisons.
-Strict reduction sets `allow_bf16_reduced_precision_reduction=False`. FP32 linear
-casts each decoder linear's inputs and weights to FP32, then casts its output
-back to BF16; it is an inference diagnostic, not a validated training method.
-
-| Arithmetic configuration | Worst probability difference | Groups above BF16's original 0.02 gate |
-| --- | ---: | ---: |
-| BF16 default SDPA | 0.238608 | 24/24 |
-| BF16, strict reduction | 0.213011 | 24/24 |
-| BF16, math SDPA + strict reduction | 0.168008 | 24/24 |
-| BF16, FP32 linear + strict reduction | 0.147468 | 24/24 |
-| BF16, math SDPA + FP32 linear + strict reduction | 0.183657 | 24/24 |
-| Same weights cast to FP32, default SDPA | 0.00002138 | 0/24 |
-
-FP32 also passes the stricter **0.0001** gate. Repeated full and repeated cached
-calls have exactly zero probability difference in every group/configuration.
-Full candidate permutations also match exactly; cached permutations can change
-which branches share a chunk and still fail in BF16. Exit 0 means the diagnostic
-completed, not that BF16 passed.
-
-Final-candidate-token traces for the first serialized group narrow the issue:
-embeddings and first input normalization match exactly, but default BF16's first
-query/key projections differ by up to **0.5** between batched/separate calls.
-Strict reduction removes those initial projection differences in this trace;
-later differences remain. Combined math attention and FP32 linears reduce the
-first decoder-layer difference from 0.02344 to 0.00003052, yet the final normalized
-hidden representation still differs by up to 2.0. This supports shape-dependent
-numerical differences that propagate through the decoder. It does not isolate
-every contributing operation or establish a particular kernel defect. The trace
-samples final candidate tokens, not every token's intermediate representation.
-
-Peak CUDA allocation was **1.90 GiB**, reserved **1.94 GiB**, against the 16 GiB
-cap. The shared environment was unchanged and OOM score adjustment was 0.
-All four CPU correctness tests passed before execution. Both diagnostic PIDs
-exited; at 16:15 UTC GX10 again had about 118 GiB available and only the original
-router GPU process. Continue useful model/data work in FP32; none of these BF16
-interventions justifies reopening its correctness gate.
-
-## Public-data 2B adapter pilot — 2026-09-16
-
-Run `/home/andy/ai/opensysone/runs/20260916T182352Z-train/artifacts`,
-execution source **`f1c9322`**, exited **0** after **40 optimizer steps**
-(160 decisions), not three completed epochs. The model is pinned
-`Qwen/Qwen3.5-2B` at `15852e8c16360a2fea060d615a32b45270f8a8fc`.
-Only its text decoder is retained; the unused vision encoder is discarded before
-CUDA loading. Rank-16 additive linear adapters and a pretrained yes-minus-no
-initialized head train **16,821,249 of 1,898,646,337 parameters** in FP32.
-
-The frozen data has 40,941 source-group-disjoint train decisions, 512 validation,
-512 calibration, 2,048 source test and 768 completely held-out Social IQA decisions.
-This model's 768-token complete-chat limit excludes four BoolQ train rows and one
-test row, leaving 40,937/512/512/2,047/768. Banking77 is a four-choice target-plus-
-three-negative transformation, not a full 77-way benchmark. Source-group splitting
-does not rule out pretraining contamination or semantic duplicates.
-
-| Family | Initial validation accuracy | Step 40 accuracy |
-| --- | ---: | ---: |
-| ARC | 74.22% | 81.25% |
-| Banking77 four-choice | 83.59% | 88.28% |
-| BoolQ | 64.84% | 79.69% |
-| SNLI | 64.06% | 82.03% |
-| All 512 decisions | **71.68%** | **82.81%** |
-
-Validation macro-family NLL fell from **0.700136 to 0.498153**. This is validation
-selection evidence, not untouched test improvement. No calibration, test or
-Social IQA predictions have been evaluated in this pilot. Median four-decision
-step was **3.869 s**; the loop including final validation took 298.1 s.
-Peak CUDA allocation/reservation was **7.746/7.855 GiB**, below the 16 GiB cap.
-All final permutation/chunk/isolation checks passed the 0.0001 probability gate,
-with worst difference **0.00000614**. The old repeat label also changed chunk
-shape; the current source restores the original chunk size before repeat testing.
-
-A fresh process in `20260916T183240Z-train`, source **`980d881`**, reconstructed
-step 40 and reproduced **all 512 raw logits and probabilities exactly**, restored
-optimizer/RNG, then completed step 41 with finite gradient norm 3.676.
-It exited **0** and all final parity gates passed, worst difference 0.00000316.
-Step 41 validation macro NLL was 0.494478. The retained setup failure
-`20260916T182256Z-train` exited 1 before any optimizer step because Transformers'
-new chat-template return default was a BatchEncoding; explicit `return_dict=False`
-fixed it without changing the shared environment.
-
-Small raw pilot evidence is in [results/20260916T182352Z-train](results/20260916T182352Z-train/).
-Checkpoint SHA-256 is
-`af5790ae2f2b56477ebbdf6ab9c418d895e48d2bf5a6416e11b6e9863ad1db55`;
-validation-selected best SHA-256 is
-`82b4261feb98d3ed56291e4c03304a65da20ce0194a6ad117d113b30d152282e`.
-New dependencies are isolated in `~/ai/envs/opensysone` (pyarrow 25.0.1), with
-read-only reuse of the existing torch/Transformers packages. The Jev-compatible
-stdlib harness and 12 CPU tests pass; real-checkpoint HTTP and longest-input
-stress are the next gate before the larger campaign.
-
-## Public-data 4B pilot selected for the 24-hour run
-
-Run `/home/andy/ai/opensysone/runs/20260916T183823Z-train/artifacts`, clean execution
-source **`ccbbe6d`**, exited **0** after 40 steps / 160 decisions. The base is
-`Qwen/Qwen3-4B-Instruct-2507`, pinned to
-`cdbee75f17c01a7cc42f958dc650907174af0554`, Apache-2.0.
-Rank-8 adapters (alpha 16) and the pretrained initialized head train
-**16,517,633 of 4,038,985,729 parameters** in FP32. Exact two-pass categorical
-gradients keep one candidate graph live; CPU gradients match ordinary CE within
-0.000001. Gradient checkpointing is enabled. No quantization or new kernels.
-
-| Family | Initial validation accuracy | Step 40 accuracy | Step 40 NLL |
+| Reserved evaluation | Decisions | Selected accuracy | Pretrained verifier |
 | --- | ---: | ---: | ---: |
-| ARC | 90.63% | 90.63% | 0.374400 |
-| Banking77 four-choice | 90.63% | 91.41% | 0.229941 |
-| BoolQ | 82.03% | 84.38% | 0.583093 |
-| SNLI | 82.81% | 83.59% | 0.395211 |
-| All 512 validation decisions | **86.52%** | **87.50%** | **0.395661** |
-
-Raw validation macro NLL improves from **1.436162 to 0.395661**; the initial
-readout was severely overconfident. A separately recorded diagnostic fits and
-scores a temperature on the same validation rows (NLL 0.407737, T 6.9183): it is
-optimistic validation analysis, not independent calibration. Reserved calibration,
-test and Social IQA predictions remain unevaluated. The trained 4B validation
-accuracy and NLL beat the 2B pilot in every family, supporting the larger candidate
-despite its lower throughput. This does not prove task generalization.
-
-The 512-token complete-chat limit retains **40,915 train / 512 validation /
-510 calibration / 2,042 test / 768 Social IQA** decisions; it drops 26 train,
-two calibration and six test BoolQ rows, with no silent truncation.
-Median four-decision step is **8.956 s**; 55,268 actual branch tokens were
-processed with no padding overhead. The loop including final validation takes
-724.0 s. Initial validation alone takes 327.85 s. Peak CUDA allocated/reserved
-is **15.510/15.604 GiB** against the 16 GiB cap. OOM adjustment is 0 and about
-99 GiB unified RAM remains available with the model loaded.
-Final correctness passes all 0.0001 gates, worst probability difference
-**0.00000167**, with exact repeated, isolated and restored-permutation predictions.
-
-Checkpoint SHA-256:
-`e26f75b2396de88311873fac4eb91e1e40d0ec940778ec99f282bcfd96a2e258`.
-Best SHA-256:
-`64977ee0b1a6147c6faf59283edea9adf564dd36d53f4580bc20940b94c6764f`.
-Small raw evidence is in [results/20260916T183823Z-train](results/20260916T183823Z-train/).
-Fresh reload, longest-input gradients with restored optimizer state, 1,024-token
-HTTP inference, and 255-choice HTTP stress **all passed** (verification exit 0).
-Reload matches all 16 checked validation predictions exactly. Longest training
-input is 509 tokens and peaks at 15.624 GiB with optimizer state; inference peaks
-at 15.465 GiB. The long HTTP request has 1,023 tokens in each of two candidate
-branches and matches direct inference exactly. Invalid-key/oversized-input
-requests return 401/422. One warm three-question request takes 1.571 s, and one
-255-choice request takes 47.042 s; these are wiring stress timings, not latency
-percentiles or intelligence benchmarks. The checkpoint SHA-256 is unchanged.
-Evidence: [results/20260916T185718Z-verify4b](results/20260916T185718Z-verify4b/).
-All **15 CPU tests pass**, including unequal-source-group bootstrap weighting
-and the measured evaluation-reserve calculation. The live Jev HTTPS endpoint
-returns 405 to an unauthenticated GET; no credentials or state were sent and no
-authenticated hosted inference has been tested.
-
-## Detached 24-hour campaign now running
-
-Launched **2026-09-16 18:59:10 UTC** from clean source **`0109eb6`** into
-`/home/andy/ai/opensysone/runs/20260916T185910Z-24h`. Supervisor PID is **1085496**,
-current trainer **1085517**; both OOM score adjustments are 0. Training resumes the
-4B step-40 checkpoint with optimizer/RNG restored, preserves validation-selected
-best and all model/data/config signatures, and has passed the initial FP32
-correctness gates. Exit is **pending**; the API has not started yet.
-
-Fresh restart reproduces **all 512 raw logits and probabilities exactly**;
-the reference and fresh prediction JSON SHA-256 are both
-`e671e1508185765552b0f933ba03f356be62143c531d8ef534457d34b1645c9b`.
-The next four updates, **41–44**, have finite losses/gradients and remain under
-the cap. Step 41 takes 8.724 s, loss 0.115940, gradient norm 3.81358.
-This proves reconstruction plus subsequent optimizer updates, not a bitwise
-interrupted-versus-uninterrupted trajectory comparison. Raw verification is in
-the launch evidence directory below. The durable checkpoint remains step 40
-until the regular save cadence, independently of those logged newer updates.
-
-Training ends by **2026-09-17 16:16:10 UTC**, reserving two hours until the final
-**18:16:10 UTC / 19:16:10 BST** deadline. The reserve estimates 6,640 base/tuned
-prediction rows at 4,251.8 seconds from measured pilot validation speed, adds
-30% plus ten minutes for setup, and keeps a two-hour minimum. Checkpoints save
-every 250 steps or 900 seconds regardless of evaluation; validation is every
-500 steps with patience eight. The three-epoch target is an upper bound.
-
-After successful training, the runner loads the best artifact fresh, calibrates
-only on the 510 reserved known-family decisions, saves a deployable checkpoint
-before untouched evaluation, records raw/calibrated test and Social IQA metrics
-against the unchanged pretrained scorer, and starts the loopback API only after
-complete evaluation and a real-model inference check. Deployment is planned at
-`http://127.0.0.1:18081`, with 1,024-token inputs. No hosted Jev call runs
-automatically. Small launch evidence lives in
-[results/20260916T185910Z-24h-launch](results/20260916T185910Z-24h-launch/), separate
-from the completion-results directory reserved by the runner.
-
-Current inspection, stop and same-deadline recovery commands are in
-[HANDOVER.md](HANDOVER.md). A running job is not a finalized model or successful
-test result. The frozen-family controls and independent calibration remain the
-quality gates for final reporting. The complete 15-test suite passed; the new
-orphan-child stop safeguard also passes an integration test that refuses to
-terminate a PID when its command line differs from the recorded command.
-
-## Three-machine expansion — 2026-09-16 evening
-
-The user assigned GX10 and both Sparks to this task and authorized terminating
-their workloads. The Spark serving head and RPC worker were stopped in order
-with verified SIGTERM; both released their GPU allocations and each had about
-118 GiB available afterward. Their weights/cache and exact restoration commands
-are retained. No network or system configuration changed.
-
-Both Sparks now have isolated copies of the exact GX10 training dependencies:
-21,368 installed file hashes and 55 package versions match. CPU autograd and both
-Qwen-family imports pass. This initial check verified the environments. Subsequently all pinned model
-files and real GPU training/HTTP checks passed; see the launch results below.
-
-The original GX10 campaign saved step 128 before a requested stop. Its trainer
-exceeded the 30-second grace while performing final correctness checks and exited
--9; the complete step-128 checkpoint and optimizer/RNG are verified intact. The
-new source records skipped final checks explicitly on a requested stop. It also
-retains step-specific prediction evidence before publishing each new best artifact
-and selects a restored checkpoint if its fresh validation improves the best.
-
-The intermediate GX10 campaign `20260916T192239Z-24h`, source `6e080e2`, restored
-step 128 and later stopped gracefully at step 178 with training exit 0.
-The Spark alternatives are a 4B weights-only warm initialization with fresh Adam,
-learning rate 0.00003 and 7,500-step cosine horizon, and a longer 2B continuation.
-The planned fleet cutoff is 2026-09-17 16:00 UTC, leaving 2 h 16 min until the
-original final deadline. All training remains under 16 GiB per process.
-
-All **32 initial fleet CPU tests passed**, including weights-only initialization, optimizer/RNG
-resume, requested-stop evidence, deadline handling, exact validation-set matching,
-checkpoint/metric mismatch rejection and API deployment lifecycle. The coordinator
-recomputes its criterion from all 512 saved validation predictions and freezes
-selection before calibration/test/holdout. Read-only compatibility checks of the
-real 4B and 2B pilot artifacts pass, reproducing NLL 0.395661 and 0.498153.
-Small setup proofs are in [results/20260916-fleet-setup](results/20260916-fleet-setup/).
-Live paths, statuses and recovery instructions are in [FLEET_RUN.md](FLEET_RUN.md).
-
-The subsequent selection revision uses the frozen four-fold source-group-disjoint
-temperature-crossfit policy `crossfit_temperature_nll_v1` (seed 431, 101 positive
-temperatures, family-balanced fitting and scoring). Step 128's validation accuracy
-is **89.0625%**, versus step 40's 87.5%; raw NLL is 0.442683 versus 0.395661.
-Crossfit NLL reverses that ranking: **0.318518 versus 0.359522**, improving in all
-four families. A 5,000-replicate paired source-group bootstrap, refitting the
-temperatures, gives difference -0.041005 with 95% interval [-0.079822, -0.001152].
-The accuracy gain alone is uncertain (29 gains, 21 losses; McNemar p=0.322).
-This supports accounting for recoverable overconfidence during checkpoint
-selection. It is a validation-driven criterion revision, not independent test
-evidence. No reserved predictions were read. Original raw-selected checkpoints
-remain preserved, and final calibration still uses the separate reserved split.
-All **37 tests pass** after adding policy/selection checks; the updated CPU
-integration also proves reselection leaves trained weights and Adam steps intact.
-Raw diagnostic: [selection-diagnostic.json](results/20260916-fleet-setup/selection-diagnostic.json).
-
-## Active fleet launch — 2026-09-16 19:44 UTC
-
-Three training-only campaigns are active on source **`4a60423`**:
-GX10 `20260916T193741Z-24h` (4B, LR 0.0001), spark-a
-`20260916T194258Z-24h` (4B, LR 0.00003, 7,500-step cosine horizon), and spark-b
-`20260916T193803Z-24h` (2B, LR 0.0001). Each uses the fixed crossfit criterion,
-16 GiB allocation cap and 2026-09-17 16:00 UTC cutoff. Training exit statuses
-remain pending. The fleet coordinator `20260916T194403396250Z-fleet`, source
-**`6a7b0ed`**, is detached on GX10 and waiting for selection; no reserved-data
-predictions or final calibration have run. The cutoff shutdown race is covered
-by a regression test, and all nine fleet tests pass after that fix.
-
-GX10 restored step 178's weights, Adam and Python/torch/CUDA RNG exactly. Its
-fresh 512-decision validation reached **90.4297% accuracy, 0.303825 crossfit NLL,
-0.404198 raw NLL**, promoting the durable best beyond step 128. Fresh FP32
-correctness passes (worst probability difference 4.77e-7), and resumed updates
-are finite. These are validation results, not independent test evidence.
-
-Spark A reproduced all 512 original 4B pilot predictions exactly before eight
-finite lower-rate updates (median 8.086 seconds, peak 15.505 GiB). That pilot
-exited 0; its step-8 accuracy 86.914% / raw NLL 0.405397 did not improve the
-starting checkpoint. The long-run crossfit selector re-evaluates both inherited
-best and current checkpoint. Real fresh-artifact verification exited 0: exact
-16-decision reload, finite restored-Adam gradients on the longest 509-token
-input, 15.624 GiB peak, 1,023-token HTTP/direct match, expected 401/422 errors,
-and 255 choices in 43.31 seconds. The long campaign reproduced all 512 step-8
-raw predictions exactly, with identical weights/Adam/RNG. Its fixed crossfit
-criterion selected step 8 at 0.358235 NLL, and new updates are finite. No
-independent generalization improvement is claimed for the short pilot.
-
-Spark B's preparation exited 0. Fresh verification passed exact reload,
-restored-Adam gradients at 700 tokens (8.123 GiB peak), 1,024-token inference,
-authentication/length errors and 255 choices in 17.73 seconds. The long campaign
-reproduced all 512 original validation predictions exactly, scoring 82.8125%
-accuracy / 0.476072 crossfit NLL / 0.498153 raw NLL before resumed training.
-Subsequent finite updates reached step 98 by 19:43:59 UTC. Timing observations
-are individual wiring checks, not p50/p95 latency measurements.
-
-Full small evidence, source revisions, frozen plan and startup state snapshots
-are under [results/20260916-fleet-setup](results/20260916-fleet-setup/). Live
-state, inspection/stop/resume and serving-pair restoration are in
-[FLEET_RUN.md](FLEET_RUN.md). Final calibrated test/holdout metrics and selected-model
-API deployment are pending; authenticated hosted Jev inference still requires
-`TYPESAFE_API_KEY`.
-
-## Overnight progress and next experiment — 2026-09-17
-
-The 02:10–02:15 UTC audit found both 4B jobs healthy and improving, while the 2B
-campaign completed cleanly at **01:59:29 UTC**, training and supervisor exit **0**.
-All recorded losses/gradients were finite. Peak allocation was 15.624 GiB on each
-4B job and 8.183 GiB on the 2B job; the 16 GiB cap remains unchanged.
-
-| Candidate | Last audited step | Selected step | Crossfit validation NLL | Selected accuracy |
-| --- | ---: | ---: | ---: | ---: |
-| GX10 4B, LR 1e-4 | 2,570 | 2,500 | **0.188640** | **93.55%** |
-| Spark A 4B, LR 3e-5 | 2,529 | 2,500 | 0.218012 | 92.58% |
-| Spark B 2B, LR 1e-4 | 6,000 | 2,000 | 0.255294 | 89.84% |
-
-These are the same 512 validation decisions, selected with the unchanged fixed
-crossfit policy. No reserved calibration, test or Social IQA predictions have
-been read. A higher maximum accuracy at a different step does not override the
-selection criterion. Spark A improved at all five scheduled validations. Spark B
-stopped after eight evaluations without a new best; its final step-6,000 score
-was 0.351593 / 86.91%. Final numerical correctness passed at worst 6.56e-7.
-The selected step-2,000 and resumable step-6,000 artifacts are preserved.
-
-The freed Spark B is training a **fourth candidate**, initialized from a frozen
-copy of GX10's step-2,500 selected weights (SHA-256
-`8956eb6c0cfbb02124aeefd99c3b418c55f55fdb9a64260350622d98dbba1aec`).
-Fresh Adam, seed 432, LR/head LR 1e-5 and a 5,000-step cosine horizon define a new
-trajectory. Other model/batch/token/correctness settings and both absolute
-deadlines stay unchanged. The eight-step pilot started at **02:16:05 UTC**;
-source `4a60423`. The pinned 4B model copied from Spark A over the existing link
-passed all 13 file hashes. Warm initialization preserves all 506 trainable tensors
-exactly and deliberately starts with an empty optimizer. All 512 initial raw
-predictions match the parent exactly. The eight-step pilot and fresh verifier
-exited 0: exact 16-decision reload, finite longest-input gradients, 15.623 GiB
-peak, direct/HTTP agreement at 1,023 tokens, expected 401/422 and 255 choices
-in 45.44 seconds. These timings are individual wiring checks, not percentiles.
-Campaign `20260917T023137Z-24h` launched at 02:31:37 UTC, restoring the complete
-step-8 optimizer/RNG state exactly, and was added as the fourth fleet candidate.
-Its inherited selected branch step 0 retains the parent score: step 8 scored
-0.188576, a change below the fixed 0.001 improvement threshold. The short pilot
-does not establish a quality gain.
-
-[Small audit evidence](results/20260917-fleet-progress/) records the 22 scheduled
-validation points, live processes, source revisions, selected-checkpoint hashes
-and frozen refinement parent. [NEXT_STEPS.md](NEXT_STEPS.md) records the decisions
-and the two-Spark alternatives: independent candidates now, bounded distributed
-adapter-gradient training or parallel scoring next. Active ConnectX/RoCE and
-installed NCCL do not establish collective correctness or useful speedup. The
-current two-pass trainer needs explicit synchronization changes, and its measured
-peak leaves only about 385 MiB for additional GPU allocations under the cap.
-
-## Fixed validation ensemble diagnostic — 2026-09-17
-
-Saved, identity-matched validation logits were combined with fixed equal weights
-and the unchanged crossfit-temperature policy; no weights were tuned and no
-reserved predictions were accessed. GX10 4B + Spark A 4B scores **93.16% /
-0.193983 NLL**, worse than GX10 alone (**93.55% / 0.188640**). Spark A 4B + the
-completed Spark B 2B scores **93.55% / 0.180560**. This more diverse pair shares
-19 errors versus 29 for the two-4B pair, but gains eight/losses eight versus GX10.
-The mixed pair's NLL difference versus GX10 is -0.008080; a 1,000-replicate paired
-source-group bootstrap with fold-temperature refitting yields 95% interval
-**[-0.039064, +0.019451]**. No gain over the best single model is established.
-These are exploratory validation results from already selected checkpoints,
-not independent generalization evidence. The deployed-candidate protocol remains
-individual models; ensemble inference and latency have not been implemented or
-measured. The exact A step-2,500 and B step-2,000 artifacts are frozen on GX10 in
-`20260917T022201Z-ensemble-reference`, with 134.3 MB copied, stable source hashes
-and CPU reconstruction/provenance checks. No weights are in Git.
-[Analysis and provenance](results/20260917-fleet-progress/fixed-ensemble-validation.json).
-
-## Remaining gates
-
-The active continuation state and checkpoint-resume verification are recorded in
-[HANDOVER.md](HANDOVER.md). Public multi-family training and the frozen unseen-family
-holdout are now implemented; independent calibration/test/holdout metrics await
-the 24-hour campaign's finalization. Frozen-head and generation controls, new-model
-prefix caching and the larger latency matrix remain open. The Sparks now host
-independent candidate experiments; GX10 does not need a ConnectX cable for this
-selection strategy. Architecture B and
-distributed training still await quality and profiling evidence in [PLAN.md](PLAN.md).
-
-## Expanded public training data — 2026-09-17
-
-Version 2 retains all 40,915 original 4B-compatible training decisions and adds
-16,000 HellaSwag, 14,360 PIQA and 9,490 CommonsenseQA decisions: **80,765 total**.
-All four reserved source files and tokenized sequences match version 1 exactly.
-The 383 retained new-source diagnostics stay outside training and checkpoint
-selection. An independent reconstruction audit checked every added source label
-and shuffled answer position, all downloaded hashes and diagnostic exclusions.
-See [EXPANDED_DATA.md](EXPANDED_DATA.md) and its linked small evidence.
-
-Clean source `24b8ccf`, pilot `20260917T070758Z-train`: eight finite updates,
-**exit 0**, all initial/final FP32 gates passed. Frozen Spark B parent step 1,500
-reproduces every initial validation logit and probability exactly. Captured step
-0 has empty Adam; every final Adam counter is eight. Median update 8.90 seconds,
-peak allocation including checks 15.426 GiB, worst final probability discrepancy
-3.58e-7. The 32 sampled decisions cover all seven task families.
-
-Step 8 scores 0.170108 validation crossfit NLL versus parent 0.170150, both
-94.7266% accuracy. The difference is below the fixed 0.001 selection threshold;
-the selected branch remains step 0. This is startup evidence, not a claim of
-improvement on the added tasks. The new campaign `20260917T072142Z-24h` restores
-all step-8 model/Adam/Python/Torch/CUDA states exactly and retains the original
-16:00 / 18:16:10 UTC deadlines. GX10's former run stopped at step 4,380 with both
-trainer and supervisor exit 0, preserving its selected step 2,500. The fleet
-retains all previous candidates and explicitly registers the new dataset.
-
-All 86 source tests passed, including rejection of reserved-data changes and
-unregistered candidate datasets. The actual expanded candidate passed the full
-fleet eligibility path. Expanded checkpoints and transformed data were uploaded
-and verified in the existing private Hugging Face repository at 07:24:36 UTC,
-with exact source revisions, upstream notices and checksums; publication receipts
-are recorded separately.
-
-At 07:28:29 UTC the resumed campaign passed its full startup audit: every one of
-512 pilot-step-8 predictions reproduced exactly, full optimizer/RNG state matched,
-and updates 9–12 were finite under the cap. GX10 reached step 15 by 07:28:58 UTC;
-both Spark trials, the coordinator, GUI and final-publication watcher remained
-running. Final campaign evaluation is still pending.
+| Original four-family test | 2,042 | **92.90%** | 84.48% |
+| Social IQA family holdout | 768 | **72.92%** | 70.31% |
+
+Paired 95% bootstrap accuracy gains are **+8.42 pp [6.85, 9.89]** on the test and
+**+2.60 pp [0.13, 5.34]** on the holdout. The latter covers one untrained fine-tuning
+family; pretrained exposure is unknown. These are decision-scoring results, not
+general-intelligence scores or a measured comparison with hosted Jev.
+
+On the separate matched 320-decision profile, selected/base-verifier/joint-label
+accuracy was **89.06% / 80.94% / 86.25%**. Warm four-choice latency with a 768-token
+state was **3.710 / 3.177 / 0.818 seconds** on the same idle Spark in FP32. The
+selected scorer was 11–17% slower than the per-option verifier across the measured
+workloads; shared-prefix reuse and merged adapters remain future experiments.
+
+The [complete report](results/report.md) includes
+calibration, per-family results, uncertainty, all timing cells, CSV/JSON data and
+charts. The [profiling protocol](docs/research/profiling-protocol.md) explains scope;
+the [full results history](docs/operations/results-history.md) retains earlier
+smoke experiments, failures and raw evidence links. Operational provenance and
+remaining service controls are in [HANDOVER.md](HANDOVER.md).

