Add registered reviewer-requested held-out-name and acquisition tests

README.md

@@ -11,31 +11,50 @@ tags:
 
 # Persistent Event-State models
 
-Assets for *Learning Persistent Referential Identity for Multi-Step Memory in Event-Stream Models*.
+Assets for *Learning Persistent Referential Identity for Two-Hop Retrieval in Event-Stream Models*.
 
 | Experiment | Assets |
 |---|---|
 | E1 / E3 | `src/`, `eval/`, `configs/`, `checkpoints/`, `results/`, `reproduce/` |
 | E8-FULL | `e8_full/source/`, nine final `e8_full/checkpoints/`, `e8_full/results/`, `e8_full/reference/` |
+| Reviewer-requested tests | `rr2/campaign/`, 15 model-only `rr2/checkpoints/`, `rr2/reference/`, `rr2/frozen_evaluations/` |
 
-## Reproduce E8-FULL
+## Reproduce reviewer-requested tests
 
-Python 3.13; CUDA GPU for evaluation. Download both repositories at `paper-v2.0`:
+Python 3.13 and a CUDA GPU for evaluation/training.
 
 ```bash
 pip install huggingface_hub
-hf download nur-dev/pns-bind-25m --revision paper-v2.0 --local-dir pns-model
-hf download nur-dev/pns-world --repo-type dataset --revision paper-v2.0 --local-dir pns-data
+hf download nur-dev/pns-bind-25m --revision paper-v2.1 --local-dir pns-model
+hf download nur-dev/pns-world --repo-type dataset --revision paper-v2.1 --local-dir pns-data
 cd pns-model
 pip install -r e8_full/requirements.txt
+python rr2/reproduce.py verify --data ../pns-data/rr2
+CUDA_VISIBLE_DEVICES=0 python rr2/reproduce.py evaluate --data ../pns-data/rr2 --suite compose --arm future --seed 821 --split confirmation --output rr2_eval_821
+```
+
+Repeat with seeds `822`, `823`; use `--arm ordinary` for the ordinary-only control.
+Use `--suite original --arm ordinary --split known_development` for the original-name
+control, or `--suite gru --update 12000` for the semantic-only recurrent baseline.
+Raw predictions are compared with published arrays. Checkpoints contain identical
+model tensors with optimizer state omitted; hashes are in `rr2/INFERENCE_EXPORT.json`.
+
+```bash
+python rr2/reproduce.py prepare-training --data ../pns-data/rr2 --output rr2_train --workers 24
+CUDA_VISIBLE_DEVICES=0 python rr2/reproduce.py train --data ../pns-data/rr2 --output rr2_train --suite compose --arm future --seed 821
+```
+
+Training corpora are regenerated deterministically and checked against frozen hashes.
+`rr2/GPU_RESULTS.json` contains all primary and secondary criterion outcomes.
+
+## Reproduce E8-FULL
+
+```bash
 python e8_full/reproduce.py verify --data ../pns-data/e8_full
-python e8_full/reproduce.py evaluate --data ../pns-data/e8_full --arm grounded_future --seed 821 --output e8_results/grounded_future_821
+CUDA_VISIBLE_DEVICES=0 python e8_full/reproduce.py evaluate --data ../pns-data/e8_full --arm grounded_future --seed 821 --output e8_eval_821
 ```
 
-Repeat evaluation for arms `grounded_future`, `numeric_future`, `grounded_cut` and seeds
-`821`, `822`, `823`. The default split is `confirmation`; use `--split development` for
-development. `--smoke` checks one batch per panel. Results are compared row by row with
-the published outcomes. `summarize` reproduces reported metrics and gates without a GPU.
+Repeat for arms `numeric_future`, `grounded_cut` and seeds `822`, `823`.
 
 ## Reproduce E1 / E3
 
@@ -45,5 +64,4 @@ PNS_DATA=../pns-data bash reproduce/reproduce_headline.sh
 bash reproduce/reproduce_tables.sh
 ```
 
-`paper-v1.0` retains the original E1/E3 release. Data and split manifests are in
-[`nur-dev/pns-world`](https://huggingface.co/datasets/nur-dev/pns-world).
+`paper-v1.0` and `paper-v2.0` retain the original releases.

rr2/GPU_EVIDENCE_MANIFEST.json

@@ -0,0 +1,167 @@
+{
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+    "reference/runs/original_ordinary_s822/known_development_ordinary_rows.npz": "226565bd14795495585c01f88c189a10d516d2cc0ca3bd1ae03445014f3e513f",
+    "reference/runs/original_ordinary_s822/train.jsonl": "1d219953b11cfaa54310c79ec25854157c0e5d5e8b759b2ec56ff7350dabbad8",
+    "reference/runs/original_ordinary_s823/DONE.json": "6043c55d69a748b7d4e3e5a186b432763f8fd358cd8becea07e8458c4d7df120",
+    "reference/runs/original_ordinary_s823/START.json": "1a0d23da26e8da9178b2209cf473027edd6369110f0198c7c97ed433844b7581",
+    "reference/runs/original_ordinary_s823/known_development.json": "972b28fab48d8adb62678ccb60758f0136f2fa32f85ef8d9a8c955092857cc97",
+    "reference/runs/original_ordinary_s823/known_development_n16_challenge_rows.npz": "c6eca22d29c90d69588019e947d096e760c986b53db25d4c2915293f67621371",
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+    "reference/runs/original_ordinary_s823/known_development_ordinary_rows.npz": "2d7a7a4f4a7f76a3187821e88896449ab79e7de127caecdb4666e5743de8724a",
+    "reference/runs/original_ordinary_s823/train.jsonl": "d0185821376e9860074eca0a0c7d6778fc7139833988aff50e671bf9bfe3358b",
+    "reference/runs/original_reference_s821/known_development.json": "56a5eb90d0628325065a241d6d8a9ec080e34f2315f186ff6a610bf62aa50f29",
+    "reference/runs/original_reference_s821/known_development_n16_challenge_rows.npz": "30933c3e367c95770e845c3badff5257463aff54d4c2cd35fc3c6ff09aa47101",
+    "reference/runs/original_reference_s821/known_development_n32_challenge_rows.npz": "0a3e90344807a581c393a6fe68a2b6ea1077948744c4243347ca241d872d86a7",
+    "reference/runs/original_reference_s821/known_development_ordinary_rows.npz": "5d2e7d70e8fecf0a01bf5dda0c1df1745cf91946d94dc969b9a14fc43083a1ef",
+    "reference/runs/original_reference_s822/known_development.json": "7388d119a79237dc9bd233aa1e8374068d7f540bef75e953f7ec7c120847fa1c",
+    "reference/runs/original_reference_s822/known_development_n16_challenge_rows.npz": "c4a505e5ff555e87507422676616cc97fbf418edbfd8bd53f6638c811428d79a",
+    "reference/runs/original_reference_s822/known_development_n32_challenge_rows.npz": "fc42a39da52c31502e70731bcda5c23b11129e1ae736038b802cf3a72e76d7cb",