source/docs/README.md

@@ -0,0 +1,38 @@
+# Documentation
+
+Start with the project [README](../README.md) and [completed results](../RESULTS.md).
+The [final report](../results/report.md) contains the
+accuracy and speed tables, charts, uncertainty and limitations.
+
+## Usage
+
+- [Browser playground](usage/playground.md): inputs, probabilities, model choices
+  and the recorded local service controls.
+- [Jev-compatible API harness](usage/jev-api.md): local scoring, hosted Jev calls,
+  authentication and request comparisons.
+
+## Research
+
+- [Original design brief](research/design.md): the preserved proposal and hypotheses.
+- [Model and precision notes](research/precision.md): initial research and the FP32/BF16 investigation.
+- [Training-data expansion](research/training-data.md): source pins, protected splits and verification.
+- [Accuracy and speed profiling](research/profiling-protocol.md): the final protocol and measured findings.
+- [Next experiments](research/next-steps.md): recommendations following the completed campaign.
+
+## Operations and history
+
+Before continuing work, read the root [HANDOVER](../HANDOVER.md), [PLAN](../PLAN.md)
+and [RESULTS](../RESULTS.md) entrypoints. Training is complete and remains stopped.
+
+- [Full handover](operations/handover.md): source revisions, artifacts, run paths and service controls.
+- [Dated execution plan](operations/plan.md): original and superseded campaign plans.
+- [Results history](operations/results-history.md): experiments, failures and evidence links.
+- [Fleet campaign](operations/fleet.md): machine allocation, campaign controls and prior serving-pair restoration.
+- [Fleet infrastructure scout](operations/fleet-scout.md): recorded capacity and topology observations.
+- [Hugging Face publication](operations/huggingface.md): snapshot procedures and publication records.
+
+Operational records preserve machine-specific paths and dated process details.
+Unless a command explicitly names another working directory, run it from the
+repository root. Original evidence stays under [`results/`](../results/); checkpoint
+files and pretrained weights remain outside this source repository. Historical
+source archives and published artifact checksums retain their original paths.

source/{FLEET_RUN.md → docs/operations/fleet.md}

@@ -1,5 +1,9 @@
 # Three-machine campaign
 
+This is the preserved campaign record. Training and evaluation are complete;
+the [current handover](handover.md) supersedes the dated running-state descriptions
+and launch plans below. Retain these commands for provenance and explicit recovery.
+
 **Latest, 2026-09-17 07:22 UTC:** GX10 now runs expanded-data 4B candidate
 `20260917T072142Z-24h`, from frozen source worktree
 `/home/andy/ai/opensysone/source/expanded-24b8ccf`. Supervisor/trainer PIDs at
@@ -7,11 +11,11 @@ launch are 1630617 / 1630638, OOM adjustment 0. The original GX10 campaign stopp
 cleanly at step 4,380, preserving best 2,500. Both Spark 4B runs continue, and the
 2B remains completed. The existing fleet now has **five candidates**, including
 the explicit v2 dataset override; coordinator PID 1630841 was restarted after
-the plan update. See [EXPANDED_DATA.md](EXPANDED_DATA.md) for verified evidence,
+the plan update. See [training-data.md](../research/training-data.md) for verified evidence,
 dataset provenance and exact stop/resume commands. Historical startup rows below
 describe the original fleet and are superseded by this update.
 
-**2026-09-17 continuation:** [NEXT_STEPS.md](NEXT_STEPS.md) records overnight
+**2026-09-17 continuation:** [next-steps.md](../research/next-steps.md) records overnight
 findings and the two-Spark assessment. The original 2B campaign finished with exit
 0 at step 6,000; its best is step 2,000. Spark B is now running a 4B refinement
 campaign from GX10 best step 2,500. The four-candidate fleet plan retains the
@@ -242,4 +246,4 @@ Success requires fleet `exit_code=0`, complete `evaluation/metrics.json`, matchi
 `evaluation/model.pt` hash, a successful `api_probe.json`, and `api_ready=true`.
 The deployment pointer is `/home/andy/ai/opensysone/deploy/current.json` and the
 resulting API is `http://127.0.0.1:18081/v1/systemone`. See
-[JEV_HARNESS.md](JEV_HARNESS.md); hosted Jev still needs `TYPESAFE_API_KEY`.
+[jev-api.md](../usage/jev-api.md); hosted Jev still needs `TYPESAFE_API_KEY`.

source/docs/operations/handover.md

@@ -0,0 +1,421 @@
+# Completed training and profiling campaign
+
+**Current state, 2026-09-17 09:22 UTC:** training and evaluation are complete.
+All trainers, final validation jobs and profiling jobs exited **0**. No optimizer
+updates remain active. Both Spark GPUs are idle. Keep all resumable checkpoints;
+do not resume training as part of this completed request.
+
+The frozen selected **Qwen3-4B-Instruct-2507 decision scorer** retains Spark B
+step **1,500** weights through the identical expanded-branch step **0** artifact.
+All 506 trainable tensors match exactly. The latest expanded **159**, Spark A
+**4,765** and Spark B **2,000** states were freshly validated; none passed the
+fixed promotion threshold. Five candidates were eligible; selection froze before
+reserved evaluation at **08:09:51 UTC**. The expanded step-159 result is a
+post-selection diagnostic and does not replace the winner.
+
+The [final report, tables and charts](../../results/20260917-wrapup/profile-report/report.md)
+records **92.90% vs 84.48%** base-verifier accuracy on 2,042 test decisions and
+**72.92% vs 70.31%** on 768 Social IQA holdout decisions. The 95% paired bootstrap
+accuracy differences are +8.42 pp [6.85, 9.89] and +2.60 pp [0.13, 5.34]. The
+separate 320-example comparison scores selected/base-verifier/joint-label at
+89.06% / 80.94% / 86.25%. Current selected inference is slower in every measured
+workload: long-state four-choice medians are 3.710 / 3.177 / 0.818 seconds.
+These are FP32 local warm measurements on an idle Spark, without prefix caching.
+See [profiling-protocol.md](../research/profiling-protocol.md) and [next-steps.md](../research/next-steps.md).
+
+## Completed runs and remaining services
+
+All run IDs below are relative to `/home/andy/ai/opensysone/runs` on the named host.
+
+- GX10 fleet `20260916T194403396250Z-fleet`: coordinator, evaluation and local
+  harness checks **exit 0**, completed **09:20:23 UTC**. Execution source is
+  **`07f10e791061a679b829ed1dc5b33897e001d67d`**, frozen worktree
+  `/home/andy/ai/opensysone/source/profile-07f10e7`. The original deadline remained
+  **18:16:10 UTC**. `plan.before-user-wrapup.json` preserves the original selection
+  schedule; the final cutoff was advanced to 08:07:30 UTC at the user's request.
+- The calibrated artifact is `evaluation/model.pt` in that fleet run, SHA256
+  **`e270e3da905604d97bf5a8f380ea308133403d1c4790a5c012cb1c12e9b6f348`**,
+  temperature **1.745822072**, saved before test predictions. The deploy pointer
+  is `/home/andy/ai/opensysone/deploy/current.json`.
+- Local Jev-compatible API remains on loopback **18081**, PID **1716630**,
+  source `07f10e7`, serving the calibrated model. `api_probe.json` records real
+  normalized inference; `api.log` is its log. Inspect `/health`. To stop, first
+  verify the PID command against fleet `state.json`, then send SIGTERM. Restart
+  the recorded `api_command` using the isolated environment; no training or
+  evaluation resume is needed. Serving exit status remains pending while alive.
+  Hosted Jev inference still requires credentials and has not been measured.
+- GUI remains on **7466**, default **Qwen3 4B · Selected**, wrapper **1674634**,
+  server **1674635**, runtime `20260917T081925Z-playground-selected`, launch source
+  **`00c80dd`**. Four-model real-browser checks passed. Historical snapshots remain
+  selectable. See [playground.md](../usage/playground.md) for inspect/stop/restart controls;
+  the active serving processes intentionally have no final exit status yet.
+- Spark A `20260917T081236Z-spark-a-profile`: all three inference methods,
+  **exit 0 at 09:08:39 UTC**. Spark B `20260917T081236Z-spark-b-profile`:
+  expanded checkpoint 159, **exit 0 at 08:20:35 UTC**. Profiling source `07f10e7`;
+  fixed protocol `20260917T081045Z-inference-profile-protocol`. All 2,812 predictions
+  and 360 measured timing samples passed audit. Collector and auditor also exited.
+- Final model publication watcher `20260917T023940Z-hf-final-watch` finished
+  **exit 0 at 09:20:52 UTC**. Hugging Face `andyshu/opensysone` remains private;
+  verified `FINAL_MODEL.json` pointer commit is
+  **`2082f71beb86740f36f00b82a6eeab64b9e89b61`**. Its source archive is `6729461`.
+  Supplemental profiling/checkpoint/report backup uses `PROFILE_RESULTS.json`;
+  inspect `20260917T092700Z-hf-wrapup-evidence/state.json` and `exit_code` for its
+  verified payload/pointer revisions. Pointers are published only after remote
+  integrity checks. The earlier `20260917T092300Z-hf-wrapup-evidence` attempt
+  exited 1 on a local progress-callback TypeError before upload; its receipt is
+  retained. The callback was corrected before retry. No credentials or base-model
+  weights belong in the archives.
+
+Final validation source is **`18f2b39`**. Training revisions remain **`4a60423`**
+(Sparks/original 4B) and **`24b8ccf`** (expanded GX10). Original/resumable snapshots,
+CPU proofs and archival resume commands are in `20260917T075209Z-training-wrapup`
+and `20260917T075129Z-spark-wrapup` on GX10. The former contains the final evidence
+file map, selected lineage proof, completion proof and `profile-deployment` with
+collected Spark outputs. The report run is `20260917T092045Z-final-profile-report`;
+its generator was committed at `6729461`. Small copies are committed under
+`results/20260917-wrapup`. The sections below preserve historical campaign detail;
+their active-training instructions are superseded by this completed state.
+
+**Latest continuation, 2026-09-17 07:29 UTC:** the user requested broader training
+data. [training-data.md](../research/training-data.md) records the 80,765-example seven-family
+mix, exact protected-split preservation, completed eight-step pilot and new GX10
+campaign `20260917T072142Z-24h`. It warm-starts from Spark B's selected step 1,500
+with fresh Adam, then resumes the verified pilot. GX10's original run stopped
+cleanly at step 4,380, retaining best step 2,500. Both Spark 4B runs continue;
+the completed 2B remains available. All **five candidates** are registered.
+The expanded run passed exact 512-prediction replay, full optimizer/RNG restore
+and subsequent finite updates; it reached step 15 at 07:28:58 UTC. Its data,
+pilot weights and source are uploaded and verified in the existing private
+Hugging Face repository. The GUI on 7466 and final-publication watcher remain up.
+The earlier overnight assessment is in [next-steps.md](../research/next-steps.md).
+
+The user assigned **GX10 and both Sparks** to this task, authorized stopping their
+workloads, and requested continued experimentation without permission prompts.
+SSH key authentication as `andy` works on **192.168.8.111** (spark-a / spark-d1b4)
+and **192.168.8.204** (spark-b / spark-3e2a). The former Qwen serving pair was
+stopped cleanly at 19:14 UTC; its files/cache and exact restoration commands are
+preserved in [fleet.md](fleet.md). All three hosts run independent trials.
+
+The original absolute final deadline remains **2026-09-17 18:16:10 UTC /
+19:16:10 BST**. Every training supervisor stops by **16:00 UTC / 17:00 BST**,
+leaving 2 h 16 min for selection, calibration, untouched evaluation and the local
+Jev-compatible API. Never reset that deadline on recovery. Training early stopping
+can finish sooner. Final evaluation and hosted Jev inference are still pending.
+
+Read [plan.md](plan.md), [results-history.md](results-history.md) and [fleet.md](fleet.md).
+The authoritative working source is `/home/andy/projects/opensysone` on GX10;
+there is no hosted Git remote. Do not overwrite it with an older Mac checkout.
+Operational documentation is copied to `/home/andy/ai/opensysone/gx10-reference`
+on each host. Read the relevant `docs/host.md`, `docs/training.md`, `docs/spark-a.md`,
+`docs/spark-b.md` and `docs/fleet.md` before changing machines.
+
+## Active runs and source
+
+All run IDs below are relative to `/home/andy/ai/opensysone/runs` **on that host**.
+The Spark trainers launched from clean source **`4a60423`**. The expanded GX10
+trainer uses clean source **`24b8ccf`** in the detached worktree
+`/home/andy/ai/opensysone/source/expanded-24b8ccf`; keep that worktree for its
+supervisor and recovery. The main checkout contains current documentation and
+backup/verification tools. Running trainers retain their execution revision
+and source hashes in their manifests. Inspect live state before
+using recorded PIDs. Exit statuses of active jobs remain pending.
+
+| Host | Trial | Campaign | Supervisor / trainer at launch |
+| --- | --- | --- | --- |
+| GX10 | Original 4B, stopped at 4,380; selected 2,500 | `20260916T193741Z-24h` | exited 0 / 0 |
+| GX10 | Expanded 4B, LR 0.00002, seed 433, resumed pilot step 8 | `20260917T072142Z-24h` | 1630617 / 1630638 |
+| spark-a | 4B, LR 0.00003, fresh optimizer then pilot resume | `20260916T194258Z-24h` | 327084 / 327116 |
+| spark-b | 2B completed at step 6,000; selected step 2,000 | `20260916T193803Z-24h` | exited 0 / 0 |
+| spark-b | 4B refinement, LR 0.00001, seed 432 | `20260917T023137Z-24h` | 483974 / 484001 |
+
+Fleet coordinator: **`20260916T194403396250Z-fleet` on GX10**, PID **1630841**,
+source **`24b8ccf`**, running in `waiting_for_selection` with OOM adjustment 0.
+It was stopped before the fifth candidate and its explicit dataset override were
+registered, then restarted. The old stop's exit 1 can remain in `exit_code` while
+the new coordinator runs; current process identity/state determines liveness.
+It selects the best durable candidate, then runs finalization and serves it on
+GX10. The individual campaigns are `train_only=true`;
+they cannot independently evaluate reserved data or publish competing deployments.
+
+Each campaign's `training/checkpoint.pt` holds resumable optimizer/RNG state;
+`training/best.pt` holds its validation-selected model. Saves occur every **250
+steps or 900 seconds**, independently of 512-decision validation every 500 steps.
+Patience is eight evaluations. A logged update can be newer than its checkpoint.
+Three epochs are an upper bound, not a promised completed data pass.
+
+Latest audit **2026-09-17 02:10–02:15 UTC**: GX10 step 2,570 / selected 2,500
+(93.55% accuracy, 0.188640 crossfit NLL); Spark A step 2,529 / selected 2,500
+(92.58%, 0.218012); Spark B 2B finished at 6,000 / selected 2,000 (89.84%,
+0.255294). All logged gradients/losses are finite; peak allocations are
+15.624 / 15.624 / 8.183 GiB. The 2B final correctness gate passed, worst 6.56e-7.
+Small evidence is in `results/20260917-fleet-progress/`; historical startup proofs
+remain in `results/20260916-fleet-setup/`. Reserved predictions remain untouched.
+At 02:38 UTC, GX10/A had logged steps 2,721/2,704, with selected checkpoints
+unchanged. The new Spark B campaign replayed all 512 pilot step-8 predictions
+exactly, preserved full Adam/RNG state, and resumed finite updates (step 13 in
+the fleet snapshot; startup proof covers 9–12). Its selected branch step 0 is
+still the frozen GX10 parent. Startup checks passed; final exits remain pending.
+
+## Evidence and selection
+
+The fixed selection policy is **`crossfit_temperature_nll_v1`**, four source-group-
+disjoint validation folds, seed 431. Each fold's temperature is fitted on the other
+three; macro-family NLL is scored only on held-out validation predictions. Final
+serving temperature is fitted afresh on reserved calibration after the winner is
+frozen. Raw NLL and accuracy remain separately reported. No reserved calibration,
+test or Social IQA predictions have selected a candidate.
+
+This is a documented validation-driven revision: 4B step 128 scores **89.0625%**
+accuracy / **0.318518** crossfit NLL, versus step 40's 87.5% / 0.359522. Raw NLL
+favored step 40 because step 128 was more overconfident. The accuracy difference
+alone is uncertain. Fresh step-178 validation subsequently reached **90.4297%**
+accuracy / **0.303825** crossfit NLL / 0.404198 raw NLL and became the durable
+best; its state and evidence passed the same fleet eligibility checks. See
+`results/20260916-fleet-setup/selection-diagnostic.json`;
+independent test/holdout results remain necessary.
+
+GX10's old `20260916T185910Z-24h` stopped with a complete step-128 checkpoint;
+its trainer exited **-9** during subsequent final checks after the supervisor's
+30-second grace. No optimizer progress was lost. The next campaign,
+`20260916T192239Z-24h`, restored all trainable weights, Adam and Python/torch/CUDA
+RNG exactly, then stopped gracefully at **step 178, training exit 0**. Its explicit
+`skipped_on_stop` final-check status is not a new correctness pass.
+The immutable `20260916T193721Z-selection-parent` keeps that step-178 checkpoint
+byte-for-byte and reselects the unchanged step-128 best weights under the new
+criterion. It preserves the old raw-NLL best separately. Migration proof is in
+`results/20260916-fleet-setup/selection_migration.json`. Do not restart old campaigns.
+
+Both Spark environments passed **21,368 file hashes and 55 exact distribution
+versions** against GX10. All 13 files in each pinned model were SHA-256 verified.
+Spark A reproduced all 512 original 4B pilot predictions exactly before eight
+finite updates; its pilot and fresh GPU/HTTP verification exited 0. Spark B passed
+fresh GPU/HTTP verification,
+reproduced all 512 original 2B predictions exactly, and resumed finite optimizer
+updates. Spark A's long campaign also reproduced all 512 step-8 predictions
+exactly, preserved all weights/Adam/RNG state, and resumed finite updates. Small proofs are in `results/20260916-fleet-setup/`. The revised source
+passes **37 CPU tests**, plus the updated trained-Adam reselection integration.
+These wiring and validation checks do not establish held-out generalization.
+
+The exact A step-2,500 / B step-2,000 ensemble-reference artifacts are preserved
+on GX10 in `20260917T022201Z-ensemble-reference`, outside fleet selection. The
+fixed mixed ensemble's small validation NLL advantage is uncertain; see
+[next-steps.md](../research/next-steps.md). This diagnostic is outside the current individual-model selection protocol.
+Adoption would require an explicit protocol revision and verified implementation
+before any reserved-data evaluation.
+
+## Model, data and machine bounds
+
+Pinned Apache-2.0 models are under `/home/andy/ai/models/opensysone`:
+
+- `Qwen3-4B-Instruct-2507-cdbee75f`, revision
+  `cdbee75f17c01a7cc42f958dc650907174af0554`: FP32, rank 8 / alpha 16,
+  16.518M trainable parameters, 512-token training, exact two-pass gradients.
+- `Qwen3.5-2B-15852e8c`, revision
+  `15852e8c16360a2fea060d615a32b45270f8a8fc`: FP32 text decoder, rank 16 /
+  alpha 32, 16.821M trainable parameters, 768-token training.
+
+Use `/home/andy/ai/envs/opensysone/bin/python`. GX10's isolated environment reuses
+existing torch/Transformers read-only; the Sparks have verified isolated copies.
+Shared environments are unchanged. BF16 remains blocked by measured numerical
+invariance failures. Keep the **16 GiB CUDA allocation cap**, at least **24 GiB
+MemAvailable** before loading, GPU process inspection and `oom_score_adj=0`.
+GX10's small existing router remains; Spark serving jobs remain stopped.
+GX10 has no ConnectX; memory pools are separate. No network, swap, earlyoom,
+firewall or clock configuration was changed.
+
+Frozen data: `/home/andy/ai/opensysone/data/public-decisions-v1-20260916`.
+Source-group-disjoint SNLI, BoolQ, ARC and four-choice Banking77; Social IQA is
+an untrained task-family holdout. Pins/licences/hashes are in
+`results/public-decisions-v1-manifest.json`. The 4B retains 40,915 train / 512
+validation / 510 calibration / 2,042 test / 768 holdout; 2B retains 40,937 / 512 /
+512 / 2,047 / 768. Validation IDs are identical. Exact deduplication does not
+exclude semantic duplicates or pretraining contamination. No customer data.
+
+## Inspect, stop and recover
+
+One read-only command checks every registered candidate concurrently, including
+completed candidates, with exact process identity and no model loading:
+
+```bash
+python3 scripts/fleet_status.py
+python3 scripts/fleet_status.py --json
+```
+
+Use the exact active host/run from the table, or the fleet controls in
+[fleet.md](fleet.md). From the project directory on the relevant host:
+
+```bash
+~/ai/envs/opensysone/bin/python scripts/campaign_status.py \
+  --campaign /home/andy/ai/opensysone/runs/20260916T193741Z-24h
+tail -n 5 /home/andy/ai/opensysone/runs/20260916T193741Z-24h/training/training.jsonl
+```
+
+Add `--stop` for a command-verified TERM to the recorded supervisor, orphan child
+or API. Wait for exit and lock release before restarting. Training checkpoints
+at a safe boundary. Do not start a second model on an occupied host. Resume a
+stopped candidate into a fresh campaign on its host:
+
+```bash
+~/ai/envs/opensysone/bin/python scripts/launch_24h.py \
+  --pilot /absolute/old/campaign/training --train-only \
+  --training-deadline 2026-09-17T16:00:00Z \
+  --deadline 2026-09-17T18:16:10Z --inference-max-tokens 1024 \
+  --selection-metric crossfit_temperature_nll_v1
+```
+
+Preserve model/data/seed/rank/alpha/learning rates/batches/token limits/schedule/
+epochs/two-pass configuration. The launcher restores them from the checkpoint.
+Use only trusted project checkpoints. **If a candidate path changes, stop the
+waiting fleet coordinator, update that candidate in its own `plan.json`, and
+resume it.** Editing a plan while the coordinator is running does not reload it.
+After `selection.json` exists, the winner is frozen; recovery must not reselect
+after test access. Stopping the waiting coordinator does not stop the independently supervised
+independently bounded training jobs; stop each campaign explicitly when needed.
+
+## Finalization and Jev harness
+
+The coordinator reconstructs the selected model, fits a scalar temperature on
+reserved calibration, checkpoints `evaluation/model.pt`, then evaluates untouched
+test/holdout against the unchanged pretrained scorer with separately fitted base
+temperature and source-group uncertainty. It verifies direct inference and a real
+HTTP request before publishing `/home/andy/ai/opensysone/deploy/current.json`.
+Success requires fleet `exit_code=0`, complete `evaluation/metrics.json`, and
+`state.json` with `api_ready=true`. Training completion alone is insufficient.
+The resulting API is **http://127.0.0.1:18081/v1/systemone**, inference limit 1,024;
+its PID/command remain recorded after the coordinator exits.
+
+[jev-api.md](../usage/jev-api.md) documents local, hosted and comparison modes,
+optional bearer authentication and Mac SSH tunneling. **`TYPESAFE_API_KEY` is
+not configured**, so authenticated hosted Jev inference has not been tested.
+Local confidence is normalized entropy, not established correctness calibration.
+This produces a general-language decision scorer, not a new general-purpose chat
+model. Frozen-head/generation controls, new-model prefix caching and the broader
+latency matrix remain open.
+
+## Completed runs and history
+
+All paths below are under `/home/andy/ai/opensysone/runs/`.
+
+| Run | Execution source | Exit / result |
+| --- | --- | --- |
+| `20260916T182256Z-train` | `4b25eec` | 1, chat-template return-type setup error before optimizer training; preserved |
+| `20260916T182352Z-train` | `f1c9322` | 0, 2B public-data 40-step pilot |
+| `20260916T183240Z-train` | `980d881` | 0, exact 512-prediction restart and step 41 |
+| `20260916T183751Z-verify2b` | script SHA in manifest | 0, restored-optimizer longest-input gradients and real authenticated HTTP |
+| `20260916T183823Z-train` | `ccbbe6d` | 0, selected 4B 40-step pilot |
+| `20260916T185718Z-verify4b` | script SHA in manifest | 0, 4B reload, optimizer-memory, long-context and 255-choice HTTP checks |
+| `20260916T155124Z` | `34a993e` | 0, original 0.5B synthetic FP32 60-step smoke |
+| `20260916T155314Z` | `91019bc` | 0, exact 72-prediction restart and step 61 |
+| `20260916T154714Z` | `4d6cb0f` | 1, BF16 probability-invariance failure; checkpoint preserved |
+| `20260916T161253Z-precision` | staged hashes later `b9dd165` | 0, diagnostic completed; BF16 fails |
+| `20260916T161355Z-precision` | `409ade4` | 0, expanded diagnosis; all BF16 variants fail |
+
+Small raw results and checkpoint hashes are retained under `results/<run-id>`;
+weights stay under `~/ai`. Original 0.5B smoke and verified prefix caching are
+unchanged in `smoke_train.py`/`decision_model.py`. FP32 passed the original expanded
+precision gate at worst 0.00002138; BF16 remains blocked. Synthetic results prove
+wiring, not task generalization. New-model prefix caching, frozen-head/generation
+controls and the larger latency matrix remain open. Fleet connectivity details
+are in [fleet-scout.md](fleet-scout.md), including verified numeric SSH addresses. The current fleet allocation supersedes
+its earlier serving-occupancy snapshot.
+
+Hugging Face backup and final-publication controls: [huggingface.md](huggingface.md).
+
+
+## Hugging Face publication watcher
+
+Backup destination: [andyshu/opensysone](https://huggingface.co/andyshu/opensysone),
+private, existing license metadata retained. The initial read-only credential
+failed with HTTP 403; its sanitized report remains in
+`results/20260917-fleet-progress/hf-initial-artifacts-publication.json`.
+At 02:52 UTC the user-supplied replacement was verified as account `andyshu`,
+role `write`, and saved to the existing local Hugging Face login store. Token
+values are excluded from source, logs and backups. **The initial snapshot upload
+completed and was verified at 02:54 UTC**, exit 0, including source and all four
+checkpoint pairs. See `hf-write-auth-verified.json` and
+`hf-snapshot-publication.json`. The first verified HF pointer commit is
+`b213728f9acc5e009bc96704db341913d582be4c`; remote `CURRENT_SNAPSHOT.json` records
+the authoritative payload/source revisions, including later documentation
+refreshes. Local publication state is in
+`~/ai/opensysone/runs/20260917T025300Z-hf-snapshot-publish`; immutable backup
+staging remains under `~/ai/opensysone/exports`.
+
+An independent final-publication watcher runs on GX10: PID **1427060**, source
+**`35d6d8f`**, OOM adjustment 0, status `waiting_for_completion` at launch. Its
+status directory is `/home/andy/ai/opensysone/runs/20260917T023940Z-hf-final-watch`;
+the adjacent `.log` file records process output. Exit status remains pending.
+Inspect `state.json` and `exit_code`; match the exact `state.json.command` against
+`/proc/1427060/cmdline` before stopping only that watcher with SIGTERM. Restart
+with the command in [huggingface.md](huggingface.md) and a new output directory.
+The watcher publishes the frozen final model only after completed evaluation and
+verified deployment, then checks the remote payload before updating
+`FINAL_MODEL.json`. Its own deadline is 18:46:10 UTC; this does not extend training
+or the original model deadline. See the launch proof for the exact command/hash.
+
+The previous watcher (PID 1426447) was deliberately stopped, exit 1, and replaced
+with the process above to remove inherited `HF_TOKEN`/`HUGGING_FACE_HUB_TOKEN`
+overrides. It will read the updated write-capable stored login when final publication
+begins. Changing credentials does not require
+changing the training jobs, fleet plan, API or repository visibility.
+
+
+## Interactive model playground — 2026-09-17
+
+The user requested a GUI for text plus candidate answers and probabilities. It is
+running on GX10 at **http://127.0.0.1:7466**, PID **1469393**, backend source **`2d0ff79`**,
+OOM adjustment 0. From the Mac, run `ssh -N -L 7466:127.0.0.1:7466 gx10`, then
+open **http://localhost:7466**. See [playground.md](../usage/playground.md).
+
+Runtime: `/home/andy/ai/opensysone/runs/20260917T034059Z-playground-port7466`, also recorded
+in `LAST_PLAYGROUND`. `launch.json` records the exact process command/source
+hashes and `server.log` receives sanitized diagnostics. Exit status is pending
+while serving. Inspect `/api/status` and match `/proc/1469393/cmdline` against
+`launch.json.command` before sending SIGTERM to this process only. The documented
+CLI restarts it from the fixed catalog after the old listener has stopped.
+
+Three immutable snapshots are available: GX10 4B step 2,500, Spark A 4B step 2,500,
+and Spark B 2B step 2,000. Their files/hashes and matching provenance are under
+the original snapshot runtime, referenced by this runtime's `models.json`; these probabilities are explicitly uncalibrated. No
+reserved evaluation examples were used for GUI testing. One backend resides at
+a time; loads, scoring and unloading are serialized on a dedicated worker thread.
+The 16 GiB allocation cap and memory/OOM checks remain active. This extra GUI
+process shares GPU compute with training, so requests can slow optimizer steps.
+Port 18081 remains reserved for final deployment; no firewall/services changed.
+
+All seven backend tests passed. Chromium passed real inference for all three
+models, return switching, clipboard JSON, input-edit staleness, duplicate options,
+actual tokenizer overflow and mobile layout. Browser script/CSP errors: none.
+Cold/switch example requests were 5.08–9.10 seconds; a warm main-model request
+was 1.53 seconds. These are individual wiring timings, not latency percentiles
+or quality estimates. Real results/screenshots and concurrency observations are
+in `results/20260917-playground/`; the separate frontend fixture report is labeled
+as stubbed UI testing. Training and the fleet/final-publication controllers remain
+independent of this GUI.
+
+A 45-second observation after GUI verification recorded the GX10 trainer advancing
+from step 3,063 to 3,068 with finite losses/gradients, a 15.624 GiB allocation peak
+and 81.3 GiB host memory available. The GUI stayed ready. This confirms continued
+training during GUI operation; it does not establish zero slowdown or capture all
+model-switch transients.
+
+At the user's request, the playground moved from port 18082 to **7466**. The
+previous process received verified SIGTERM and exited; its wait status could not
+be collected by the replacement launcher. `stop.json` in the old runtime records
+that observation. The new process starts without a resident model and loads one
+on the next scoring request. The page, scripts, styles, model catalog and status
+respond on 7466; the old listener is closed. Evidence: `results/20260917-playground/port-7466.json`.
+
+The frontend now fits the viewport, with Context/Choices/Results tabs on compact
+screens and internally scrolling text/results. A fixed action bar and result-copy
+footer stay accessible. Very short portrait layouts compact optional content to
+retain readable inputs when a keyboard reduces the viewport. Browser fixture
+checks pass 13 sizes, including 320×568, 844×390 and 390×360; they check visible
+controls, readable input lines, loading, validation, keyboard tabs, resizing,
+long result lists, stale results, clipboard and recovery. Fixture probabilities
+are not new model evidence. See `results/20260917-playground-layout/`.
+
+The backend process and model snapshots continue unchanged. Static files are
+served directly from `web/` with no-store caching, so refresh the browser to use
+the layout. `frontend-current.json` in the active runtime records the current
+frontend commit and served-file hashes independently of the backend launch
+revision. Training source and processes were not modified for this relayout.

source/{HUGGINGFACE.md → docs/operations/huggingface.md}

@@ -1,4 +1,29 @@
-# Hugging Face backup
+# Hugging Face publication and backup
+
+## Publication layout
+
+The completed release has a reader-facing layout: `model/` for the calibrated
+artifact and reconstruction notes, `results/` for the report and charts, `docs/`
+for reproduction instructions, and `source/` for the current committed project.
+`archive/README.md` indexes the existing versioned checkpoint and evidence trees.
+The original `FINAL_MODEL.json`, `PROFILE_RESULTS.json`, `CURRENT_SNAPSHOT.json`
+and their historical payload paths remain unchanged. `PUBLICATION.json` records
+the verified presentation manifest, payload commit and current source revision.
+
+The explicit publication file map and before/after verification live in the run
+recorded by `~/ai/opensysone/runs/LAST_PUBLICATION_CLEANUP`. The dedicated
+`scripts/publish_publication.py` publishes that reviewed map, checks every remote
+file and preserved historical entry, then updates the landing README and index.
+It does not change repository visibility or model-card license metadata.
+
+The timestamped records below describe earlier backups; the three original
+pointers remain the source of exact artifact identities.
+
+## Historical backup record
+
+The [current handover](handover.md) records completed final-model publication.
+The dated snapshot and watcher procedures below preserve publication history;
+their training-stage descriptions do not describe a still-running campaign.
 