+    "reference/runs/original_reference_s822/known_development_ordinary_rows.npz": "4032579d2892d470a5384c0aec55e39b5fd28d8b14da7d3b43d6933af8221535",
+    "reference/runs/original_reference_s823/known_development.json": "0fc7716e7cd643ad00a155faec6e972a6337041682f961b79c7b29dd052adaca",
+    "reference/runs/original_reference_s823/known_development_n16_challenge_rows.npz": "2671df297954fc886bd4c5c9992cddd0e42c161e7a779bcde5953cf4a02102dc",
+    "reference/runs/original_reference_s823/known_development_n32_challenge_rows.npz": "323ea589627686c96c134572ddffaa3c9cbdb8ed94f0dbc25ae6aed4577c1f61",
+    "reference/runs/original_reference_s823/known_development_ordinary_rows.npz": "16b390dab4e1fb848f68cd08c602e1c6de933731c334cbf4910121146b22cafd",
+    "reproduce.py": "74507ed1f7d5d98823135f161bb2f718f29af03fe0ca43647ecdff71ea943079"
+  }
+}

rr2/campaign/PREREGISTRATION.md

@@ -0,0 +1,134 @@
+# RR2 additional-training campaign
+
+Registered 4 October 2026 after the author explicitly authorised additional GPU
+work. This is a new reviewer-requested campaign, not a reopening or rescue of
+E1, E3, E8, or the completed RR2 frozen-checkpoint tests. Their artifacts, public
+tags, verdicts and failed novel-name test remain unchanged. Do not revise the
+article or claim success before these runs finish and their evidence is checked.
+
+## Questions and fixed design
+
+1. Can the unchanged 24,352,640-parameter Grounded-Future configuration learn
+   name-component composition and retain causally functional destination identity
+   for surface names absent from every training path?
+2. What is achieved by the otherwise identical Grounded-Ordinary arm, with no
+   challenge worlds, future tables or challenge loss during training?
+3. Does a semantic-only conventional recurrent baseline improve on the historical
+   unsuccessful multitask generic-state optimization regime?
+
+Keep the published tokenizer, eight-layer 384-dimensional encoder, 640-wide
+records, name pooling, projection, structural addressing and output heads fixed.
+The only primary change is the lexical inventory. Form names from 12 left and
+18 right components separated by a hyphen. The existing tokenizer must encode
+every name into exactly seven pieces. Divide pairs by `(left_index + right_index)
+% 3` into training, lexical-development and lexical-confirmation inventories.
+Within each partition shuffle with seed 20261004 + partition index and select
+66 names: the original five entity-kind inventories retain sizes 26/14/10/8/8;
+the challenge uses the first 64. Check exact name disjointness, shared token
+support, fixed token lengths and coverage of every component before training.
+Confirmation names are also disjoint from development names. This tests new
+combinations of seen pieces, not entirely new characters or new semantic values.
+Hyphenated controlled names are not a claim about unrestricted natural language.
+
+Train six compositional-name runs: Grounded-Future and Grounded-Ordinary at
+seeds 821/822/823. Also train three original-inventory Grounded-Ordinary runs at
+the same seeds and on the exact archived E8 training shards. The original
+released Grounded-Future checkpoints provide the corresponding fixed reference.
+All full-model runs start from scratch and use the E8 initialization, 6,000
+updates, batch 96, 32-event TBPTT, AdamW 4e-4, betas (.9,.95), weight decay .01,
+clip 1, 300-update warmup and the original decay. Grounded-Future adds the
+unchanged 24 challenge worlds with four future value tables per update.
+Grounded-Ordinary performs only the unchanged ordinary multitask loss, retaining
+the sparse ordinary identity credit path. Do not remove ordinary two-hop losses.
+The ordinary arm's unused challenge value head has no training: its challenge
+answer accuracy is NOT an interpretable mechanism comparison. Report challenge
+identity separately, and use ordinary identity/answer metrics for attribution.
+
+The new ordinary training worlds use seeds 92000000--92119999 (120,000 lifetimes,
+256 events). Fresh ordinary known-name development: 92400000--92401999;
+lexical development: 92500000--92501999; lexical confirmation:
+92900000--92901999. Original-inventory controls use fresh evaluation seeds
+92400000--92401999 and the archived original training data. The compositional
+challenge training namespace is 95e12 + model_seed*1e7 + update*100; known-name
+development starts at 96e12, lexical development at 97e12 and sealed lexical
+confirmation at 98e12. Each evaluation condition has 2,000 worlds and all 32
+unrestricted value classes with collisions permitted. Test 16 AND 32 candidates
+at 0/128/512 actual unrelated writes. The 32-candidate test uses 64 records,
+twice the 16-candidate allocation; parameters are unchanged. All models use common
+evaluation worlds within each condition, not independent 6,000-row samples.
+
+## Quantitative primary gates and attribution
+
+The compositional Grounded-Future primary passes only if ALL three seeds pass:
+
+- Lexical-development identity >= .90 and two-hop >= .85, with Wilson lower
+  bound >= .80, at every tested delay and candidate count; one-hop >= .95.
+- Identity and two-hop loss from 0 to 512 writes <= .03.
+- At 512 writes both relation-erasure and second-value-erasure answer drops
+  >= .60; relation counterfactual identity >= .90 and answer >= .85; value
+  counterfactual answer >= .90; unrelated deletion changes <= .02 predictions.
+- Replay agrees with persistence >= .999, serialization agrees exactly,
+  candidate permutation agrees >= .999 and zero-state accuracy <= .08.
+- Ordinary known-name SEM_LATEST >= .98, SEM_2HOP >= .80 and intermediate
+  identity >= .85. On ordinary lexical development require SEM_LATEST >= .95,
+  SEM_2HOP >= .80 and intermediate identity >= .85. Report all eleven families,
+  candidate-set nulls and every denominator without pooling task families.