 The user requested backups in [andyshu/opensysone](https://huggingface.co/andyshu/opensysone).
 The existing model repository is private; retain its visibility and `license: unknown`
@@ -84,7 +109,7 @@ The watcher stops by **2026-09-17 18:46:10 UTC**, 30 minutes after the original
 delivery deadline. Uploads have three bounded attempts. Training still stops by
 16:00 UTC and the final model's deadline remains 18:16:10 UTC.
 
-The current watcher path and process are recorded in `HANDOVER.md`. Inspect its
+The current watcher path and process are recorded in [handover.md](handover.md). Inspect its
 `state.json`, `exit_code` and adjacent log. Before stopping, verify `/proc/<pid>/cmdline`
 against `state.json.command`, then send SIGTERM to that exact watcher only. Resume
 with a fresh output directory; an existing immutable export is hash-checked and reused:

source/docs/operations/plan.md

@@ -0,0 +1,289 @@
+# OpenSysOne: single-node start and GX10 handover
+
+## Training wrap-up and profiling completed — 2026-09-17 09:20 UTC
+
+Training stopped at the user's request, all latest checkpoints received fresh
+validation, and the five-candidate selection froze before held-out inference.
+The retained 4B weights are Spark B step 1,500 (identical expanded branch 0).
+Full calibration/test/holdout evaluation, local API inference checks, all matched
+accuracy/speed profiling and independent audits completed with exit 0. Both Sparks
+are idle. The selected calibrated model is in the port-7466 GUI and local API18081.
+
+[Final report](../../results/20260917-wrapup/profile-report/report.md): test accuracy
+92.90% vs 84.48% pretrained verifier; Social IQA 72.92% vs 70.31%. The selected
+scorer is slower than both pretrained inference paths in this measured FP32
+implementation. See [profiling-protocol.md](../research/profiling-protocol.md) for scope and
+[next-steps.md](../research/next-steps.md) for future experiments. Do not resume optimizer
+updates. Current services, checkpoints and verified publication controls are in
+[handover.md](handover.md). All continuation plans below are historical.
+
+## Training-data expansion — 2026-09-17 07:22 UTC
+
+The user requested expansion using best judgment. Add pinned official TRAIN
+rows from HellaSwag, PIQA and CommonsenseQA while retaining every original
+training example and preserving all four reserved splits byte-for-byte. The
+filtered mix has 80,765 decisions, approximately half replay and half additions.
+Keep 383 additional new-source diagnostics outside training and the fixed
+selection protocol. See [training-data.md](../research/training-data.md) for provenance,
+verification, current controls and the limits of the unchanged selection set.
+
+Replace the plateaued GX10 run, preserving its resumable step 4,380 and selected
+step 2,500. Initialize a new 4B candidate from frozen Spark B step 1,500 with fresh
+Adam, seed 433 and LR 2e-5. The eight-step pilot passed; campaign
+`20260917T072142Z-24h` resumes it from clean frozen source `24b8ccf`. Both Spark
+4B runs continue. The fifth fleet candidate explicitly registers v2 data;
+protected bytes and identical validation identities remain eligibility gates.
+Keep the original 16 GiB cap and absolute 16:00 / 18:16:10 UTC deadlines.
+
+## Continuation decision — 2026-09-17
+
+See [next-steps.md](../research/next-steps.md) for the overnight findings and the assessment
+of work shared by the two Sparks. GX10 and Spark A continue improving 4B trials.
+Spark B's 2B run exited 0 at step 6,000 after validation early stopping; preserve
+its step-2,000 selected artifact. Use the freed GPU for a fourth candidate from
+GX10's selected step-2,500 4B weights, with fresh Adam, seed 432 and LR 1e-5.
+Keep the fixed selection policy, 16 GiB cap and original absolute deadlines.
+
+The two Sparks have active ConnectX/RoCE and installed NCCL, but distributed
+training has no measured correctness or throughput result. Do not interrupt the
+improving trials to replace their trainer during this delivery window. Next joint
+experiment: bounded communication and synchronized-gradient parity, followed by
+25–100 representative updates at equal global batch and measured memory. Parallel
+scoring replicas are a simpler later use; preserve the tested GX10 finalizer now.
+
+## Active 24-hour campaign — 2026-09-16
+
+The user now authorizes all three machines for this task, including stopping
+existing workloads. GX10 continues the main run; the Sparks run independent
+lower-learning-rate 4B and longer-running 2B candidates. See
+[fleet.md](fleet.md) for the active fleet plan and process controls.
+The existing 16 GiB allocation cap applies to each training process. Select the
+candidate using the same 512 validation decisions before final calibration/test.
+The frozen selection criterion is now four-fold source-group-disjoint temperature
+crossfit macro-family NLL, seed 431, policy `crossfit_temperature_nll_v1`. Fit each
+fold's scalar temperature on the other three; fit serving temperature afresh on
+reserved calibration after selection. Raw NLL/accuracy remain separately reported.
+This validation-driven revision preserves the stronger step-128 classifier that
+raw NLL discarded because of overconfidence; see the diagnostic in the [results history](results-history.md).
+The absolute delivery deadline is **2026-09-17 18:16:10 UTC (19:16:10 BST)**,
+24 hours from this request. Reserve at least the final two hours for fresh reconstruction,
+calibration, untouched evaluation and loopback API deployment. Earlier sections
+below preserve the smoke plan; their 1.5B and leave-idle boundary is superseded.
+The runner increases that reserve from measured pilot validation time when needed,
+including both pretrained/tuned passes, 30% margin and ten minutes for setup.
+
+Start from a pinned posttrained model, retain its pretrained yes-minus-no
+readout, and train ordinary FP32 low-rank decoder adapters plus scalar head.
+BF16 remains blocked by the measured correctness gate. Compare Qwen3.5-2B
+with Qwen3-4B-Instruct-2507, using the fixed validation crossfit criterion, memory and
+throughput. The 4B pilot uses rank 8 and an exact two-pass categorical gradient
+to keep one candidate graph live. The reliable 2B fallback uses rank 16.
+Keep the 16 GiB CUDA cap and launch free-memory/OOM hardening unchanged.
+**Initial single-node selection:** the 4B rank-8 candidate, with 87.50% validation
+accuracy and
+0.395661 macro NLL versus the 2B pilot's 82.81% / 0.498153. It beats 2B in
+every measured validation family. Training limit is 512 complete-chat tokens;
+separately verified inference limit is 1,024. Longest-input training stress peaks
+at 15.624 GiB, and real HTTP reload/limits/255-choice checks pass.
+
+Frozen source-group-disjoint public data covers SNLI, BoolQ, ARC and four-choice
+Banking77 routing. Social IQA is a completely untrained task-family holdout.
+Source pins, licences, raw hashes and split audit are in
+`results/public-decisions-v1-manifest.json`; data is under `~/ai/opensysone/data/`.
+Model-specific length filtering is reported, with no silent truncation.
+Validation chooses checkpoints. Calibration fits only one global temperature;
+untouched test/holdout evaluation happens in the separate finalization process.
+Compare the trained scorer with its unchanged pretrained readout, both raw and
+separately temperature-calibrated, with source-group bootstrap uncertainty.
+
+Before launch, prove reconstruction, a subsequent optimizer step, longest-input
+gradients with restored optimizer state and authenticated HTTP inference using
+the real checkpoint. Then detach `scripts/launch_24h.py`, preserving optimizer,
+RNG, source/data/model provenance, checkpoint cadence independent of evaluation,
+individual child PIDs and an absolute deadline. Select the best validation
+checkpoint rather than assuming more updates improve intelligence.
+
+The standard-library harness supports local inference, Jev HTTP calls and
+response/timing comparison; see [jev-api.md](../usage/jev-api.md). Hosted calls
+require `TYPESAFE_API_KEY`; no key is available in the current process environment.
+Deploy only on loopback and use the existing SSH tunnel for Mac access.
+This trains a general-language **decision scorer**, not a new general-purpose
+chat model or a demonstrated substitute for Jev. Generalization and calibration
+remain evaluation outcomes. Generation baselines, frozen-head controls, shared
+prefix caching for these new models and the original latency matrix remain open.
+
+Consolidated **2026-09-16**. This is the active execution plan. The original
+proposal is preserved verbatim in [design.md](../research/design.md).
+Start a continuation with [handover.md](handover.md), then read this file.
+
+Build a decision scorer from a pretrained causal Transformer: arbitrary state,
+question and natural-language candidate go in; one scalar score comes out.
+Normalize mutually exclusive choices to a distribution. No generated answer or
+fixed label vocabulary. TypeSafe/Jev architecture claims remain hypotheses;
+softmax alone does not establish calibration.
+
+## Resources available now
+
+| Host | Installed unified RAM | Available at 16:36 BST | Current use | Project role |
+| --- | ---: | ---: | --- | --- |
+| GX10 | 121.6 GiB | 118.7 GiB | Idle router; no substantial loaded model | Development, tiny training/evaluation |
+| spark-a | 121.7 GiB | 28.0 GiB | Qwen3.8-Flash-Next Q8_0 head | Existing serving workload |
+| spark-b | 121.7 GiB | 22.4 GiB | Same model's RPC worker | Existing serving workload |
+
+These are snapshots, not reservations. CPU, GPU, cache and OS share each pool.
+The table above records the earlier smoke snapshot. The user subsequently
+assigned all three GB10s to OpenSysOne; at 19:14 UTC the Spark serving pair was
+stopped and both GPUs were empty, with about 118 GiB available on each host.
+These remain three separate memory pools. Recheck `free -b` and GPU processes
+before every run.
+
+The Sparks have one physical ConnectX port-0 cable, with two PCIe-domain paths:
+`192.168.100.10/11` and `192.168.101.10/11`. Both were verified active; earlier
+fleet tests measured 108.9 Gb/s RDMA per domain, 188 Gb/s aggregate. llama.cpp
+RPC works; **PyTorch/NCCL training is unverified**. See [fleet-scout.md](fleet-scout.md).
+
+GX10 uses ordinary Ethernet/Wi-Fi/tailnet, without connected ConnectX.
+Additional connectivity is expected around **2026-09-18**, per the user; this is
+an estimate. GX10 can coordinate jobs over SSH today, but should not join the
+Sparks' collective over a slow network. Even after cabling, verify topology,
+transport and collective correctness before revising capacity. A two-node DAC
+does not specify the future three-node topology or create coherent pooled RAM.
+
+## Immediate experiment and handover boundary
+
+Finish a bounded smoke on GX10 and leave it free for the next session.
+
+- Base: `Qwen/Qwen2.5-0.5B`, revision
+  `060db6499f32faf8b98477b0a26969ef7d8b9987`, Apache-2.0, dense causal decoder.
+- Method: FP32 backbone, final two layers trainable, FP32 scalar head,
+  categorical cross-entropy. This is partial fine-tuning, not LoRA.
+- Data: invented inventory facts, three questions per state, shuffled candidate
+  text; 192 train / 48 calibration / 72 test decisions. Disjoint entity groups,
+  same task templates. No customer data.
+- Bounds: 60 steps, four decisions/batch, short sequences, 16 GiB CUDA cap,
+  24 GiB available-memory launch gate, 25-minute timeout, checkpoints every ten
+  steps and before evaluation. No long unattended run needed for this phase.
+- Stack: existing `~/ai/envs/comfy/bin/python`, torch 2.11.0+cu130,
+  Transformers 5.15.0, SDPA; no shared-environment package changes.
+- Compare base yes-minus-no token logits, initial uniform scalar head, trained
+  scalar and separate-calibration-split global temperature. Uniform output is
+  an optimization sanity baseline, not a competitive classifier.
+- Time the same trained checkpoint and token IDs: full batched forwards versus
+  cached branching; about 128/1,024 state tokens, 1/4/16 questions, two choices,
+  eight branches/chunk. Save actual lengths and raw warm repetitions, prefill,
+  branch and end-to-end times. This is not yet the complete generation comparison.
+
+Completion gates: finite gradients/loss, changed backbone/head weights, checkpoint
+and optimizer/RNG state, reload/resume verification, strict FP32 tiny-model cache
+tests and measured BF16 parity/permutation/isolation. Record peak allocated and
+reserved CUDA memory plus host availability. Save before evaluation can fail.
+Leave source, model, artifacts, commands, hashes and process state on GX10.
+
+Synthetic improvements demonstrate optimization and wiring only. They cannot
+establish calibration, zero-shot ability, useful judgment or superiority over
+prompt-and-generate classification.
+
+**Precision gate found during the smoke:** BF16 changes probabilities by up to
+0.099 when batch composition changes, including uncached forwards. Forcing
+SDPA MATH does not fix it. Casting the same weights to FP32 reduces discrepancies
+to about 0.000014 across the tested comparisons. Use FP32 for the reference;
+BF16 requires an explicit correctness investigation before larger experiments.
+Preserve the failed run and diagnostic; do not relax tolerances to accept it.
+
+**Expanded gate, 2026-09-16:** all 24 synthetic groups fail BF16 even with
+strict accumulation, math SDPA, FP32 decoder linears, or their combination.
+Worst probability differences are 0.147–0.239; the same weights cast to FP32
+stay below 0.000022. First-layer traces expose shape-dependent projection
+differences, but correcting those alone does not fix the decoder. See
+`results/20260916T161355Z-precision/precision.json`. Use FP32 for the next public
+data/1.5B experiment; further BF16 work should target remaining operations rather
+than repeat these unsuccessful switches.
+
+## Next working session: first useful 1.5B experiment
+
+Budget the next one or two hours for a real data cut and a proven resumed run.
+
+1. Read smoke results and traces. Fix correctness before interpreting speed.
+   Preserve the 0.5B run as a reference and verify fresh checkpoint reconstruction.
+2. Pin `Qwen2.5-1.5B` base. Start at 128–1,024 state tokens; increase to 4k after
+   measuring memory. BF16 weights are about 2.9 GiB; training processes every
+   candidate branch. Record exact config rather than assuming context limits.
+3. Select public sentiment, entailment and intent/routing sources, checking each
+   licence/version first. Preserve source splits, deduplicate/group before
+   transformations, and reserve an entire further task family plus unseen
+   question/label paraphrases for zero-shot evaluation. Freeze test data early.
+4. Compare token scoring, frozen-backbone trained head and tuned scalar on the
+   same data. Start with FP32/SDPA; restore BF16 only after the precision gate.
+   Add LoRA in an isolated pinned PEFT
+   environment if useful; preserve the shared Comfy environment. Defer QLoRA,
+   FP8 and custom kernels.
+5. Count all processed branch tokens/padding and measure elapsed step time. Set
+   dataset size and deadline from those observations. Keep evaluation batches
+   small and checkpoint on a cadence independent of evaluation.
+
+## Following 48 hours: prove utility on one node
+
+Start with at least 1,000 untouched test decisions across multiple public
+datasets; increase until proper-score uncertainty is informative. Report
+per-family counts, accuracy, NLL, multiclass Brier (class sum), declared-bin
+top-label ECE, reliability and accuracy-versus-coverage. Fit one temperature
+on a separate calibration set. Evaluate once on test and held-out family.
+Bootstrap source groups, not augmented rows; use ECE alongside proper scores.
+
+Add generation and constrained-output baselines using the same base and inputs.
+Document prompt, output-token budget, parse/failure policy and timing scope.
+Distinguish model-load, first-call, warmed and application end-to-end latency.
+
+Extend one axis at a time: 1/4/16/64 questions; 2/4/16 choices; 128/1k/4k states.
+Add 255 choices as one stress point after bounded chunking is proven. Do not
+run the original full Cartesian product yet. Estimate tail latency with enough
+repetitions before reporting p95. Account for KV copies, padding and transfers.
+Recheck permutations, mixed lengths and unrelated-question perturbations.
+
+**Scale only after:** repeatable useful accuracy and improved NLL/Brier on at
+least one untouched task family, no unexplained severe regression elsewhere,
+and meaningful measured multi-question latency/throughput improvement over a
+fair baseline. If only familiar label words improve, fix data/objective first.
+The original <150/<250/<500 ms targets are exploratory, not commitments.
+
+## Later hardware and architecture decisions
+
+Schedule a service transition before large Spark training; verify actual memory
+release. spark-a's active swap and missing earlyoom must be addressed before
+sustained training. Operational changes belong in the relevant GX10 docs.
+
+Before DDP: test CUDA/NCCL all-reduce numerical correctness, transport logs,
+both directions and realistic message sizes, then a short two-rank optimizer
+run with checkpoint/resume. Compare useful examples/second with one node and
+two independent runs. DDP replicates state; it does not combine memory.
+FSDP is a separate decision, justified by measured memory needs.
+
+The 3B class remains the target after the 1.5B gate. Qwen2.5-3B has a separate
+research licence; select it deliberately or choose another base if deployment
+requires different terms. A 7B/8B run follows useful scaling evidence. Keep
+inference local. Primary model/cache links are in [precision.md](../research/precision.md).
+
+Stay with architecture A (shared-prefix decoder) until profiling identifies its
+cost. Every suffix still runs all layers and attends to the state. Batching
+does not guarantee constant latency. A 1.5B 4k prefix is about 112 MiB KV;
+256 physical copies are about 28 GiB before suffixes, weights and workspace.
+
+Test architecture B (state encoder plus shallow cross-attention decoder) if
+branch work/copies dominate and quality passes. Compare at equal data budget.
+Packed branching needs numerical independence tests; custom kernels need a
+profiled bottleneck. Soft teacher targets, proper-score losses and quantization
+calibration ablations follow a reliable baseline. RL is unnecessary initially.
+
+## Evidence and artifacts
+
+- [handover.md](handover.md): exact continuation commands and run state.
+- [results-history.md](results-history.md): measured outcomes and limitations.
+- [fleet-scout.md](fleet-scout.md): live survey and prior bandwidth evidence.
+- [precision.md](../research/precision.md): primary sources and memory arithmetic.
+- `results/<run-id>/`: small raw config/data/prediction/correctness/timing files.
+- GX10 `~/ai/opensysone/runs/<run-id>/`: complete run including checkpoint.
+- GX10 `~/ai/models/opensysone/`: pinned pretrained weights.
+
+Record source commit/hashes, model/data revisions, config, exact software and
+hardware for every run. No API service is needed for this phase; any future
+HTTP listener follows the existing loopback/tailnet policy.