+
+Freeze all six compositional final checkpoints and development results before
+opening lexical confirmation ONCE. The no-future controls are not required to
+fail, and do not determine opening. If any primary development gate fails, keep
+lexical confirmation sealed, report the failure, and stop this branch. If they
+pass, apply the same gates to fresh lexical confirmation. No thresholds, names,
+training budget, loss weights or architectures change in response to outcomes.
+
+Compare Grounded-Future against Grounded-Ordinary using paired ordinary rows
+and per-seed identity/answer differences, plus lifetime-cluster bootstrap intervals
+(2,000 resamples, seed 20261004). A claim of a material added contribution of the
+explicit challenge/future-use training requires >= .20 absolute ordinary two-hop
+gain in every seed, with paired 95% lower bound above zero. A smaller or absent
+gain is reported, not converted into a necessary-mechanism claim. This comparison
+changes the explicit challenge supervision package, including its language/data
+exposure; it does not isolate a loss term independently of that exposure.
+The existing frozen parse-and-average comparator remains a stronger-structure,
+wider-view comparator; do not portray it as a matched no-pooling training arm.
+
+## Semantic-only recurrent diagnostic
+
+Train one fixed conventional global-GRU architecture at seeds 821/822/823 using
+original-inventory ordinary training data. Event encoder: token/position/type/dt
+embeddings, two Transformer blocks, width 192, six heads; a 384-dimensional GRU
+state and a query-conditioned enum head. No exact-store, protected slot address,
+gold entity, destination or value is an input. All chronological events update
+the GRU. Train SEM_LATEST CE only, batch 128, TBPTT 32, 12,000 updates, AdamW
+3e-4, betas (.9,.95), weight decay .01, clip 1, warmup 300 and linear decay to
+.1 of peak. Final checkpoint only; report the predefined 6,000-update checkpoint
+as a learning-curve observation, never checkpoint selection.
+
+Evaluate on the fresh original-inventory ordinary development worlds with current-
+event (zero carried state), cross-lifetime state-swap, zero/reset state, and
+delay strata. A functional diagnostic positive requires SEM_LATEST >= .80,
+advantage over current-event >= .20, and state-zero drop >= .20 in each seed.
+This is a different architecture/objective/capacity from E1, NOT a rescue of E1
+or an equal-capacity architectural comparison. Either outcome constrains only
+this diagnostic; success is welcome evidence against an impossibility narrative.
+
+## Invariants, execution and stopping
+
+Before optimizers are created, verify the immutable public source hashes,
+train/development/confirmation name separation, shared name-token support,
+absence of reserved names from ordinary AND challenge training, exact solver
+certificates, no token truncation, candidate eligibility/information and
+permutation paths, nonzero writer gradients, sparse replay forward/gradient
+agreement, exact-store semantic firewall, and finite full-size backwards.
+Verify no-future training never calls the challenge generator or loss. For GRU,
+verify chronological hidden state use, semantic-only loss masking and gradients
+to encoder/GRU, with no supervision or structural semantic store in forward.
+Missing endpoint is an error; an unestimable endpoint is NA and blocks a gate.
+
+Use only GPUs 1--4 if idle. Leave GPUs 0/5/6 and other campaigns untouched.
+Sources and generated manifests are hashed before optimizer updates. Large
+artifacts go into a new /raid directory. Runs are append-only. Preserve failed
+instrument attempts; correct an instrument only before admitting affected
+results, with a recorded erratum. Any nonfinite optimization or source mismatch
+stops that run. No extra seeds, continuation, warm starts or outcome-driven tuning.
+Report actual elapsed time and compute before making a delivery-time promise.

rr2/campaign/adjudicate.py

@@ -0,0 +1,70 @@
+"""Fixed primary gate; no absent-key-to-zero conversions."""
+from __future__ import annotations
+
+import argparse
+from runtime import ART, read, write, sha, freeze
+
+
+def assess(split):
+    checks = {}
+    for seed in (821, 822, 823):
+        run = ART / 'runs' / f'compose_future_s{seed}'
+        result = read(run / f'{split}.json')
+        ordinary = result['ordinary']
+        checks[f'{seed}_ordinary_latest'] = ordinary['family_0_acc'] is not None and ordinary['family_0_acc'] >= .95
+        checks[f'{seed}_ordinary_twohop'] = ordinary['family_1_acc'] is not None and ordinary['family_1_acc'] >= .80
+        checks[f'{seed}_ordinary_identity'] = ordinary['twohop_identity'] is not None and ordinary['twohop_identity'] >= .85
+        known = read(run / 'known_development.json')['ordinary']
+        checks[f'{seed}_known_latest'] = known['family_0_acc'] is not None and known['family_0_acc'] >= .98
+        checks[f'{seed}_known_twohop'] = known['family_1_acc'] is not None and known['family_1_acc'] >= .80
+        checks[f'{seed}_known_identity'] = known['twohop_identity'] is not None and known['twohop_identity'] >= .85
+        for n in (16, 32):
+            m = result['challenge'][str(n)]
+            def accuracy(key):
+                item = m[key]
+                if item['accuracy'] is None or item['n'] != 2000:
+                    raise RuntimeError(f'Unestimable or incomplete required endpoint: {key}')
+                return item['accuracy']
+            prefix = f'{seed}_n{n}_'
+            for t in (0, 128, 512):
+                checks[prefix + f'identity_{t}'] = accuracy(f'identity_{t}') >= .90
+                checks[prefix + f'twohop_{t}'] = accuracy(f'twohop_{t}') >= .85 and m[f'twohop_{t}']['wilson95'][0] >= .80
+                checks[prefix + f'onehop_{t}'] = accuracy(f'onehop_{t}') >= .95
+            checks[prefix + 'identity_retention'] = accuracy('identity_0') - accuracy('identity_512') <= .03
+            checks[prefix + 'answer_retention'] = accuracy('twohop_0') - accuracy('twohop_512') <= .03
+            checks[prefix + 'relation_lesion'] = accuracy('twohop_512') - accuracy('relation_zero') >= .60
+            checks[prefix + 'value_lesion'] = accuracy('twohop_512') - accuracy('second_zero') >= .60
+            checks[prefix + 'relation_cf_identity'] = accuracy('relation_cf_identity') >= .90
+            checks[prefix + 'relation_cf_answer'] = accuracy('relation_cf_answer') >= .85
+            checks[prefix + 'value_cf_answer'] = accuracy('value_cf_answer') >= .90
+            checks[prefix + 'unrelated_deletion'] = accuracy('unrelated_changed') <= .02
+            checks[prefix + 'replay_agreement'] = accuracy('replay_agrees') >= .999
+            checks[prefix + 'restore_agreement'] = accuracy('restore_agrees') == 1
+            checks[prefix + 'permutation_agreement'] = accuracy('permutation_agrees') >= .999
+            checks[prefix + 'zero_state'] = accuracy('current_only') <= .08
+    return {'split': split, 'checks': checks, 'all_passed': all(checks.values()),
+            'sources': freeze()['sources'], 'required_endpoint_count': len(checks)}
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--split', choices=('development', 'confirmation'), required=True)
+    a = p.parse_args()
+    gate = assess(a.split)
+    write(ART / f'{a.split.upper()}_GATE.json', gate)
+    if a.split == 'development' and gate['all_passed']:
+        # Require final checkpoints and every development control before opening.
+        for arm in ('future', 'ordinary'):
+            for seed in (821, 822, 823):
+                run = ART / 'runs' / f'compose_{arm}_s{seed}'
+                read(run / 'DONE.json')
+                read(run / 'development.json')
+                read(run / 'known_development.json')
+        write(ART / 'CONFIRMATION_OPENED.json', {'gate_sha256': sha(ART / 'DEVELOPMENT_GATE.json'),
+                                               'data_freeze_sha256': sha(ART / 'DATA_FREEZE_COMPOSE.json'),
+                                               'one_opening_only': True})
+    print({'split': a.split, 'all_passed': gate['all_passed'], 'failed': [k for k, v in gate['checks'].items() if not v]}, flush=True)
+
+
+if __name__ == '__main__':
+    main()

rr2/campaign/evaluate_runs.py

@@ -0,0 +1,163 @@
+"""Fresh ordinary and challenge tests, with explicit missing-endpoint failures."""
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+import numpy as np
+import torch
+from safetensors.torch import load_file
+from runtime import (ART, PUBLIC, FullModel, data, evaluate, published_train,
+                     configure_names, configure_data, freeze, read, write, sha)
+from models import SemanticGRU
+from gates import wilson
+from pns.model.modules import enum_legal_mask
+
+torch.set_num_threads(4)
+
+
+def verify_checkpoint(run):
+    done = read(run / 'DONE.json')
+    path = run / done['checkpoint']
+    if sha(path) != done['sha256']:
+        raise RuntimeError('Checkpoint changed')
+    ck = torch.load(path, map_location='cpu', weights_only=False)
+    if ck['sources'] != freeze()['sources']:
+        raise RuntimeError('Checkpoint source mismatch')
+    return ck, path
+
+
+@torch.no_grad()
+def full(suite, arm, seed, split):
+    freeze()
+    if split == 'confirmation' and not (ART / 'CONFIRMATION_OPENED.json').exists():
+        raise RuntimeError('Lexical confirmation remains sealed')
+    run = ART / 'runs' / f'{suite}_{arm}_s{seed}'
+    if suite == 'original' and arm == 'reference':
+        path = PUBLIC / 'checkpoints' / f'grounded_future_s{seed}.safetensors'
+        expected = read(PUBLIC / 'MANIFEST.json')['files'][str(path.relative_to(PUBLIC))]
+        if sha(path) != expected:
+            raise RuntimeError('Public checkpoint changed')
+        weights = load_file(str(path))
+        run.mkdir(parents=True, exist_ok=True)
+    else:
+        ck, path = verify_checkpoint(run)
+        weights = ck['model']
+    configure_names(None if suite == 'original' else
+                    0 if split == 'known_development' else 1 if split == 'development' else 2)
+    configure_data(suite)
+    model = FullModel('grounded_future').cuda().eval()
+    model.load_state_dict(weights)
+    model.requires_grad_(False)
+    rows = evaluate.ordinary_eval(model, split)
+    np.savez_compressed(run / f'{split}_ordinary_rows.npz', **rows)
+    summary = {'seed': seed, 'suite': suite, 'arm': arm, 'split': split,
+               'checkpoint_sha256': sha(path), 'ordinary': evaluate.summarize(rows), 'challenge': {}}
+    print(json.dumps({'run': run.name, 'split': split, 'ordinary': summary['ordinary']}), flush=True)
+    for n in (16, 32):
+        collected = {}
+        paths = sorted((data.ART / 'challenge' / f'{split}_n{n}').glob('*.npz'))
+        if len(paths) != 125:
+            raise RuntimeError('Required challenge shards missing')
+        for i, p in enumerate(paths):
+            with np.load(p) as z:
+                g = data.device_batch(dict(z), 'cuda')
+            out = evaluate.challenge_batch(model, g)
+            # Paired lifetime identifiers and margins are preserved, not aggregated away.
+            out['world_seed'] = g['world_seed'].cpu().numpy()
+            rel = model.challenge_write(g['reltok'])[torch.arange(len(g['query']), device='cuda'), g['query']][:, None]
+            candidates = model.challenge_write(g['valtok'][:, 0])
+            lg = model.challenge_answer(rel, candidates)[2][:, 0]
+            top = lg.topk(2, dim=-1).values
+            out['margin_at_write'] = (top[:, 0] - top[:, 1]).cpu().numpy()
+            for key, val in out.items():
+                collected.setdefault(key, []).append(val)
+            if i % 25 == 0:
+                print({'run': run.name, 'split': split, 'candidates': n, 'batch': i + 1}, flush=True)
+        result = {k: np.concatenate(v) for k, v in collected.items()}
+        np.savez_compressed(run / f'{split}_n{n}_challenge_rows.npz', **result)
+        metric = {k: {'accuracy': float(v.mean()), 'n': len(v), 'wilson95': wilson(v)}
+                  for k, v in result.items() if k not in ('gold', 'world_seed', 'margin_at_write')}
+        metric['margin_at_write'] = {str(q): float(np.quantile(result['margin_at_write'], q))
+                                     for q in (0, .01, .05, .5, .95, 1)}
+        if arm == 'ordinary':
+            # This classifier was not trained. Retain raw outcomes, mark answer
+            # endpoints NA in the report and do not interpret them as a contrast.