source/docs/operations/results-history.md

@@ -0,0 +1,592 @@
+# OpenSysOne results
+
+## Completed 4B accuracy and speed profile — 2026-09-17
+
+Training and profiling are finished. The selected model is Qwen3-4B-Instruct-2507
+with rank-8 LoRA and a learned scalar decision head: Spark B step-1,500 weights,
+retained unchanged at expanded branch step 0. Selection used validation only.
+The [complete report](../../results/20260917-wrapup/profile-report/report.md) includes
+per-family results, all timing cells, machine/source/checkpoint provenance,
+CSV/JSON data and standalone charts.
+
+| Reserved evaluation | Decisions | Selected accuracy | Pretrained verifier accuracy | Selected calibrated NLL | Base calibrated NLL |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| Original four-family test | 2,042 | **92.90%** | 84.48% | 0.2051 | 0.4527 |
+| Social IQA family holdout | 768 | **72.92%** | 70.31% | 0.6783 | 0.7425 |
+
+Paired, source-group-stratified 95% bootstrap intervals (400 resamples) put the
+accuracy gains at **+8.42 pp [6.85, 9.89]** and **+2.60 pp [0.13, 5.34]**. The
+holdout improvement is modest; this is one task family. Social IQA was excluded
+from our fine-tuning, but exposure in the pretrained base model is unknown.
+The test contains four-choice Banking77, BoolQ, ARC and SNLI; this is not a
+general-intelligence score or a comparison with the hosted Jev service.
+
+Temperature 1.745822 was fitted on 510 separate calibration decisions. Selected
+ECE changes from 4.27% to 1.08% on the test and 15.55% to 8.30% on Social IQA;
+Brier changes from 0.1172 to 0.1098 and 0.4187 to 0.3797 respectively. Calibration
+helps these evaluations but does not establish reliability on arbitrary inputs.
+
+| Matched profile method | Accuracy, same 320 decisions | Warm median, 128-state-token / four-choice | Warm median, 768-state-token / four-choice |
+| --- | ---: | ---: | ---: |
+| Selected scorer | **89.06%** | 0.902 s | 3.710 s |
+| Pretrained per-option verifier | 80.94% | 0.806 s | 3.177 s |
+| Pretrained joint answer-label method | 86.25% | **0.213 s** | **0.818 s** |
+
+These warm local measurements use the same otherwise idle Spark in FP32, one
+question per request, and include tokenization/probability construction. They
+exclude loading, HTTP, generated explanations and concurrent serving. The
+joint-label method conditions on all options together. The current scorer is
+**11–17% slower** than the per-option base and **2.18–15.58 times slower** than the
+joint-label baseline across all 12 workload cells. Ten repetitions per cell make
+p95 exploratory. A scalar head alone has not made this implementation faster;
+shared-prefix caching and merged adapters remain future measured experiments.
+
+The expanded step-159 checkpoint scores 312/383 (81.46%) expansion diagnostics
+versus selected 303/383 (79.11%), while losing one answer on the matched 320.
+This post-selection comparison is descriptive and does not change the winner.
+All final/resumable states remain preserved. Training, final validation,
+full evaluation and profiling exited 0. Final correctness differences were at
+most 2.65e-7 against a 1e-4 tolerance. Four-model GUI browser checks passed.
+Source/control details are in [handover.md](handover.md); independent profile
+proofs are in [profile-audit](../../results/20260917-wrapup/profile-audit).
+
+## Historical smoke results — 2026-09-16
+
+The 0.5B model trains, its artifact reconstructs, and FP32 shared-prefix scoring
+passes correctness checks. **BF16 failed batch invariance on this checkpoint and
+stack.** The result supports continuing the experiment; it does not establish a
+useful zero-shot decision model or calibrated deployment probabilities.
+
+## Reference experiment
+
+Completed run: `20260916T155124Z`, source commit `34a993e`, exit **0**.
+Full artifacts: GX10 `/home/andy/ai/opensysone/runs/20260916T155124Z/`.
+Small artifacts: [results/20260916T155124Z](../../results/20260916T155124Z).
+
+Pinned pretrained `Qwen/Qwen2.5-0.5B` revision
+`060db6499f32faf8b98477b0a26969ef7d8b9987`: 494,033,665 total parameters including
+the scalar head, **29,825,665 trainable** (final two layers and head). FP32 weights,
+AdamW state and inference, SDPA; no LM next-token training loss or generated answers.
+Backbone learning rate 2e-5, head 1e-3, gradient clipping 1, 60 steps, four decisions
+per batch. The rest of the pretrained backbone is frozen.
+
+Invented inventory facts supply 192 train, 48 calibration and 72 test decisions.
+Each group shares one state across color, seal and quantity questions; candidate
+orders are shuffled. Entity IDs are disjoint, but templates and underlying fact
+combinations overlap. These are simple wiring/optimization examples, **not a
+semantic holdout or a real task-family generalization benchmark**.
+
+| Same FP32 run; 72 test decisions | Accuracy | NLL | Brier, class sum | Top-label ECE, 10 bins |
+| --- | ---: | ---: | ---: | ---: |
+| Base yes-minus-no token score | 66.7% | 1.090 | 0.574 | 0.290 |
+| Initial zero scalar head | 27.8% | 1.059 | 0.639 | 0.083 |
+| Trained scalar | 68.1% | 0.628 | 0.419 | 0.205 |
+| Trained + calibration-split temperature | 68.1% | 0.568 | 0.370 | 0.141 |
+
+The trained model gets **one more example** correct than the matched token
+baseline. This is not evidence of an accuracy gain. NLL/Brier improve on this
+tiny synthetic set; the temperature (1.88365) was selected using only the separate
+48-example calibration split. There is no basis for a general calibration claim.
+The uniform head's low ECE despite poor accuracy illustrates why ECE alone is
+not the selection criterion. NLL uses stable log-softmax, without probability clipping.
+
+The optimization loop including periodic saves took **8.80 seconds**; median step
+was 105 ms. This is partial tuning on very short inputs and is not a full-model
+training throughput estimate. Maximum allocated CUDA memory over training, eval
+and the timing grid was **3.43 GiB**, reserved **3.65 GiB**, against a 16 GiB cap.
+The query-projection probe changed by max 0.000964; scalar weight norm became
+0.2667. Full parameter and artifact provenance is in `manifest.json`.
+
+## Shared-prefix correctness and timings
+
+The tiny random FP32 CPU model passes four tests, including mixed lengths,
+chunk sizes 1/2/4/16, candidate permutation, unrelated-question perturbation,
+gradient flow and equivalence of selected token logits to full vocabulary logits.
+
+On the trained GPU model, probability maximum absolute differences were:
+
+| Comparison | Difference |
+| --- | ---: |
+| Full forward vs shared prefix | 0.00000304 |
+| Question batch vs isolated question | 0.00000381 |
+| Candidate permutation, restored order | 0.00000131 |
+| Repeated prefix call | 0 |
+| Reset all trainable tensors, reload checkpoint | 0 |
+
+The first successful run recorded the original 0.02 tolerance. Its actual errors
+are below 0.000004. The continuation harness tightens FP32 tolerance to **0.0001**;
+BF16 retains the original gate so its known failure remains visible.
+
+Illustrative end-to-end warm medians, including tokenization, cache copies and
+device synchronization. One warm-up plus **three measured repeats** per cell;
+these are not p95 or production claims. Same trained checkpoint/serialized token
+IDs in both modes, two candidates/question, maximum eight branches per chunk.
+The full reference is already batched fairly (four two-choice questions at once).
+
+| Actual state-prefix tokens | Questions | Full batched forwards | Shared prefix | Speedup |
+| --- | ---: | ---: | ---: | ---: |
+| 143 | 1 | 35.9 ms | 47.2 ms | 0.76× |
+| 143 | 4 | 117.3 ms | 50.6 ms | 2.32× |
+| 143 | 16 | 464.5 ms | 129.2 ms | 3.60× |
+| 1,031 | 1 | 312.0 ms | 184.6 ms | 1.69× |
+| 1,031 | 4 | 1,262.6 ms | 201.6 ms | 6.26× |
+| 1,031 | 16 | 5,033.1 ms | 336.1 ms | 14.97× |
+
+Caching loses on the shortest one-question case. At 1,031 tokens/16 questions,
+the shared run spends about 156 ms in prefill and 178 ms in branches; single-prefix
+KV occupies 24.2 MiB before the per-chunk copies. That longer-context point
+demonstrates amortization in this implementation. The repeated short question is
+a workload timing probe, not a semantic multi-question benchmark. No generation,
+constrained decoding, service throughput or 1.5B/3B latency comparison has run.
+
+## Failed BF16 experiment and diagnosis
+
+Run `20260916T154714Z`, source `4d6cb0f`, completed its 60 training steps but
+exited **1** at the correctness gate. The checkpoint and all earlier predictions
+remain available; no performance conclusion was taken from that failed run.
+
+The same trained BF16 weights were evaluated with different precision/backends:
+
+| Comparison | BF16 probability difference | Same weights cast to FP32 |
+| --- | ---: | ---: |
+| Full vs shared | 0.08544 | 0.00000727 |
+| Shared vs isolated | 0.09897 | 0.00000519 |
+| Shared candidate permutation | 0.07889 | 0.00000137 |
+| Batched full vs separate full calls | 0.05262 | 0.00001433 |
+
+SDPA MATH retains the BF16 failure and passes in FP32. This demonstrates precision
+and batch-shape sensitivity beyond cache handling; it does **not** isolate the
+root cause to a specific kernel or prove every GB10/model fails in BF16. The
+BF16 token baseline had different metrics from FP32 and must not be mixed into
+the matched FP32 comparison above. BF16 AdamW also lacks FP32 master weights in
+this simple implementation, making small updates prone to rounding.
+
+Raw evidence: [parity_diagnosis.json](../../results/20260916T154714Z/parity_diagnosis.json).
+Reproducer: `scripts/diagnose_parity.py --run <failed-run-directory>`.
+Keep the FP32 reference; investigate BF16 explicitly before scaling.
+
+## Expanded precision investigation — 2026-09-16
+
+Completed read-only runs `20260916T161253Z-precision` and
+`20260916T161355Z-precision`, both exit **0**. The second run used clean source
+commit **`409ade4`**; the first manifest records `94a24e8` with staged additions,
+whose script hashes correspond to `b9dd165`. Full artifacts are under GX10
+`/home/andy/ai/opensysone/runs/<run-id>/artifacts/`; small copies are in
+[results/20260916T161355Z-precision](../../results/20260916T161355Z-precision).
+
+All ablations reconstruct the preserved BF16-trained checkpoint from
+`20260916T154714Z`; its SHA-256 remained
+`106efdfb0794e6ca870b7add11c71f06c58281ef46b348305866a85f1e6f6bc8`.
+The base, data and checkpoint are unchanged. This comparison concerns arithmetic
+on the same weights, rather than FP32 versus BF16 training quality. It does not
+evaluate a new task or supply generalization evidence.
+
+The expanded test covers **all 24 groups / 72 decisions**, comparing batched
+full calls with separate question calls, full with cached, cache chunks of 4/16,
+cached with isolated questions, and restored candidate permutations. The table
+shows the worst absolute probability difference across these comparisons.
+Strict reduction sets `allow_bf16_reduced_precision_reduction=False`. FP32 linear
+casts each decoder linear's inputs and weights to FP32, then casts its output
+back to BF16; it is an inference diagnostic, not a validated training method.
+
+| Arithmetic configuration | Worst probability difference | Groups above BF16's original 0.02 gate |
+| --- | ---: | ---: |
+| BF16 default SDPA | 0.238608 | 24/24 |
+| BF16, strict reduction | 0.213011 | 24/24 |
+| BF16, math SDPA + strict reduction | 0.168008 | 24/24 |
+| BF16, FP32 linear + strict reduction | 0.147468 | 24/24 |
+| BF16, math SDPA + FP32 linear + strict reduction | 0.183657 | 24/24 |
+| Same weights cast to FP32, default SDPA | 0.00002138 | 0/24 |
+
+FP32 also passes the stricter **0.0001** gate. Repeated full and repeated cached
+calls have exactly zero probability difference in every group/configuration.
+Full candidate permutations also match exactly; cached permutations can change
+which branches share a chunk and still fail in BF16. Exit 0 means the diagnostic
+completed, not that BF16 passed.
+
+Final-candidate-token traces for the first serialized group narrow the issue:
+embeddings and first input normalization match exactly, but default BF16's first
+query/key projections differ by up to **0.5** between batched/separate calls.
+Strict reduction removes those initial projection differences in this trace;
+later differences remain. Combined math attention and FP32 linears reduce the
+first decoder-layer difference from 0.02344 to 0.00003052, yet the final normalized
+hidden representation still differs by up to 2.0. This supports shape-dependent
+numerical differences that propagate through the decoder. It does not isolate
+every contributing operation or establish a particular kernel defect. The trace
+samples final candidate tokens, not every token's intermediate representation.
+
+Peak CUDA allocation was **1.90 GiB**, reserved **1.94 GiB**, against the 16 GiB
+cap. The shared environment was unchanged and OOM score adjustment was 0.
+All four CPU correctness tests passed before execution. Both diagnostic PIDs
+exited; at 16:15 UTC GX10 again had about 118 GiB available and only the original
+router GPU process. Continue useful model/data work in FP32; none of these BF16
+interventions justifies reopening its correctness gate.
+
+## Public-data 2B adapter pilot — 2026-09-16
+
+Run `/home/andy/ai/opensysone/runs/20260916T182352Z-train/artifacts`,
+execution source **`f1c9322`**, exited **0** after **40 optimizer steps**
+(160 decisions), not three completed epochs. The model is pinned
+`Qwen/Qwen3.5-2B` at `15852e8c16360a2fea060d615a32b45270f8a8fc`.
+Only its text decoder is retained; the unused vision encoder is discarded before
+CUDA loading. Rank-16 additive linear adapters and a pretrained yes-minus-no
+initialized head train **16,821,249 of 1,898,646,337 parameters** in FP32.
+
+The frozen data has 40,941 source-group-disjoint train decisions, 512 validation,
+512 calibration, 2,048 source test and 768 completely held-out Social IQA decisions.
+This model's 768-token complete-chat limit excludes four BoolQ train rows and one
+test row, leaving 40,937/512/512/2,047/768. Banking77 is a four-choice target-plus-
+three-negative transformation, not a full 77-way benchmark. Source-group splitting
+does not rule out pretraining contamination or semantic duplicates.
+
+| Family | Initial validation accuracy | Step 40 accuracy |
+| --- | ---: | ---: |
+| ARC | 74.22% | 81.25% |
+| Banking77 four-choice | 83.59% | 88.28% |
+| BoolQ | 64.84% | 79.69% |
+| SNLI | 64.06% | 82.03% |
+| All 512 decisions | **71.68%** | **82.81%** |
+
+Validation macro-family NLL fell from **0.700136 to 0.498153**. This is validation
+selection evidence, not untouched test improvement. No calibration, test or
+Social IQA predictions have been evaluated in this pilot. Median four-decision
+step was **3.869 s**; the loop including final validation took 298.1 s.
+Peak CUDA allocation/reservation was **7.746/7.855 GiB**, below the 16 GiB cap.
+All final permutation/chunk/isolation checks passed the 0.0001 probability gate,
+with worst difference **0.00000614**. The old repeat label also changed chunk
+shape; the current source restores the original chunk size before repeat testing.
+
+A fresh process in `20260916T183240Z-train`, source **`980d881`**, reconstructed
+step 40 and reproduced **all 512 raw logits and probabilities exactly**, restored
+optimizer/RNG, then completed step 41 with finite gradient norm 3.676.
+It exited **0** and all final parity gates passed, worst difference 0.00000316.
+Step 41 validation macro NLL was 0.494478. The retained setup failure
+`20260916T182256Z-train` exited 1 before any optimizer step because Transformers'
+new chat-template return default was a BatchEncoding; explicit `return_dict=False`
+fixed it without changing the shared environment.
+
+Small raw pilot evidence is in [results/20260916T182352Z-train](../../results/20260916T182352Z-train).
+Checkpoint SHA-256 is
+`af5790ae2f2b56477ebbdf6ab9c418d895e48d2bf5a6416e11b6e9863ad1db55`;
+validation-selected best SHA-256 is
+`82b4261feb98d3ed56291e4c03304a65da20ce0194a6ad117d113b30d152282e`.
+New dependencies are isolated in `~/ai/envs/opensysone` (pyarrow 25.0.1), with
+read-only reuse of the existing torch/Transformers packages. The Jev-compatible
+stdlib harness and 12 CPU tests pass; real-checkpoint HTTP and longest-input
+stress are the next gate before the larger campaign.
+
+## Public-data 4B pilot selected for the 24-hour run
+
+Run `/home/andy/ai/opensysone/runs/20260916T183823Z-train/artifacts`, clean execution
+source **`ccbbe6d`**, exited **0** after 40 steps / 160 decisions. The base is
+`Qwen/Qwen3-4B-Instruct-2507`, pinned to
+`cdbee75f17c01a7cc42f958dc650907174af0554`, Apache-2.0.
+Rank-8 adapters (alpha 16) and the pretrained initialized head train
+**16,517,633 of 4,038,985,729 parameters** in FP32. Exact two-pass categorical
+gradients keep one candidate graph live; CPU gradients match ordinary CE within
+0.000001. Gradient checkpointing is enabled. No quantization or new kernels.
+
+| Family | Initial validation accuracy | Step 40 accuracy | Step 40 NLL |
+| --- | ---: | ---: | ---: |
+| ARC | 90.63% | 90.63% | 0.374400 |
+| Banking77 four-choice | 90.63% | 91.41% | 0.229941 |
+| BoolQ | 82.03% | 84.38% | 0.583093 |
+| SNLI | 82.81% | 83.59% | 0.395211 |
+| All 512 validation decisions | **86.52%** | **87.50%** | **0.395661** |
+
+Raw validation macro NLL improves from **1.436162 to 0.395661**; the initial
+readout was severely overconfident. A separately recorded diagnostic fits and
+scores a temperature on the same validation rows (NLL 0.407737, T 6.9183): it is
+optimistic validation analysis, not independent calibration. Reserved calibration,
+test and Social IQA predictions remain unevaluated. The trained 4B validation
+accuracy and NLL beat the 2B pilot in every family, supporting the larger candidate
+despite its lower throughput. This does not prove task generalization.
+
+The 512-token complete-chat limit retains **40,915 train / 512 validation /
+510 calibration / 2,042 test / 768 Social IQA** decisions; it drops 26 train,
+two calibration and six test BoolQ rows, with no silent truncation.
+Median four-decision step is **8.956 s**; 55,268 actual branch tokens were
+processed with no padding overhead. The loop including final validation takes
+724.0 s. Initial validation alone takes 327.85 s. Peak CUDA allocated/reserved
+is **15.510/15.604 GiB** against the 16 GiB cap. OOM adjustment is 0 and about
+99 GiB unified RAM remains available with the model loaded.
+Final correctness passes all 0.0001 gates, worst probability difference
+**0.00000167**, with exact repeated, isolated and restored-permutation predictions.
+
+Checkpoint SHA-256:
+`e26f75b2396de88311873fac4eb91e1e40d0ec940778ec99f282bcfd96a2e258`.
+Best SHA-256:
+`64977ee0b1a6147c6faf59283edea9adf564dd36d53f4580bc20940b94c6764f`.
+Small raw evidence is in [results/20260916T183823Z-train](../../results/20260916T183823Z-train).
+Fresh reload, longest-input gradients with restored optimizer state, 1,024-token
+HTTP inference, and 255-choice HTTP stress **all passed** (verification exit 0).
+Reload matches all 16 checked validation predictions exactly. Longest training
+input is 509 tokens and peaks at 15.624 GiB with optimizer state; inference peaks
+at 15.465 GiB. The long HTTP request has 1,023 tokens in each of two candidate
+branches and matches direct inference exactly. Invalid-key/oversized-input
+requests return 401/422. One warm three-question request takes 1.571 s, and one
+255-choice request takes 47.042 s; these are wiring stress timings, not latency
+percentiles or intelligence benchmarks. The checkpoint SHA-256 is unchanged.
+Evidence: [results/20260916T185718Z-verify4b](../../results/20260916T185718Z-verify4b).
+All **15 CPU tests pass**, including unequal-source-group bootstrap weighting
+and the measured evaluation-reserve calculation. The live Jev HTTPS endpoint
+returns 405 to an unauthenticated GET; no credentials or state were sent and no
+authenticated hosted inference has been tested.
+
+## Detached 24-hour campaign now running
+
+Launched **2026-09-16 18:59:10 UTC** from clean source **`0109eb6`** into
+`/home/andy/ai/opensysone/runs/20260916T185910Z-24h`. Supervisor PID is **1085496**,
+current trainer **1085517**; both OOM score adjustments are 0. Training resumes the
+4B step-40 checkpoint with optimizer/RNG restored, preserves validation-selected
+best and all model/data/config signatures, and has passed the initial FP32
+correctness gates. Exit is **pending**; the API has not started yet.
+
+Fresh restart reproduces **all 512 raw logits and probabilities exactly**;
+the reference and fresh prediction JSON SHA-256 are both
+`e671e1508185765552b0f933ba03f356be62143c531d8ef534457d34b1645c9b`.
+The next four updates, **41–44**, have finite losses/gradients and remain under
+the cap. Step 41 takes 8.724 s, loss 0.115940, gradient norm 3.81358.
+This proves reconstruction plus subsequent optimizer updates, not a bitwise
+interrupted-versus-uninterrupted trajectory comparison. Raw verification is in
+the launch evidence directory below. The durable checkpoint remains step 40
+until the regular save cadence, independently of those logged newer updates.
+
+Training ends by **2026-09-17 16:16:10 UTC**, reserving two hours until the final
+**18:16:10 UTC / 19:16:10 BST** deadline. The reserve estimates 6,640 base/tuned
+prediction rows at 4,251.8 seconds from measured pilot validation speed, adds
+30% plus ten minutes for setup, and keeps a two-hour minimum. Checkpoints save
+every 250 steps or 900 seconds regardless of evaluation; validation is every
+500 steps with patience eight. The three-epoch target is an upper bound.
+
+After successful training, the runner loads the best artifact fresh, calibrates
+only on the 510 reserved known-family decisions, saves a deployable checkpoint
+before untouched evaluation, records raw/calibrated test and Social IQA metrics
+against the unchanged pretrained scorer, and starts the loopback API only after
+complete evaluation and a real-model inference check. Deployment is planned at
+`http://127.0.0.1:18081`, with 1,024-token inputs. No hosted Jev call runs
+automatically. Small launch evidence lives in
+[results/20260916T185910Z-24h-launch](../../results/20260916T185910Z-24h-launch), separate
+from the completion-results directory reserved by the runner.
+
+Current inspection, stop and same-deadline recovery commands are in
+[handover.md](handover.md). A running job is not a finalized model or successful
+test result. The frozen-family controls and independent calibration remain the
+quality gates for final reporting. The complete 15-test suite passed; the new
+orphan-child stop safeguard also passes an integration test that refuses to
+terminate a PID when its command line differs from the recorded command.
+
+## Three-machine expansion — 2026-09-16 evening
+
+The user assigned GX10 and both Sparks to this task and authorized terminating
+their workloads. The Spark serving head and RPC worker were stopped in order
+with verified SIGTERM; both released their GPU allocations and each had about
+118 GiB available afterward. Their weights/cache and exact restoration commands
+are retained. No network or system configuration changed.
+
+Both Sparks now have isolated copies of the exact GX10 training dependencies:
+21,368 installed file hashes and 55 package versions match. CPU autograd and both
+Qwen-family imports pass. This initial check verified the environments. Subsequently all pinned model
+files and real GPU training/HTTP checks passed; see the launch results below.
+
+The original GX10 campaign saved step 128 before a requested stop. Its trainer
+exceeded the 30-second grace while performing final correctness checks and exited
+-9; the complete step-128 checkpoint and optimizer/RNG are verified intact. The
+new source records skipped final checks explicitly on a requested stop. It also
+retains step-specific prediction evidence before publishing each new best artifact
+and selects a restored checkpoint if its fresh validation improves the best.
+
+The intermediate GX10 campaign `20260916T192239Z-24h`, source `6e080e2`, restored
+step 128 and later stopped gracefully at step 178 with training exit 0.
+The Spark alternatives are a 4B weights-only warm initialization with fresh Adam,
+learning rate 0.00003 and 7,500-step cosine horizon, and a longer 2B continuation.
+The planned fleet cutoff is 2026-09-17 16:00 UTC, leaving 2 h 16 min until the
+original final deadline. All training remains under 16 GiB per process.
+
+All **32 initial fleet CPU tests passed**, including weights-only initialization, optimizer/RNG
+resume, requested-stop evidence, deadline handling, exact validation-set matching,
+checkpoint/metric mismatch rejection and API deployment lifecycle. The coordinator
+recomputes its criterion from all 512 saved validation predictions and freezes
+selection before calibration/test/holdout. Read-only compatibility checks of the
+real 4B and 2B pilot artifacts pass, reproducing NLL 0.395661 and 0.498153.
+Small setup proofs are in [results/20260916-fleet-setup](../../results/20260916-fleet-setup).
+Live paths, statuses and recovery instructions are in [fleet.md](fleet.md).
+
+The subsequent selection revision uses the frozen four-fold source-group-disjoint
+temperature-crossfit policy `crossfit_temperature_nll_v1` (seed 431, 101 positive
+temperatures, family-balanced fitting and scoring). Step 128's validation accuracy
+is **89.0625%**, versus step 40's 87.5%; raw NLL is 0.442683 versus 0.395661.
+Crossfit NLL reverses that ranking: **0.318518 versus 0.359522**, improving in all
+four families. A 5,000-replicate paired source-group bootstrap, refitting the
+temperatures, gives difference -0.041005 with 95% interval [-0.079822, -0.001152].
+The accuracy gain alone is uncertain (29 gains, 21 losses; McNemar p=0.322).
+This supports accounting for recoverable overconfidence during checkpoint
+selection. It is a validation-driven criterion revision, not independent test
+evidence. No reserved predictions were read. Original raw-selected checkpoints
+remain preserved, and final calibration still uses the separate reserved split.
+All **37 tests pass** after adding policy/selection checks; the updated CPU
+integration also proves reselection leaves trained weights and Adam steps intact.
+Raw diagnostic: [selection-diagnostic.json](../../results/20260916-fleet-setup/selection-diagnostic.json).
+
+## Active fleet launch — 2026-09-16 19:44 UTC
+
+Three training-only campaigns are active on source **`4a60423`**:
+GX10 `20260916T193741Z-24h` (4B, LR 0.0001), spark-a
+`20260916T194258Z-24h` (4B, LR 0.00003, 7,500-step cosine horizon), and spark-b
+`20260916T193803Z-24h` (2B, LR 0.0001). Each uses the fixed crossfit criterion,
+16 GiB allocation cap and 2026-09-17 16:00 UTC cutoff. Training exit statuses
+remain pending. The fleet coordinator `20260916T194403396250Z-fleet`, source
+**`6a7b0ed`**, is detached on GX10 and waiting for selection; no reserved-data
+predictions or final calibration have run. The cutoff shutdown race is covered
+by a regression test, and all nine fleet tests pass after that fix.
+
+GX10 restored step 178's weights, Adam and Python/torch/CUDA RNG exactly. Its
+fresh 512-decision validation reached **90.4297% accuracy, 0.303825 crossfit NLL,
+0.404198 raw NLL**, promoting the durable best beyond step 128. Fresh FP32
+correctness passes (worst probability difference 4.77e-7), and resumed updates
+are finite. These are validation results, not independent test evidence.
+
+Spark A reproduced all 512 original 4B pilot predictions exactly before eight
+finite lower-rate updates (median 8.086 seconds, peak 15.505 GiB). That pilot
+exited 0; its step-8 accuracy 86.914% / raw NLL 0.405397 did not improve the
+starting checkpoint. The long-run crossfit selector re-evaluates both inherited
+best and current checkpoint. Real fresh-artifact verification exited 0: exact
+16-decision reload, finite restored-Adam gradients on the longest 509-token
+input, 15.624 GiB peak, 1,023-token HTTP/direct match, expected 401/422 errors,
+and 255 choices in 43.31 seconds. The long campaign reproduced all 512 step-8
+raw predictions exactly, with identical weights/Adam/RNG. Its fixed crossfit
+criterion selected step 8 at 0.358235 NLL, and new updates are finite. No
+independent generalization improvement is claimed for the short pilot.
+
+Spark B's preparation exited 0. Fresh verification passed exact reload,
+restored-Adam gradients at 700 tokens (8.123 GiB peak), 1,024-token inference,
+authentication/length errors and 255 choices in 17.73 seconds. The long campaign
+reproduced all 512 original validation predictions exactly, scoring 82.8125%
+accuracy / 0.476072 crossfit NLL / 0.498153 raw NLL before resumed training.
+Subsequent finite updates reached step 98 by 19:43:59 UTC. Timing observations
+are individual wiring checks, not p50/p95 latency measurements.
+
+Full small evidence, source revisions, frozen plan and startup state snapshots
+are under [results/20260916-fleet-setup](../../results/20260916-fleet-setup). Live
+state, inspection/stop/resume and serving-pair restoration are in
+[fleet.md](fleet.md). Final calibrated test/holdout metrics and selected-model
+API deployment are pending; authenticated hosted Jev inference still requires
+`TYPESAFE_API_KEY`.
+
+## Overnight progress and next experiment — 2026-09-17
+
+The 02:10–02:15 UTC audit found both 4B jobs healthy and improving, while the 2B
+campaign completed cleanly at **01:59:29 UTC**, training and supervisor exit **0**.
+All recorded losses/gradients were finite. Peak allocation was 15.624 GiB on each
+4B job and 8.183 GiB on the 2B job; the 16 GiB cap remains unchanged.
+
+| Candidate | Last audited step | Selected step | Crossfit validation NLL | Selected accuracy |
+| --- | ---: | ---: | ---: | ---: |
+| GX10 4B, LR 1e-4 | 2,570 | 2,500 | **0.188640** | **93.55%** |
+| Spark A 4B, LR 3e-5 | 2,529 | 2,500 | 0.218012 | 92.58% |
+| Spark B 2B, LR 1e-4 | 6,000 | 2,000 | 0.255294 | 89.84% |
+
+These are the same 512 validation decisions, selected with the unchanged fixed
+crossfit policy. No reserved calibration, test or Social IQA predictions have
+been read. A higher maximum accuracy at a different step does not override the
+selection criterion. Spark A improved at all five scheduled validations. Spark B
+stopped after eight evaluations without a new best; its final step-6,000 score
+was 0.351593 / 86.91%. Final numerical correctness passed at worst 6.56e-7.
+The selected step-2,000 and resumable step-6,000 artifacts are preserved.
+
+The freed Spark B is training a **fourth candidate**, initialized from a frozen
+copy of GX10's step-2,500 selected weights (SHA-256
+`8956eb6c0cfbb02124aeefd99c3b418c55f55fdb9a64260350622d98dbba1aec`).
+Fresh Adam, seed 432, LR/head LR 1e-5 and a 5,000-step cosine horizon define a new
+trajectory. Other model/batch/token/correctness settings and both absolute
+deadlines stay unchanged. The eight-step pilot started at **02:16:05 UTC**;
+source `4a60423`. The pinned 4B model copied from Spark A over the existing link
+passed all 13 file hashes. Warm initialization preserves all 506 trainable tensors
+exactly and deliberately starts with an empty optimizer. All 512 initial raw
+predictions match the parent exactly. The eight-step pilot and fresh verifier
+exited 0: exact 16-decision reload, finite longest-input gradients, 15.623 GiB
+peak, direct/HTTP agreement at 1,023 tokens, expected 401/422 and 255 choices
+in 45.44 seconds. These timings are individual wiring checks, not percentiles.
+Campaign `20260917T023137Z-24h` launched at 02:31:37 UTC, restoring the complete
+step-8 optimizer/RNG state exactly, and was added as the fourth fleet candidate.
+Its inherited selected branch step 0 retains the parent score: step 8 scored
+0.188576, a change below the fixed 0.001 improvement threshold. The short pilot
+does not establish a quality gain.
+
+[Small audit evidence](../../results/20260917-fleet-progress) records the 22 scheduled
+validation points, live processes, source revisions, selected-checkpoint hashes
+and frozen refinement parent. [next-steps.md](../research/next-steps.md) records the decisions
+and the two-Spark alternatives: independent candidates now, bounded distributed
+adapter-gradient training or parallel scoring next. Active ConnectX/RoCE and
+installed NCCL do not establish collective correctness or useful speedup. The
+current two-pass trainer needs explicit synchronization changes, and its measured
+peak leaves only about 385 MiB for additional GPU allocations under the cap.
+
+## Fixed validation ensemble diagnostic — 2026-09-17
+
+Saved, identity-matched validation logits were combined with fixed equal weights
+and the unchanged crossfit-temperature policy; no weights were tuned and no
+reserved predictions were accessed. GX10 4B + Spark A 4B scores **93.16% /
+0.193983 NLL**, worse than GX10 alone (**93.55% / 0.188640**). Spark A 4B + the
+completed Spark B 2B scores **93.55% / 0.180560**. This more diverse pair shares
+19 errors versus 29 for the two-4B pair, but gains eight/losses eight versus GX10.
+The mixed pair's NLL difference versus GX10 is -0.008080; a 1,000-replicate paired
+source-group bootstrap with fold-temperature refitting yields 95% interval
+**[-0.039064, +0.019451]**. No gain over the best single model is established.
+These are exploratory validation results from already selected checkpoints,
+not independent generalization evidence. The deployed-candidate protocol remains
+individual models; ensemble inference and latency have not been implemented or
+measured. The exact A step-2,500 and B step-2,000 artifacts are frozen on GX10 in
+`20260917T022201Z-ensemble-reference`, with 134.3 MB copied, stable source hashes
+and CPU reconstruction/provenance checks. No weights are in Git.
+[Analysis and provenance](../../results/20260917-fleet-progress/fixed-ensemble-validation.json).
+
+## Remaining gates
+
+The active continuation state and checkpoint-resume verification are recorded in
+[handover.md](handover.md). Public multi-family training and the frozen unseen-family
+holdout are now implemented; independent calibration/test/holdout metrics await
+the 24-hour campaign's finalization. Frozen-head and generation controls, new-model
+prefix caching and the larger latency matrix remain open. The Sparks now host
+independent candidate experiments; GX10 does not need a ConnectX cable for this
+selection strategy. Architecture B and
+distributed training still await quality and profiling evidence in [plan.md](plan.md).
+
+## Expanded public training data — 2026-09-17
+
+Version 2 retains all 40,915 original 4B-compatible training decisions and adds
+16,000 HellaSwag, 14,360 PIQA and 9,490 CommonsenseQA decisions: **80,765 total**.
+All four reserved source files and tokenized sequences match version 1 exactly.
+The 383 retained new-source diagnostics stay outside training and checkpoint
+selection. An independent reconstruction audit checked every added source label
+and shuffled answer position, all downloaded hashes and diagnostic exclusions.
+See [training-data.md](../research/training-data.md) and its linked small evidence.
+
+Clean source `24b8ccf`, pilot `20260917T070758Z-train`: eight finite updates,
+**exit 0**, all initial/final FP32 gates passed. Frozen Spark B parent step 1,500
+reproduces every initial validation logit and probability exactly. Captured step
+0 has empty Adam; every final Adam counter is eight. Median update 8.90 seconds,
+peak allocation including checks 15.426 GiB, worst final probability discrepancy
+3.58e-7. The 32 sampled decisions cover all seven task families.
+
+Step 8 scores 0.170108 validation crossfit NLL versus parent 0.170150, both
+94.7266% accuracy. The difference is below the fixed 0.001 selection threshold;
+the selected branch remains step 0. This is startup evidence, not a claim of
+improvement on the added tasks. The new campaign `20260917T072142Z-24h` restores
+all step-8 model/Adam/Python/Torch/CUDA states exactly and retains the original
+16:00 / 18:16:10 UTC deadlines. GX10's former run stopped at step 4,380 with both
+trainer and supervisor exit 0, preserving its selected step 2,500. The fleet
+retains all previous candidates and explicitly registers the new dataset.
+
+All 86 source tests passed, including rejection of reserved-data changes and
+unregistered candidate datasets. The actual expanded candidate passed the full
+fleet eligibility path. Expanded checkpoints and transformed data were uploaded
+and verified in the existing private Hugging Face repository at 07:24:36 UTC,
+with exact source revisions, upstream notices and checksums; publication receipts
+are recorded separately.
+
+At 07:28:29 UTC the resumed campaign passed its full startup audit: every one of
+512 pilot-step-8 predictions reproduced exactly, full optimizer/RNG state matched,
+and updates 9–12 were finite under the cap. GX10 reached step 15 by 07:28:58 UTC;
+both Spark trials, the coordinator, GUI and final-publication watcher remained
+running. Final campaign evaluation is still pending.