+            for k in metric:
+                if k not in ('margin_at_write',) and not k.startswith('identity_') and k != 'relation_cf_identity':
+                    metric[k]['interpretation'] = 'NA: challenge value head and templates were not trained'
+        summary['challenge'][str(n)] = metric
+    write(run / f'{split}.json', summary)
+    print(json.dumps({'run': run.name, 'evaluation_complete': split}), flush=True)
+
+
+@torch.no_grad()
+def gru(seed, update):
+    freeze()
+    configure_names(None)
+    configure_data('original')
+    run = ART / 'runs' / f'gru_semantic_s{seed}'
+    done = read(run / 'DONE.json')
+    if done['updates'] != 12000:
+        raise RuntimeError('Training incomplete')
+    path = run / f'update_{update:05d}.pt'
+    if update == 12000 and sha(path) != done['sha256']:
+        raise RuntimeError('Final checkpoint changed')
+    ck = torch.load(path, map_location='cpu', weights_only=False)
+    if ck['sources'] != freeze()['sources']:
+        raise RuntimeError('Checkpoint source mismatch')
+    model = SemanticGRU().cuda().eval()
+    model.load_state_dict(ck['model'])
+    rows = {k: [] for k in ('seed', 'event', 'delay', 'correct', 'current_only', 'state_swap', 'reset128')}
+    for batch in data.batches('known_development', 32):
+        g = data.device_batch(batch, 'cuda')
+        b, length = g['etype'].shape
+        state = model.initial_state(b, 'cuda')
+        reset = state.clone()
+        for start in range(0, length, 32):
+            events = model.encode(published_train.trim(g['tok'][:, start:start + 32]),
+                                  g['etype'][:, start:start + 32], g['dt'][:, start:start + 32])
+            for local in range(events.shape[1]):
+                t = start + local
+                if t == 128:
+                    reset.zero_()
+                prior = state
+                state, logits = model.step_encoded(state, events[:, local])
+                reset, reset_logits = model.step_encoded(reset, events[:, local])
+                selected = g['family'][:, t] == 0
+                if not selected.any():
+                    continue
+                legal = enum_legal_mask(g['enum_legal'][:, t])
+                gold = g['enum_gold'][:, t]
+                zero_logits = model.step_encoded(torch.zeros_like(prior), events[:, local])[1]
+                swap_logits = model.step_encoded(prior.roll(1, 0), events[:, local])[1]
+                fields = {'seed': g['seed'], 'event': torch.full((b,), t, device='cuda'),
+                          'delay': g['delay'][:, t],
+                          'correct': logits.masked_fill(~legal, -1e9).argmax(-1) == gold,
+                          'current_only': zero_logits.masked_fill(~legal, -1e9).argmax(-1) == gold,
+                          'state_swap': swap_logits.masked_fill(~legal, -1e9).argmax(-1) == gold,
+                          'reset128': reset_logits.masked_fill(~legal, -1e9).argmax(-1) == gold}
+                for k, v in fields.items():
+                    rows[k].append(v[selected].cpu().numpy())
+    result = {k: np.concatenate(v) for k, v in rows.items()}
+    np.savez_compressed(run / f'development_{update}_rows.npz', **result)
+    summary = {'seed': seed, 'update': update, 'checkpoint_sha256': sha(path), 'n': len(result['correct'])}
+    for k in ('correct', 'current_only', 'state_swap', 'reset128'):
+        summary[k] = {'accuracy': float(result[k].mean()), 'wilson95': wilson(result[k])}
+    summary['delay_strata'] = {}
+    for label, lo, hi in (('le16', 0, 16), ('17to128', 17, 128), ('gt128', 129, 100000)):
+        m = (result['delay'] >= lo) & (result['delay'] <= hi)
+        summary['delay_strata'][label] = {'n': int(m.sum()), 'accuracy': None if not m.any() else float(result['correct'][m].mean())}
+    write(run / f'development_{update}.json', summary)
+    print(json.dumps(summary), flush=True)
+
+
+if __name__ == '__main__':
+    p = argparse.ArgumentParser()
+    p.add_argument('--suite', choices=('original', 'compose', 'gru'), required=True)
+    p.add_argument('--arm', choices=('future', 'ordinary', 'reference'), default='ordinary')
+    p.add_argument('--seed', type=int, choices=(821, 822, 823), required=True)
+    p.add_argument('--split', choices=('known_development', 'development', 'confirmation'), default='known_development')
+    p.add_argument('--update', type=int, choices=(6000, 12000), default=12000)
+    a = p.parse_args()
+    gru(a.seed, a.update) if a.suite == 'gru' else full(a.suite, a.arm, a.seed, a.split)

rr2/campaign/launch.py

@@ -0,0 +1,161 @@
+"""Bounded four-GPU queue, isolated from the user's other research processes."""
+from __future__ import annotations
+
+import json
+import os
+import shutil
+import subprocess
+import sys
+import time
+from pathlib import Path
+from runtime import (ART, HERE, ORIGINAL_ART, source_hashes, name_checks, sha, write, read)
+
+GPUS = (1, 2, 3, 4)
+
+
+def command(script, *args):
+    return [sys.executable, '-u', str(HERE / script), *map(str, args)]
+
+
+def start(cmd, label, gpu=None):
+    env = os.environ.copy()
+    env.update(PYTHONDONTWRITEBYTECODE='1', TOKENIZERS_PARALLELISM='false', PNS_RR2_ART=str(ART))
+    if gpu is not None:
+        env['CUDA_VISIBLE_DEVICES'] = str(gpu)
+    stream = (ART / 'logs' / f'{label}.log').open('x')
+    process = subprocess.Popen(cmd, env=env, stdout=stream, stderr=subprocess.STDOUT,
+                               start_new_session=True)
+    stream.close()
+    print(json.dumps({'started': label, 'pid': process.pid, 'gpu': gpu}), flush=True)
+    return process
+
+
+def checked(process, label):
+    code = process.wait()
+    if code:
+        raise RuntimeError(f'{label} failed ({code}); inspect its preserved log')
+
+
+def validate_original():
+    manifest = read(ORIGINAL_ART / 'DATA.json')
+    results = {}
+    for r in manifest['shards']:
+        if r['path'].startswith('data/train/') or r['path'].startswith('data/preflight/'):
+            p = ORIGINAL_ART / r['path']
+            value = sha(p)
+            if value != r['sha256']:
+                raise RuntimeError(f'Archived data changed: {r["path"]}')
+            results[r['path']] = value
+    write(ART / 'ORIGINAL_DATA_VERIFIED.json', {'manifest_sha256': sha(ORIGINAL_ART / 'DATA.json'),
+                                              'files': results})
+
+
+def compose_data_freeze():
+    paths = [ART / 'compose' / 'DATA.json', ART / 'compose' / 'CHALLENGE_DATA.json']
+    hashes = {p.name: sha(p) for p in paths}
+    files = {}
+    for r in read(paths[0])['shards']:
+        p = ART / 'compose' / r['path']
+        actual = sha(p)
+        if actual != r['sha256']:
+            raise RuntimeError(f'New ordinary data changed: {p}')
+        files[str(p.relative_to(ART))] = actual
+    for r in read(paths[1])['files']:
+        p = ART / r['path']
+        actual = sha(p)
+        if actual != r['sha256']:
+            raise RuntimeError(f'New challenge data changed: {p}')
+        files[r['path']] = actual
+    write(ART / 'DATA_FREEZE_COMPOSE.json', {**hashes, 'files': files, 'sources': source_hashes()})
+
+
+def run_queue(tasks):
+    running = {}
+    failed = []
+    while tasks or running:
+        for gpu, (process, label) in list(running.items()):
+            code = process.poll()
+            if code is not None:
+                print(json.dumps({'finished': label, 'exit_code': code, 'gpu': gpu}), flush=True)
+                if code:
+                    failed.append(label)
+                del running[gpu]
+        for gpu in GPUS:
+            if gpu not in running and tasks:
+                cmd, label = tasks.pop(0)
+                running[gpu] = (start(cmd, label, gpu), label)
+        if running:
+            time.sleep(5)
+    if failed:
+        raise RuntimeError(f'Preserved failed tasks: {failed}; no rescue runs scheduled')
+
+
+def full_tasks(suite, arms, splits, train=True):
+    tasks = []
+    # Training and evaluation are separate stages; evaluation never selects a checkpoint.
+    if train:
+        for arm in arms:
+            for seed in (821, 822, 823):
+                tasks.append((command('train_runs.py', '--suite', suite, '--arm', arm, '--seed', seed),
+                              f'train_{suite}_{arm}_s{seed}'))
+    else:
+        for arm in arms:
+            for seed in (821, 822, 823):
+                for split in splits:
+                    tasks.append((command('evaluate_runs.py', '--suite', suite, '--arm', arm, '--seed', seed, '--split', split),
+                                  f'eval_{suite}_{arm}_s{seed}_{split}'))
+    return tasks
+
+
+def main():
+    if ART.exists():
+        raise FileExistsError(f'Append-only campaign already exists: {ART}')
+    if shutil.disk_usage(ART.parent).free < 15 * 1024**3:
+        raise RuntimeError('Insufficient disk headroom')
+    query = subprocess.check_output(['nvidia-smi', '--query-gpu=index,memory.used,utilization.gpu', '--format=csv,noheader,nounits'], text=True)
+    for line in query.splitlines():
+        index, memory, utilization = map(int, (x.strip() for x in line.split(',')))
+        if index in GPUS and (memory > 128 or utilization > 5):
+            raise RuntimeError(f'GPU {index} is not idle; no other process will be stopped')
+    (ART / 'logs').mkdir(parents=True)
+    write(ART / 'AUTHORIZATION.json', {'date': '2026-10-04', 'user_message':
+          'we can obtaine evidence with additional GPU workload , i am totally fine',
+          'original_campaigns_unchanged': True, 'gpus': GPUS})
+    checks, names = name_checks()
+    if not all(checks.values()):
+        raise RuntimeError(checks)
+    write(ART / 'REGISTRATION.json', {'sources': source_hashes(), 'name_checks': checks, **names})
+    validate_original()
+    write(ART / 'FREEZE.json', {'sources': source_hashes(), 'created_unix': time.time(),
+                               'original_data_verification_sha256': sha(ART / 'ORIGINAL_DATA_VERIFIED.json')})
+    prep_original = start(command('prepare.py', '--phase', 'original'), 'prepare_original')
+    prep_compose = start(command('prepare.py', '--phase', 'compose'), 'prepare_compose')
+    checked(start(command('preflight.py', '--suite', 'original'), 'preflight_original', 1), 'preflight_original')
+    first = full_tasks('original', ('ordinary',), (), train=True)
+    first += [(command('train_runs.py', '--suite', 'gru', '--seed', seed), f'train_gru_s{seed}') for seed in (821, 822, 823)]
+    run_queue(first)
+    checked(prep_original, 'prepare_original')
+    checked(prep_compose, 'prepare_compose')
+    compose_data_freeze()
+    checked(start(command('preflight.py', '--suite', 'compose'), 'preflight_compose', 1), 'preflight_compose')
+    run_queue(full_tasks('compose', ('future', 'ordinary'), (), train=True))
+    tasks = full_tasks('original', ('ordinary', 'reference'), ('known_development',), train=False)
+    tasks += full_tasks('compose', ('future', 'ordinary'), ('known_development', 'development'), train=False)
+    tasks += [(command('evaluate_runs.py', '--suite', 'gru', '--seed', seed, '--update', update),
+               f'eval_gru_s{seed}_u{update}') for seed in (821, 822, 823) for update in (6000, 12000)]
+    run_queue(tasks)
+    checked(start(command('adjudicate.py', '--split', 'development'), 'adjudicate_development'), 'adjudicate_development')
+    if read(ART / 'DEVELOPMENT_GATE.json')['all_passed']:
+        run_queue(full_tasks('compose', ('future', 'ordinary'), ('confirmation',), train=False))
+        checked(start(command('adjudicate.py', '--split', 'confirmation'), 'adjudicate_confirmation'), 'adjudicate_confirmation')
+        outcome = read(ART / 'CONFIRMATION_GATE.json')['all_passed']
+    else:
+        outcome = False
+    write(ART / 'CAMPAIGN_FINISHED.json', {'primary_confirmed': outcome,
+                                         'confirmation_opened': (ART / 'CONFIRMATION_OPENED.json').exists(),
+                                         'time_unix': time.time()})
+    print(json.dumps({'campaign_finished': True, 'primary_confirmed': outcome}), flush=True)
+
+
+if __name__ == '__main__':
+    main()

rr2/campaign/models.py

@@ -0,0 +1,41 @@
+"""The one preregistered semantic-only recurrent diagnostic."""