source/docs/publication/archive.md

@@ -0,0 +1,25 @@
+# Archives and provenance
+
+The current release is easy to browse in [model/](../model/),
+[results/](../results/) and [docs/](../docs/README.md). This index groups the original
+experiment records, which remain at their existing versioned paths so saved
+links and integrity manifests continue to work.
+
+| Record | Entry point |
+| --- | --- |
+| Selected calibrated model and full evaluation | [FINAL_MODEL.json](../FINAL_MODEL.json) · [final/](../final/) |
+| Training wrap-up, nine checkpoint artifacts, matched profiles and raw predictions | [PROFILE_RESULTS.json](../PROFILE_RESULTS.json) · [profiles/](../profiles/) |
+| Earlier training and expanded-data snapshots | [CURRENT_SNAPSHOT.json](../CURRENT_SNAPSHOT.json) · [snapshots/](../snapshots/) |
+| Snapshot publication manifests | [publications/](../publications/) |
+| Original source revisions | [sources/](../sources/) |
+| Source snapshot for this publication layout | [publication-manifest.json](../publication-manifest.json) |
+| Complete inventory immediately before this cleanup | [inventory-before.json](inventory-before.json) |
+
+`CURRENT_SNAPSHOT.json` describes a historical training snapshot. Use
+`FINAL_MODEL.json` for the calibrated model and `PUBLICATION.json` for the
+verified publication layout. Each original pointer records its immutable payload
+commit and manifest checksum; use that revision when checking historical files.
+
+No historical checkpoint, prediction or timing file was moved or rewritten.
+The browsable [source/](../source/) tree reflects the current committed source;
+versioned source archives preserve the execution revisions of the experiments.