+from __future__ import annotations
+
+import torch
+from torch import nn
+from runtime import schema
+from pns.model.modules import Block, RecordEncoder
+
+
+class SemanticGRU(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.tok_emb = nn.Embedding(8192, 192)
+        self.position = nn.Parameter(torch.zeros(112, 192))
+        self.encoder = nn.ModuleList(Block(192, 6) for _ in range(2))
+        self.norm = nn.LayerNorm(192)
+        self.event_type = nn.Embedding(len(schema.ET), 192)
+        self.dt = nn.Linear(8, 192)
+        self.recurrence = nn.GRUCell(192, 384)
+        self.answer = nn.Sequential(nn.Linear(576, 384), nn.GELU(),
+                                    nn.Linear(384, len(schema.ENUM_VOCAB)))
+
+    def initial_state(self, batch, device):
+        return torch.zeros(batch, 384, device=device)
+
+    def encode(self, tokens, event_type, dt):
+        shape = tokens.shape[:-1]
+        flat = tokens.reshape(-1, tokens.shape[-1]).long()
+        pad = flat == 0
+        x = self.tok_emb(flat) + self.position[:flat.shape[1]]
+        for block in self.encoder:
+            x = block(x, self_padding_mask=pad)
+        x = self.norm(x)
+        v = x.masked_fill(pad[..., None], 0).sum(1) / (~pad).sum(1, keepdim=True).clamp_min(1)
+        v = v + self.event_type(event_type.reshape(-1).long())
+        v = v + self.dt(RecordEncoder.age_feats(dt.reshape(-1)))
+        return v.reshape(*shape, 192)
+
+    def step_encoded(self, state, event):
+        new = self.recurrence(event, state.to(event.dtype))
+        return new.float(), self.answer(torch.cat([new, event], -1))

rr2/campaign/preflight.py

@@ -0,0 +1,73 @@
+"""Run public invariants unchanged, plus new split and training-route checks."""
+from __future__ import annotations
+
+import argparse
+import importlib.util
+import inspect
+import time
+import torch
+from torch.nn import functional as F
+from runtime import (ART, PUBLIC, ORIGINAL_ART, data, configure_names, configure_data,
+                     source_hashes, name_checks, read, write)
+from models import SemanticGRU
+from pns.model.modules import enum_legal_mask
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--suite', choices=('original', 'compose'), required=True)
+    args = parser.parse_args()
+    checks, names = name_checks()
+    if not all(checks.values()):
+        raise RuntimeError(checks)
+    configure_names(0 if args.suite == 'compose' else None)
+    configure_data('original_train' if args.suite == 'original' else 'compose')
+    folder = ART / 'preflight' / args.suite
+    folder.mkdir(parents=True, exist_ok=True)
+    # Only metadata is copied. Evaluations use preflight worlds, never held-out rows.
+    write(folder / 'DATA.json', read(data.ART / 'DATA.json'))
+    spec = importlib.util.spec_from_file_location('deposited_rr2_preflight', PUBLIC / 'source/campaigns/e8_full/preflight.py')
+    pf = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(pf)
+    pf.ART = folder
+    pf.sources = source_hashes
+    pf.main()
+    write(folder / 'NAME_PREFLIGHT.json', {'checks': checks, **names, 'all_passed': all(checks.values())})
+    if args.suite == 'original':
+        torch.manual_seed(20261004)
+        model = SemanticGRU().cuda().train()
+        g = data.device_batch(next(data.batches('preflight', 128)), 'cuda')
+        # Archived preflight has 96 rows, not a training update.
+        tokens = g['tok'][:, :32]
+        events = model.encode(tokens, g['etype'][:, :32], g['dt'][:, :32])
+        state = model.initial_state(len(events), 'cuda')
+        logits = []
+        for t in range(32):
+            state, out = model.step_encoded(state, events[:, t])
+            logits.append(out)
+        out = torch.stack(logits, 1)
+        selected = g['family'][:, :32] == 0
+        legal = enum_legal_mask(g['enum_legal'][:, :32][selected])
+        gold = g['enum_gold'][:, :32][selected]
+        loss = F.cross_entropy(out[selected].masked_fill(~legal, -1e9), gold)
+        loss.backward()
+        recurrent_checks = {
+            'only_tokens_type_dt_in_encoder': list(inspect.signature(model.encode).parameters) == ['tokens', 'event_type', 'dt'],
+            'only_state_and_event_in_step': list(inspect.signature(model.step_encoded).parameters) == ['state', 'event'],
+            'semantic_latest_mask_has_rows': bool(selected.any()),
+            'gru_gradient_nonzero': float(model.recurrence.weight_hh.grad.norm()) > 0,
+            'encoder_gradient_nonzero': float(model.tok_emb.weight.grad.norm()) > 0,
+            'all_gradients_finite': all(torch.isfinite(p.grad).all() for p in model.parameters() if p.grad is not None),
+            'chronological_state_changes_output': not torch.allclose(model.step_encoded(state, events[:, -1])[1],
+                                                                     model.step_encoded(torch.zeros_like(state), events[:, -1])[1]),
+            'no_optimizer_created': True,
+        }
+        write(ART / 'preflight' / 'gru.json', {'checks': recurrent_checks, 'all_passed': all(recurrent_checks.values()),
+                                             'parameters': sum(p.numel() for p in model.parameters()), 'optimizer_updates': 0})
+        if not all(recurrent_checks.values()):
+            raise RuntimeError(recurrent_checks)
+    print({'suite': args.suite, 'all_passed': True}, flush=True)
+
+
+if __name__ == '__main__':
+    main()

rr2/campaign/prepare.py

@@ -0,0 +1,88 @@
+"""Generate new name-partitioned worlds without touching original artifacts."""