source/docs/publication/model.md

@@ -0,0 +1,72 @@
+# Calibrated 4B model
+
+[model.pt](model.pt) is the completed OpenSysOne decision-scoring artifact. It
+stores learned additive adapters, a scalar head, reconstruction metadata and a
+global temperature. Pretrained backbone weights are required separately.
+
+| Property | Recorded value |
+| --- | --- |
+| Format | `opensysone-adapter-v1`, custom PyTorch checkpoint |
+| Base | `Qwen/Qwen3-4B-Instruct-2507` |
+| Base revision | `cdbee75f17c01a7cc42f958dc650907174af0554` |
+| Precision | FP32 |
+| Adapters | Rank 8, alpha 16; 16,517,633 trainable adapter/head parameters |
+| Selected weights | Spark B refinement step 1,500, retained at expanded branch step 0 |
+| Temperature | `1.7458220720291138`, fitted on 510 separate calibration decisions |
+| Verified inference limit | 1,024 complete formatted candidate tokens; no silent truncation |
+
+The calibrated artifact SHA-256 is:
+
+```text
+e270e3da905604d97bf5a8f380ea308133403d1c4790a5c012cb1c12e9b6f348
+```
+
+The original release remains at
+[`final/20260916T194403396250Z-fleet/model.pt`](../final/20260916T194403396250Z-fleet/model.pt),
+with [its immutable manifest](../final/20260916T194403396250Z-fleet/backup_manifest.json).
+[FINAL_MODEL.json](../FINAL_MODEL.json) records the exact payload commit, model hash,
+manifest hash and publication source. The `model/model.pt` front copy has identical
+bytes; moving the presentation does not change the artifact.
+
+## Source and lineage
+
+The selected checkpoint records training source
+`24b8ccf60d388f9cbb184e03a6ae260a1f5a8b86`; its warm-start parent's source was
+`4a60423c39d70f8d50472ce4f4f7fa4a4bd9fce1`. Final evaluation used
+`07f10e791061a679b829ed1dc5b33897e001d67d`.
+The final [evaluation manifest](../final/20260916T194403396250Z-fleet/evaluation/manifest.json)
+records source-file hashes, model pin, dataset signature, configuration and packages.
+
+The selected branch step is zero because it retains the already-trained parent's
+weights. CPU lineage checks confirmed all 506 trainable tensors equal the parent,
+and that calibration leaves them unchanged. Later expanded-data checkpoint 159
+was evaluated but not promoted. Its diagnostic results do not describe a different
+deployed model.
+
+## Reconstruction constraint
+
+The existing loader in [experiment.py](../source/experiment.py) reads the base path
+from checkpoint metadata. For this release that path is:
+
+```text
+/home/andy/ai/models/opensysone/Qwen3-4B-Instruct-2507-cdbee75f
+```
+
+That directory must contain the pinned local base, tokenizer and matching
+`opensysone-provenance.json`. The loader uses local files only and verifies base,
+prompt and adapter versions. The checkpoint itself may be downloaded elsewhere
+and supplied through `--checkpoint`; relocating it does not relocate the saved
+base path. The current CLI has no base-path override. Do not rewrite and re-save
+the published checkpoint to disguise that constraint: doing so changes its hash.
+
+Use the project's [reproduction guide](../docs/reproduce.md) and
+[Jev-compatible harness](../source/docs/usage/jev-api.md). This is not a drop-in
+Transformers or standard PEFT package, and its serialization is intended for
+trusted, hash-verified project artifacts. The final artifact excludes optimizer
+state; resumable checkpoints and sibling validation evidence are preserved in
+the [historical bundles](../archive/README.md).
+
+Probabilities are normalized over the supplied choices. Temperature calibration
+does not change the chosen answer. It was fitted on known task families; Social
+IQa holdout ECE remains 8.30%, so calibration on arbitrary tasks is unproven.
+See the [full measured results](../results/report.md).

source/docs/publication/overview.md

@@ -0,0 +1,49 @@
+# Publication guide
+
+OpenSysOne is an independent decision-scoring experiment inspired by
+[Jev](https://typesafe.ai/) and the TypeSafe team. The current release is the
+completed, calibrated Qwen3 4B scorer evaluated on 17 September 2026.
+
+The published repository has a short entry path:
+
+| Directory | Contents |
+| --- | --- |
+| [model/](../model/README.md) | Calibrated checkpoint, exact hash, base-model requirements and reconstruction constraints |
+| [results/](../results/) | [Final report](../results/report.md), metrics, CSV tables and standalone charts |
+| [docs/](README.md) | This guide and [reproduction instructions](reproduce.md) |
+| [source/](../source/) | Complete committed project tree, including code, tests, examples, frontend, documentation and small evidence |
+| [archive/](../archive/README.md) | Index to historical checkpoints, source revisions and publication records |
+
+The [API guide](../source/docs/usage/jev-api.md) describes the Jev-compatible
+request shape. The [playground guide](../source/docs/usage/playground.md) describes
+the local browser interface. Neither the Hugging Face repository nor its model
+card is a hosted inference service.
+
+## Current files and immutable history
+
+The front directories provide convenient copies and navigation. Exact release
+identity comes from the existing pointers and their recorded Hub payload commits:
+
+- [FINAL_MODEL.json](../FINAL_MODEL.json): calibrated model hash and original final-evaluation manifest.
+- [PROFILE_RESULTS.json](../PROFILE_RESULTS.json): completed profiling, stopped-training evidence and source archives.
+- [CURRENT_SNAPSHOT.json](../CURRENT_SNAPSHOT.json): earlier training snapshot, including resumable state; it is not the final-model pointer.
+
+Historical `final/`, `profiles/`, `snapshots/`, `sources/` and `publications/`
+payloads remain available at their recorded paths. Their manifests and checksums
+are not rewritten to fit this presentation. Use a pointer's `payload_commit`
+when retrieving its `path` and `manifest_path` for a reproducible download.
+
+`source/` is the complete publication source tree. The exact training and
+evaluation revisions are separately recorded in artifact metadata and immutable
+source archives; a later documentation revision is not a new model training run.
+Keep source-relative paths intact when executing commands.
+
+## Scope
+
+The calibrated checkpoint contains adapter/head parameters and requires the
+pinned pretrained base. It is a custom OpenSysOne artifact. Results support the
+reported benchmark comparisons, with separate calibration and validation-only
+selection; they do not establish general intelligence or calibration on arbitrary
+tasks. The release preserves the repository's existing license metadata and all
+upstream data notices. See the [model notes](../model/README.md) and
+[measured report](../results/report.md) before interpreting probabilities or speed.

source/docs/publication/reproduce.md

@@ -0,0 +1,133 @@
+# Reproduce the published scorer
+
+The published artifact is a custom adapter/head checkpoint requiring a pinned
+local base and the supplied project code. These instructions describe the
+evaluated Linux/GB10 setup and its current path constraints, not a portable
+one-command installation.
+
+## Retrieve and verify
+
+Download the published tree with your authorized Hugging Face client, retaining
+the sibling `source/` and `model/` directories. For immutable evidence, retrieve
+the path in [FINAL_MODEL.json](../FINAL_MODEL.json) at its recorded `payload_commit`
+and verify its model and manifest hashes. The front model must match the same
+bytes. From the downloaded repository root:
+
+```bash
+sha256sum model/model.pt
+cd source
+```
+
+Expected model SHA-256:
+
+```text
+e270e3da905604d97bf5a8f380ea308133403d1c4790a5c012cb1c12e9b6f348
+```
+
+Keep `source/` intact. Scripts import modules relative to its root, examples and
+web assets use that layout, and evaluation's data signature hashes
+`training_model.py` relative to the working directory. Run the following commands
+from `source/`.
+
+## Environment and pinned base
+
+The completed [evaluation manifest](../final/20260916T194403396250Z-fleet/evaluation/manifest.json)
+records NVIDIA GB10, CUDA 13.0 and these installed packages:
+
+| Package | Recorded version |
+| --- | --- |
+| torch | `2.11.0+cu130` |
+| transformers | `5.15.0` |
+| pyarrow | `25.0.1` |
+| numpy | `2.5.2` |
+
+These are measured environment identifiers, not a claim that the same CUDA build
+is available on every platform. The evaluated isolated interpreter is
+`/home/andy/ai/envs/opensysone/bin/python`. On another machine, create an isolated
+compatible environment and verify it against the recorded evidence before
+claiming reproduction. No dependency version should be inferred from the model
+card alone.
+
+The base is `Qwen/Qwen3-4B-Instruct-2507` at revision
+`cdbee75f17c01a7cc42f958dc650907174af0554`. On the recorded `/home/andy` account,
+the existing CPU-only downloader retrieves that pin and writes its provenance:
+
+```bash
+/home/andy/ai/envs/opensysone/bin/python scripts/download_candidate.py \
+  --model Qwen/Qwen3-4B-Instruct-2507
+```
+
+It writes under the invoking user's home. The artifact loader specifically
+expects `/home/andy/ai/models/opensysone/Qwen3-4B-Instruct-2507-cdbee75f`, including
+`opensysone-provenance.json`. Another home directory requires arranging the pinned
+base at that recorded location; the current loader has no base-path override.
+Do not modify the released checkpoint to change its paths. See
+[model reconstruction notes](../model/README.md).
+
+## Local scoring and API
+
+Before loading a model, inspect available memory and existing GPU jobs:
+
+```bash
+free -b
+nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv
+```
+
+The harness checks for at least 24 GiB currently available host memory, restores
+OOM adjustment 0 and applies a 16 GiB CUDA allocation cap. The verified path uses
+FP32. The example is an invented request, not a benchmark measurement:
+
+```bash
+/home/andy/ai/envs/opensysone/bin/python jev_harness.py \
+  --backend local --checkpoint ../model/model.pt \
+  --request examples/jev_request.json --device cuda --max-tokens 1024
+```
+
+To run the same scorer as a loopback API on an unused local port:
+
+```bash
+/home/andy/ai/envs/opensysone/bin/python jev_harness.py \
+  --backend serve --checkpoint ../model/model.pt \
+  --device cuda --max-tokens 1024 --port 18081
+```
+
+In another terminal:
+
+```bash
+curl --fail http://127.0.0.1:18081/health
+```
+
+The server binds to `127.0.0.1`; it does not expose a public endpoint. Read the
+[API guide](../source/docs/usage/jev-api.md) for request shape, optional local
+authentication and hosted Jev comparison. Hosted Jev needs a separate credential
+and was not exercised in the published evaluation. The
+[playground guide](../source/docs/usage/playground.md) covers the browser interface
+and its fixed checkpoint catalog. Existing machine-specific run paths in usage
+guides are operational records, not files downloaded with the model.
+
+The 1,024-token limit applies separately to each complete chat-formatted candidate
+prompt. Excess-length input is rejected rather than truncated. The artifact's
+default training limit is 512, so preserve `--max-tokens 1024` for the documented
+inference configuration. Scalar calibration is applied by the harness.
+
+## Reproducing evidence
+
+The [final report](../results/report.md) separates the full 2,042-decision test and
+768-decision Social IQA holdout from the matched 320-decision speed-profile sample
+and 383 expansion diagnostics. It reports the baseline definition, repeat counts,
+exact sample sizes and confidence-interval direction.
+
+Use [PROFILE_RESULTS.json](../PROFILE_RESULTS.json) and its immutable manifest to
+retrieve the frozen profiling protocol, requests, raw predictions, timings and
+source archives. [CURRENT_SNAPSHOT.json](../CURRENT_SNAPSHOT.json) records earlier
+training backups; it is not the final selected model. Historical dataset and
+source manifests record the exact hashes required for retraining or evaluation.
+For resume, keep each `checkpoint.pt`, sibling `best.pt` and matching validation
+evidence together. The calibrated `model.pt` is an inference artifact without
+optimizer state.
+
+Replay requires those complete evidence bundles and recorded source revisions;
+the convenient current `source/` view alone is not a substitute for the frozen
+training/evaluation provenance. Do not select a new checkpoint or fit temperatures
+using the published test or holdout results. No new inference is required to read
+the existing report and integrity manifests.

source/{NEXT_STEPS.md → docs/research/next-steps.md}

@@ -1,5 +1,56 @@
 # Findings and next steps — 17 September 2026
 
+## After training wrap-up
+
+Training is stopped at the user's request. Preserve the frozen selected 4B model
+and all final resumable states; the overnight actions below are historical.
+The completed matched profile gives the selected model 285/320 correct (89.06%),
+the pretrained per-option verifier 259/320 (80.94%), and the pretrained joint-label
+method 276/320 (86.25%). These small-sample differences are descriptive.
+
+The current implementation does not demonstrate a speed advantage over the
+same-sized base model. A 768-token state with one four-choice question takes
+3.710 seconds for the trained scorer, 3.177 seconds for the original verifier,
+and 0.818 seconds for the joint-label method on the same idle Spark in FP32.
+The trained scorer repeats the context for every option; its unmerged adapters
+also add work. This comparison excludes model loading, HTTP and generated prose.
+See [profiling-protocol.md](profiling-protocol.md) for the protocol and final report.
+
+Recommended follow-up experiments, after this completed campaign:
+
+1. **Reuse the context prefix in the selected 4B inference path.** Keep the
+   existing full-forward FP32 result as the reference, prove candidate-order,
+   question-batch and cache-reuse invariance, then remeasure the same 12 timing
+   cells. The earlier 0.5B smoke establishes feasibility only; it is not a speed
+   result for this 4B checkpoint.
+2. **Measure merged adapters separately.** Merge the frozen LoRA updates into
+   a deployment copy and verify logits/probabilities against the saved model
+   before timing. The current scorer is 11–17% slower than the unadapted verifier;
+   removing adapter operations is a plausible optimization, not a measured gain.
+   Keep precision changes in a separate correctness-controlled experiment.
+3. **Give expanded-data training an appropriate validation plan.** The stopped
+   159-update branch improves expansion diagnostics from 303/383 to 312/383,
+   while the matched original/holdout sample changes from 285/320 to 284/320.
+   HellaSwag supplies most of the gain. Its original four-family validation
+   criterion did not select the new weights. A future run should predeclare a
+   seven-family validation objective and new untouched test data; these observed
+   diagnostics must not become an unacknowledged selection set.
+4. **Use the Sparks together first as independent scoring replicas.** Request
+   sharding is a bounded way to test aggregate throughput while each host holds
+   its own model and memory. Measure one- and two-host completed requests per
+   second and latency under identical load. This does not itself reduce the
+   latency of a single request. Data-parallel LoRA training is a later option:
+   verify gradient/update parity and recovery, then measure communication cost
+   before committing a campaign. No distributed training or pooled-memory speed
+   claim follows from this run.
+
+The two Sparks did useful parallel work in this wrap-up: Spark A measured all
+three inference methods sequentially without competing GPU work, while Spark B
+independently evaluated the expanded checkpoint on identical frozen examples.
+No further training or optimization was started as part of reporting these results.
+
+## Historical overnight recommendation
+
 Continue the two improving 4B runs and use Spark B's freed GPU for a conservative
 4B refinement. Preserve the completed 2B candidate. Do not replace the working
 training/finalization path with unmeasured distributed training before today's
@@ -41,8 +92,8 @@ predeclared criterion rather than switch objectives to whichever number looks
 best. Final serving temperature will be fitted on the separate calibration split.
 
 Small reproducible evidence and all 22 scheduled validation points are in
-[results/20260917-fleet-progress](results/20260917-fleet-progress/).
-[HANDOVER.md](HANDOVER.md) and [FLEET_RUN.md](FLEET_RUN.md) own live paths and controls.
+[results/20260917-fleet-progress](../../results/20260917-fleet-progress).
+[handover.md](../operations/handover.md) and [fleet.md](../operations/fleet.md) own live paths and controls.
 
 ## Actions within this deadline
 
@@ -170,10 +221,10 @@ analysis, not independent test evidence.
 Both exact checkpoints are preserved under
 `~/ai/opensysone/runs/20260917T022201Z-ensemble-reference` on GX10, with
 source/copy hashes and CPU reconstruction checks in
-[ensemble-reference.json](results/20260917-fleet-progress/ensemble-reference.json),
+[ensemble-reference.json](../../results/20260917-fleet-progress/ensemble-reference.json),
 for a later latency/quality experiment. Do not
 add an ensemble to today's deployment based on this small uncertain difference.
 The current fleet still selects individual checkpoints. A future ensemble needs
 its own frozen artifact rule, common input-length policy, separate calibration,
 held-out evaluation, measured latency and two-worker recovery checks.
-[Raw analysis and input hashes](results/20260917-fleet-progress/fixed-ensemble-validation.json).
+[Raw analysis and input hashes](../../results/20260917-fleet-progress/fixed-ensemble-validation.json).

source/{RESEARCH_NOTES.md → docs/research/precision.md}

@@ -1,6 +1,6 @@
 # Research notes for the GX10 handover
 
-Assessed 2026-09-16. Read alongside `PLAN.md`, the measured run artifacts, and
+Assessed 2026-09-16. Read alongside [plan.md](../operations/plan.md), the measured run artifacts, and
 `~/code/gx10/docs/{training,ai-environment}.md`. These are engineering recommendations;
 they do not claim a reproduction of TypeSafe's architecture or measured model quality.
 
@@ -10,7 +10,7 @@ SDPA MATH backend. The same weights evaluated in FP32 passed at about 1.4e-5 or
 better across the diagnostic comparisons. The reference smoke therefore uses
 FP32 parameters/optimizer/inference. BF16 elsewhere in these notes is a proposed
 future configuration and arithmetic estimate, conditional on fixing this gate.
-See `results/20260916T154714Z/parity_diagnosis.json` and `RESULTS.md`.
+See `results/20260916T154714Z/parity_diagnosis.json` and [results-history.md](../operations/results-history.md).
 
 ## Scope and available resources
 

source/docs/research/profiling-protocol.md

@@ -0,0 +1,146 @@
+# Training wrap-up and accuracy/speed protocol
+
+The user requested training to finish and accuracy/speed to be profiled on
+2026-09-17 at approximately 07:49 UTC. This explicitly advances the training
+stop and final selection; it does not extend the original 18:16:10 UTC deadline.
+All further model work is evaluation/inference, with no optimizer updates.
+
+## Checkpoint selection
+
+Stop the three active supervisors gracefully and retain both current/resumable
+and validation-selected checkpoints. Record final steps and exit codes. Evaluate
+the latest saved weights of each stopped run on the same frozen 512 validation
+decisions, including the expanded-data run whose next periodic validation had
+not yet occurred. Keep the fixed four-fold temperature-crossfit macro-family NLL
+policy and require improvement strictly greater than 0.001 to replace that run's
+previous best. Preserve the original checkpoint bytes, source/model/data pins,
+optimizer/RNG state and matching validation evidence in separate snapshots.
+
+`scripts/final_validation.py` creates a durable reconstruction before inference,
+verifies original source and all data hashes, reads only validation decisions,
+and writes a separate fleet-compatible candidate directory. The fixed reference
+SHA256 is `e671e1508185765552b0f933ba03f356be62143c531d8ef534457d34b1645c9b`.
+Final validation runs on the three independent GPUs, with a 09:00 UTC watchdog.
+The original stopped GX10 and completed 2B candidates remain eligible. Freeze
+the winner before reading any held-out prediction results.
+
+## Accuracy
+
+Use the existing finalizer for the winner, preserving the calibrated checkpoint
+before test predictions. The 4B evaluation contains 510 calibration decisions,
+2,042 original test decisions and 768 Social IQA decisions from an untrained task
+family. Compare the tuned scorer with the unchanged pinned pretrained readout,
+both raw and with independently fitted global temperatures. Report accuracy,
+NLL, multiclass Brier, top-label ECE and per-family results. Existing confidence
+intervals use 400 paired, stratified source-group bootstrap resamples.
+
+Separately compare inference methods on a predeclared balanced sample of 320
+held-out decisions (64 each from ARC, Banking77, BoolQ, SNLI and Social IQA), plus
+the 383 retained HellaSwag/PIQA/CommonsenseQA diagnostic decisions that never
+entered training or checkpoint selection. Sample IDs are chosen deterministically
+before scoring; apply a common 1,024-token inference eligibility limit, report
+every exclusion, and use matched retained rows across methods. These additional
+diagnostics cannot change the frozen winner. If the expanded latest checkpoint
+does not win, evaluate it on the same diagnostic rows on the spare Spark to
+measure the observed effect of the expanded-data run without further selection.
+
+## Speed
+
+Benchmark the actual current inference implementation. Do not introduce shared
+prefix caching or change numerical precision during this profile. Compare on
+one otherwise idle Spark, with the same pinned 4B base, FP32, SDPA and isolated
+software environment:
+
+1. Trained adapters and scalar head, full context separately for each option.
+2. Physically adapter-free pretrained yes-minus-no scoring, with the identical
+   per-option verifier prompt.
+3. Physically adapter-free pretrained model reading all options together and
+   returning one constrained answer-label token. Its label probabilities use
+   logits restricted to verified single-token label IDs. Computing only those
+   output projections is an explicit optimization equivalent to constrained
+   next-token scoring; there is no generated explanation or JSON output.
+
+The first two methods treat candidates independently; the third uses one joint
+prompt. Record that semantic difference and each method's processed token counts.
+The third method is a strong, efficient baseline, not a claim about arbitrary
+general-purpose serving systems or the hosted Jev API.
+
+Use 12 workload cells: approximately 128/768 state tokens crossed with
+(questions, choices) = (1,2), (1,4), (1,16), (4,2), (4,4), (16,2).
+Use distinct fixed invented questions and serialize the exact requests. These are
+timing workloads, not semantic generalization tests. Each cell has two warmups
+and ten measured repetitions, with synchronized wall-clock timings including
+tokenization and probability construction. Save all observations, median, an
+exploratory empirical p95, throughput, token/branch counts and peak allocation.
+Ten observations provide only a rough tail estimate. Record model-load time
+separately; warm timings must not be labeled cold-start timings.
+
+Keep the 16 GiB CUDA allocation cap, at least 24 GiB available host memory before
+loading, GPU process inspection and OOM adjustment 0. No training shares the
+benchmark GPU. The GX10 GUI remains available on 7466 while accuracy evaluation
+runs; speed measurements therefore use a Spark. Hardware/software/source and
+checkpoint hashes, exclusions, exit status and exact raw measurements accompany
+the report. Source, final checkpoints and evidence are backed up to the existing
+private Hugging Face repository with its current licenses and visibility.
+
+## Execution and findings
+
+All three training stops and final validation passes exited 0. The latest
+expanded159 / A4765 / B2000 checkpoints scored validation crossfit NLL
+0.172775 / 0.189890 / 0.170304, retaining best0 / best4000 / best1500 respectively.
+All five candidates were eligible. The selection froze at 08:09:51 UTC: expanded
+branch0 ties Spark B1500 at 0.170150 and 94.7266% validation accuracy, and wins the
+deterministic name tie-break. A live CPU proof confirms all 506 trainable tensors
+are identical across selected branch0, parent1500 and the calibrated artifact.
+
+Calibration is complete with temperature 1.745822072, checkpoint SHA256
+`e270e3da905604d97bf5a8f380ea308133403d1c4790a5c012cb1c12e9b6f348` saved before test
+prediction. Full accuracy and matched speed evaluations completed from source
+`07f10e7`; see the [handover](../operations/handover.md) for run paths and controls. The fixed profile contains
+320 held-out decisions and all 383 diagnostics, preserving the original 512-token
+per-choice eligibility before the common 1,024-token inference check. Six original
+test rows and one diagnostic row remain excluded; no further rows are dropped.
+
+The selected calibrated model is available in the port-7466 GUI. All matched
+profiling jobs have now exited 0; the independent audit checked all 2,812
+prediction rows, 36 timing cells and 360 measured samples. Both Spark GPUs were
+idle at 09:09:56 UTC. Full GX10 test/holdout evaluation and local API checks completed at 09:20:23 UTC,
+with evaluation, harness and coordinator exit 0.
+
+| Method | Matched held-out accuracy (320) | Expansion diagnostics (383) |
+| --- | ---: | ---: |
+| Selected 4B scorer | 89.06% (285) | 79.11% (303) |
+| Pretrained per-option verifier | 80.94% (259) | 77.81% (298) |
+| Pretrained joint-label method | 86.25% (276) | 79.11% (303) |
+| Expanded checkpoint 159 | 88.75% (284) | 81.46% (312) |
+
+The selected scorer beats the joint-label method by 9 correct answers on the
+matched 320: 19 selected-only successes versus 10 label-only successes. Its
+aggregate diagnostic score equals the joint-label method, with different errors.
+These point estimates do not establish a universal accuracy ranking. The expanded
+checkpoint gains 14 answers and loses 5 on the diagnostics; it gains 2 and loses
+3 on the matched held-out sample. Neither result changes the frozen selection.
+
+For one four-choice question with a 768-token state, median local warm latency is
+3.710 s selected / 3.177 s per-option base / 0.818 s joint-label base. The current
+trained implementation is therefore about 4.53 times slower than the efficient
+joint-label baseline on this workload. Across all 12 cells it is 11–17% slower
+than the matched unadapted per-option verifier. Repeated context processing and
+unmerged adapter work are targets for future measurement; no 4B prefix-cache or
+merged-adapter speedup has been established. See [next-steps.md](next-steps.md).
+
+Raw profiles, exact requests and independent audit live under
+`20260917T075209Z-training-wrapup/profile-deployment` in the run store; profile
+source is `07f10e7`. The [completed report](../../results/20260917-wrapup/profile-report/report.md)
+contains tables, CSV/JSON data and standalone latency/accuracy plots. Full test
+accuracy is 92.90% selected versus 84.48% base verifier; Social IQA is 72.92%
+versus 70.31%. The 95% paired source-group bootstrap intervals on the gains are
+[6.85, 9.89] and [0.13, 5.34] percentage points respectively. These results cover
+the declared public datasets; pretraining exposure is unknown. Proper scores
+and independent temperature calibration are reported separately from accuracy.
+
+The final calibrated model has been uploaded and remotely verified at
+[andyshu/opensysone](https://huggingface.co/andyshu/opensysone). `FINAL_MODEL.json`
+identifies the calibrated release; `PROFILE_RESULTS.json` identifies the separately
+verified profiling/report/checkpoint archive. Publication receipts and live
+service controls are recorded in the [handover](../operations/handover.md).

source/{EXPANDED_DATA.md → docs/research/training-data.md}

@@ -1,5 +1,10 @@
 # Training-data expansion, 2026-09-17
 
+This document preserves the expansion experiment and its verification. The
+campaign has completed; see the [current handover](../operations/handover.md) and
+[final results](../../RESULTS.md). The dated launch and control details below
+remain as provenance.
+
 The user requested a broader training set. Version 2 adds official training data
 from HellaSwag, PIQA and CommonsenseQA to the complete version-1 replay set.
 These sources add plausible continuations, physical problem solving and everyday

source/{JEV_HARNESS.md → docs/usage/jev-api.md}

@@ -1,13 +1,14 @@
 # Use OpenSysOne and hosted Jev with the same request
 
 For an interactive text-and-options interface, use the
-[browser playground](PLAYGROUND.md). It serves frozen training snapshots through
-this scoring code while the longer training campaign continues.
+[browser playground](playground.md). It serves frozen training snapshots through
+this scoring code. Training is complete; the selected calibrated 4B model is the
+default, and the final local API is running on loopback port **18081**.
 
 The harness implements TypeSafe's documented `POST /v1/systemone` request and
 answer shapes for `choice`, `score` and `noul`. See the
 [official API reference](https://docs.typesafe.ai/api) and
-[example request](examples/jev_request.json). It supports local trained scoring,
+[example request](../../examples/jev_request.json). It supports local trained scoring,
 hosted Jev calls, and a comparison of both responses and elapsed request times.
 Agreement is not a quality benchmark.
 
@@ -18,7 +19,7 @@ cd /home/andy/projects/opensysone
 OPENSYSONE_PYTHON=/home/andy/ai/envs/opensysone/bin/python
 ```
 
-After the campaign finishes, its calibrated checkpoint is recorded in
+The completed campaign's calibrated checkpoint is recorded in
 `/home/andy/ai/opensysone/deploy/current.json`. Substitute its `model` path below.
 Only load trusted project checkpoints; they contain serialized Python state.
 
@@ -90,5 +91,5 @@ ssh -N -L 18081:127.0.0.1:18081 gx10
 Inspect and stop the fleet coordinator or its resulting API with
 `scripts/fleet_campaign.py --campaign <fleet-run> --status` or `--stop`.
 Individual training jobs use `scripts/campaign_status.py`. See
-[HANDOVER.md](HANDOVER.md) for exact run paths,
+[handover.md](../operations/handover.md) for exact run paths,
 deadline, source revisions and restart commands.

source/{PLAYGROUND.md → docs/usage/playground.md}

@@ -11,7 +11,7 @@ ssh -N -L 7466:127.0.0.1:7466 gx10
 ```
 
 Open **http://localhost:7466**. If you are using a browser directly on GX10, no
-tunnel is needed. Port 18081 remains reserved for the final evaluated model API.
+tunnel is needed. The final evaluated model API is running on loopback port 18081.
 
 Paste your text, adjust the question and enter at least two distinct options.
 Click **Get probabilities**, or press **Command/Ctrl + Enter**. The examples are
@@ -30,13 +30,16 @@ The available snapshots are:
 
 | Model | Selected step | Calibration |
 | --- | ---: | --- |
+| Qwen3 4B · Selected (default) | Spark B 1,500, retained at expanded branch 0 | Separate 510-decision calibration |
 | Qwen3 4B · Main | 2,500 | Uncalibrated |
 | Qwen3 4B · Lower rate | 2,500 | Uncalibrated |
 | Qwen3.5 2B | 2,000 | Uncalibrated |
 
 Probabilities are normalized over the supplied options. Adding/removing an option
 changes the question being scored. An uncalibrated 90% output is not a demonstrated
-90% success rate. These fixed snapshots remain stable while training continues.
+90% success rate. The default selected model uses temperature **1.745822** fitted
+on separate calibration data; calibration on arbitrary new tasks is unproven.
+Training has stopped. The other three entries are preserved historical snapshots.
 
 Each complete context/question/option prompt must fit the verified **1,024-token**
 inference limit, including chat formatting. The server reports an error for longer
@@ -46,13 +49,13 @@ loads its weights. One request runs at a time; other requests receive a busy rep
 
 ## Runtime and controls
 
-The current server is running as PID **1469393**, backend source **`2d0ff79`**, with pending
-exit status. Its immutable runtime is
-`/home/andy/ai/opensysone/runs/20260917T034059Z-playground-port7466`.
+The current server is running as PID **1674635**, supervised by **1674634**, backend
+source **`00c80dd`**, with pending exit status while serving. Its runtime is
+`/home/andy/ai/opensysone/runs/20260917T081925Z-playground-selected`.
 
 The runtime directory is recorded in `~/ai/opensysone/runs/LAST_PLAYGROUND`.
 Its `models.json` lists the fixed snapshots, and its launch/verification evidence
-records the process, source revision, log and numerical checks. `HANDOVER.md`
+records the process, source revision, log and numerical checks. [handover.md](../operations/handover.md)
 records the active process. Inspect the listener and status with:
 
 ```bash
@@ -64,12 +67,13 @@ Use the existing isolated environment from this project directory to start it:
 
 ```bash
 ~/ai/envs/opensysone/bin/python playground.py \
-  --models /home/andy/ai/opensysone/runs/20260917T034059Z-playground-port7466/models.json \
+  --models /home/andy/ai/opensysone/runs/20260917T081925Z-playground-selected/models.json \
   --port 7466 --device cuda --max-tokens 1024
 ```
 
-Before restarting, verify and stop the exact recorded playground process with
-SIGTERM. This is separate from the training jobs, fleet coordinator and final-model
+Before restarting, verify the command in `launch.json` and stop the exact recorded
+playground process with SIGTERM. Its wrapper records the exit in `state.json` and
+`exit_code`; serving output is in `run.log`. This is separate from the fleet coordinator and final-model
 uploader. The server binds only to loopback; it requires no firewall or service
 configuration changes. No frontend dependency installation is required.
 Static assets are read afresh on each request; `frontend-current.json` in the
@@ -78,22 +82,29 @@ runtime records the current frontend revision and verified served asset hashes.
 The server loads one model at a time, checks frozen checkpoint hashes and model
 metadata, and releases a previous model before loading a different one. The same
 24 GiB available-memory check, 16 GiB CUDA allocation cap, FP32 scoring and OOM
-adjustment 0 apply. GPU inference shares compute with GX10 training; frequent or
-large requests can slow that training. Idle browser tabs perform no model inference.
+adjustment 0 apply. Final accuracy evaluation and speed profiling are complete.
+Inference requests consume GX10 compute while they run; idle browser tabs perform
+no model inference. The recorded speed profile used an otherwise idle Spark.
 The runtime supports `--device cpu` as an alternative, with latency depending on
 the model and input. Check current memory and GPU processes before any model load.
 
 The frontend is in `web/`, the server is `playground.py`, and the input/scoring
-contract is inherited from `jev_harness.py`. See [JEV_HARNESS.md](JEV_HARNESS.md)
+contract is inherited from `jev_harness.py`. See [jev-api.md](jev-api.md)
 for the underlying API and hosted Jev integration.
 
 
 ## Verification
 
+The selected-model refresh passed real-browser scoring for all four entries on
+2026-09-17 at approximately 08:20 UTC, including normalized probabilities, the
+calibrated badge, responsive layout and input/error/copy behavior. Runtime evidence
+and screenshots are in `20260917T081925Z-playground-selected/browser-check` under
+the run root. The earlier screenshots and timings below are historical.
+
 Seven backend tests pass with
 `python3 -m unittest discover -s tests -p 'test_playground.py'`.
 Real Chromium checks and screenshots are in
-[`results/20260917-playground`](results/20260917-playground/). They score invented
+[`results/20260917-playground`](../../results/20260917-playground). They score invented
 examples with all three actual models; reserved test/holdout data remains untouched.
 The main-model warm example took 1.53 seconds; first-load/model-switch requests
 were 5.08–9.10 seconds. These single observations are not latency percentiles.
@@ -124,4 +135,4 @@ control visibility, tab navigation, input preservation, long result lists and
 error recovery across desktop, phone and landscape sizes.
 The relayout passed 13 viewport sizes, including 320×568 and short 390×360
 windows, with at least one readable line in every visible input. Reports and
-screenshots are in [`results/20260917-playground-layout`](results/20260917-playground-layout/).
+screenshots are in [`results/20260917-playground-layout`](../../results/20260917-playground-layout).

source/results/20260917-wrapup/final-completion-proof.json

@@ -0,0 +1,132 @@
+{
+  "utc": "2026-09-17T09:21:35.846962+00:00",
+  "status": "complete",
+  "fleet": {
+    "status": "complete",
+    "stage": "serving",
+    "evaluation_exit_code": 0,
+    "harness_check_exit_code": 0,
+    "api_pid": 1716630,
+    "api_ready": true,
+    "finished_utc": "2026-09-17T09:20:23.727387+00:00",
+    "source_commit": "07f10e791061a679b829ed1dc5b33897e001d67d"
+  },
+  "fleet_exit_code": 0,
+  "final_publication": {
+    "attempt": 1,
+    "campaign": "/home/andy/ai/opensysone/runs/20260916T194403396250Z-fleet",
+    "command": [
+      "/home/andy/ai/envs/opensysone/bin/python",
+      "/home/andy/projects/opensysone/scripts/publish_hf_final.py",
+      "--campaign",
+      "/home/andy/ai/opensysone/runs/20260916T194403396250Z-fleet",
+      "--repo-id",
+      "andyshu/opensysone",
+      "--output",
+      "/home/andy/ai/opensysone/runs/20260917T023940Z-hf-final-watch",
+      "--watch"
+    ],
+    "export": "/home/andy/ai/opensysone/exports/20260916T194403396250Z-fleet-final",
+    "finished_utc": "2026-09-17T09:20:52.353902+00:00",
+    "heartbeat_utc": "2026-09-17T09:20:52.353917+00:00",
+    "manifest_path": "final/20260916T194403396250Z-fleet/backup_manifest.json",
+    "manifest_sha256": "47763a137e92890a0ddd32ba0f061e854cc7072a863f8a574edc941b63567c64",
+    "model_sha256": "e270e3da905604d97bf5a8f380ea308133403d1c4790a5c012cb1c12e9b6f348",
+    "path": "final/20260916T194403396250Z-fleet/model.pt",
+    "payload_commit": "8cb06c73102eb4b3fe8944600e915c9df33d4b4a",
+    "pid": 1427060,
+    "pointer_commit": "2082f71beb86740f36f00b82a6eeab64b9e89b61",
+    "published_utc": "2026-09-17T09:20:51.125488+00:00",
+    "repo_id": "andyshu/opensysone",
+    "repository_private": true,
+    "source_commit": "6729461ccaad32e239c2148fa0c8f9ca23513a7a",
+    "stage": "complete",
+    "started_utc": "2026-09-17T02:39:41.036136+00:00",
+    "status": "complete",
+    "watchdog_deadline_utc": "2026-09-17T18:46:10+00:00"
+  },
+  "final_publication_exit_code": 0,
+  "api_health": {
+    "status": "ready",
+    "model": "opensysone-qwen3-4b-instruct-2507",
+    "checkpoint": "/home/andy/ai/opensysone/runs/20260916T194403396250Z-fleet/evaluation/model.pt",
+    "max_tokens": 1024,
+    "temperature_fitted": true
+  },
+  "playground_status": {
+    "status": "ready",
+    "loaded_model_id": "spark-b-2b"
+  },
+  "playground_models": {
+    "models": [
+      {
+        "id": "selected-4b",
+        "label": "Qwen3 4B \u00b7 Selected",
+        "description": "Selected Spark B step-1,500 weights, retained at expanded branch step 0; separately calibrated.",
+        "calibrated": true,
+        "max_tokens": 1024,
+        "checkpoint_step": 0
+      },
+      {
+        "id": "gx10-4b",
+        "label": "Qwen3 4B \u00b7 Main",
+        "description": "Main training run; selected checkpoint at step 2,500.",
+        "calibrated": false,
+        "max_tokens": 1024,
+        "checkpoint_step": 2500
+      },
+      {
+        "id": "spark-a-4b",
+        "label": "Qwen3 4B \u00b7 Lower rate",
+        "description": "Lower learning rate; selected checkpoint at step 2,500.",
+        "calibrated": false,
+        "max_tokens": 1024,
+        "checkpoint_step": 2500
+      },
+      {
+        "id": "spark-b-2b",
+        "label": "Qwen3.5 2B",
+        "description": "Smaller model; selected checkpoint at step 2,000.",
+        "calibrated": false,
+        "max_tokens": 1024,
+        "checkpoint_step": 2000
+      }
+    ],
+    "default_model": "selected-4b"
+  },
+  "model_sha256": "e270e3da905604d97bf5a8f380ea308133403d1c4790a5c012cb1c12e9b6f348",
+  "gx10_open_sys_one_processes": [
+    {
+      "pid": 1674635,
+      "command": [
+        "/home/andy/ai/envs/opensysone/bin/python",
+        "/home/andy/projects/opensysone/playground.py",
+        "--models",
+        "/home/andy/ai/opensysone/runs/20260917T081925Z-playground-selected/models.json",
+        "--port",
+        "7466",
+        "--device",
+        "cuda",
+        "--max-tokens",
+        "1024"
+      ],
+      "oom_score_adj": "0"
+    },
+    {
+      "pid": 1716630,
+      "command": [
+        "/home/andy/ai/envs/opensysone/bin/python",
+        "/home/andy/ai/opensysone/source/profile-07f10e7/jev_harness.py",
+        "--backend",
+        "serve",
+        "--checkpoint",
+        "/home/andy/ai/opensysone/runs/20260916T194403396250Z-fleet/evaluation/model.pt",
+        "--port",
+        "18081",
+        "--max-tokens",
+        "1024"
+      ],
+      "oom_score_adj": "0"
+    }
+  ]
+}