+from __future__ import annotations
+
+import argparse
+from concurrent.futures import ProcessPoolExecutor
+from pathlib import Path
+import numpy as np
+from runtime import ART, data, configure_names, name_checks, source_hashes, write, sha
+
+
+def ordinary_job(job):
+    suite, split, start, count, panel, partition = job
+    configure_names(partition)
+    data.ART = ART / suite
+    result = data.build((split, start, count, panel))
+    result.update(suite=suite, partition=partition)
+    return result
+
+
+def challenge_job(job):
+    suite, split, base, count, n, partition = job
+    configure_names(partition)
+    folder = ART / suite / 'challenge' / f'{split}_n{n}'
+    folder.mkdir(parents=True, exist_ok=True)
+    path = folder / f'batch_{base}.npz'
+    if path.exists():
+        raise FileExistsError(path)
+    generated = data.challenge(base, count, n=n, futures=1, interference=512)
+    np.savez_compressed(path, **generated)
+    return {'path': str(path.relative_to(ART)), 'sha256': sha(path), 'n': count,
+            'candidates': n, 'partition': partition, 'suite': suite, 'split': split}
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--workers', type=int, default=24)
+    parser.add_argument('--phase', choices=('original', 'compose'), required=True)
+    args = parser.parse_args()
+    checks, names = name_checks()
+    if not all(checks.values()):
+        raise RuntimeError(checks)
+    if not (ART / 'REGISTRATION.json').exists():
+        write(ART / 'REGISTRATION.json', {'sources': source_hashes(), 'name_checks': checks, **names})
+    suite = args.phase
+    jobs = []
+    specs = [('preflight', 92300000, 96, False, None),
+             ('known_development', 92400000, 2000, False, None)]
+    if suite == 'compose':
+        specs = [('train', 92000000, 120000, False, 0),
+                 ('preflight', 92300000, 96, False, 0),
+                 ('known_development', 92400000, 2000, False, 0),
+                 ('development', 92500000, 2000, False, 1),
+                 ('confirmation', 92900000, 2000, False, 2),
+                 ('development_long', 92510000, 256, True, 1),
+                 ('confirmation_long', 92910000, 256, True, 2)]
+    for split, base, count, panel, partition in specs:
+        for offset in range(0, count, 256):
+            jobs.append((suite, split, base + offset, min(256, count - offset), panel, partition))
+    reports = []
+    with ProcessPoolExecutor(max_workers=args.workers) as pool:
+        for i, report in enumerate(pool.map(ordinary_job, jobs)):
+            reports.append(report)
+            if i % 10 == 0:
+                print({'phase': suite, 'ordinary_shards': i + 1, 'total': len(jobs)}, flush=True)
+    digests = [d for report in reports for d in report['digests']]
+    if len(set(digests)) != len(digests):
+        raise RuntimeError('Duplicate ordinary content')
+    write(ART / suite / 'DATA.json', {'shards': reports, 'content_duplicates': 0,
+                                     'split_protocol': 'PREREGISTRATION.md'})
+    conditions = [('known_development', 96 * 10**12, 0 if suite == 'compose' else None)]
+    if suite == 'compose':
+        conditions += [('development', 97 * 10**12, 1), ('confirmation', 98 * 10**12, 2)]
+    challenge_jobs = [(suite, split, base + (n == 32) * 100000 + offset,
+                       min(16, 2000 - offset), n, part)
+                      for split, base, part in conditions for n in (16, 32)
+                      for offset in range(0, 2000, 16)]
+    challenge_reports = []
+    with ProcessPoolExecutor(max_workers=args.workers) as pool:
+        for i, report in enumerate(pool.map(challenge_job, challenge_jobs)):
+            challenge_reports.append(report)
+            if i % 30 == 0:
+                print({'phase': suite, 'challenge_batches': i + 1, 'total': len(challenge_jobs)}, flush=True)
+    write(ART / suite / 'CHALLENGE_DATA.json', {'files': challenge_reports})
+    print({'prepared': suite, 'ordinary_lifetimes': sum(r['n'] for r in reports)}, flush=True)
+
+
+if __name__ == '__main__':
+    main()

rr2/campaign/runtime.py

@@ -0,0 +1,133 @@
+"""Isolated RR2 execution against the immutable paper-v2.0 source release."""
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+import sys
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+ART = Path(os.environ.get('PNS_RR2_ART', '/raid/nurgaly/datasets/pns/reviewer_rr2_20261004'))
+PUBLIC = Path('/raid/nurgaly/datasets/pns/release/pns-hf-e8-20260911/model/e8_full')
+ORIGINAL_ART = Path('/raid/nurgaly/datasets/pns/release/pns-e8-full-paper-20260910/PNS_experiment/campaigns/e8_full/artifacts')
+sys.dont_write_bytecode = True
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+os.environ['PNS_TOKENIZER'] = str(PUBLIC / 'source/tokenizer/tokenizer.json')
+os.environ['PNS_GOLD'] = 'v2'
+sys.path.insert(0, str(PUBLIC / 'source/campaigns/e8_full'))
+import common
+import data
+import evaluate
+import train as published_train
+from model import FullModel
+from pns.data.tokenize import load_tokenizer
+from pns.world import schema
+
+LEFT = ('Zavi', 'Pemo', 'Nori', 'Feta', 'Rulo', 'Gumi', 'Helo', 'Kora', 'Suni', 'Vepi', 'Wado', 'Yaku')
+RIGHT = LEFT + ('Jomi', 'Celu', 'Ami', 'Oru', 'Umo', 'Qela')
+KINDS = ('PERSON_NAMES', 'PROJECT_NAMES', 'SHIPMENT_NAMES', 'VAULT_NAMES', 'DEVICE_NAMES')
+SIZES = (26, 14, 10, 8, 8)
+ORIGINAL_NAMES = tuple(n for kind in KINDS for n in getattr(schema, kind))
+
+
+def sha(path):
+    h = hashlib.sha256()
+    with Path(path).open('rb') as stream:
+        for chunk in iter(lambda: stream.read(1 << 20), b''):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def write(path, obj):
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open('x') as stream:
+        json.dump(obj, stream, indent=2, sort_keys=True, allow_nan=False)
+        stream.write('\n')
+
+
+def read(path):
+    return json.loads(Path(path).read_text())
+
+
+def inventories():
+    import random
+    result = []
+    for split in range(3):
+        names = [a + '-' + b for i, a in enumerate(LEFT) for j, b in enumerate(RIGHT)
+                 if (i + j) % 3 == split]
+        random.Random(20261004 + split).shuffle(names)
+        result.append(tuple(names[:66]))
+    return tuple(result)
+
+
+INVENTORIES = inventories()
+
+
+def configure_names(partition=None):
+    names = ORIGINAL_NAMES if partition is None else INVENTORIES[partition]
+    offset = 0
+    # Mutate lists, not module files. Imported simulator references remain valid.
+    for kind, size in zip(KINDS, SIZES):
+        getattr(schema, kind)[:] = names[offset:offset + size]
+        offset += size
+    data.NAMES = list(names[:64])
+    evaluate.NAMES = list(names[:64])
+    return names
+
+
+def source_hashes():
+    manifest = read(PUBLIC / 'MANIFEST.json')['files']
+    public_hashes = {}
+    for path in sorted((PUBLIC / 'source').rglob('*')):
+        if not path.is_file() or (path.suffix != '.py' and path.name != 'tokenizer.json'):
+            continue
+        key = str(path.relative_to(PUBLIC))
+        actual = sha(path)
+        if actual != manifest[key]:
+            raise RuntimeError(f'Immutable public source mismatch: {key}')
+        public_hashes[key] = actual
+    return {'campaign': {p.name: sha(p) for p in sorted(HERE.glob('*.py'))},
+            'preregistration': sha(HERE / 'PREREGISTRATION.md'), 'public': public_hashes}
+
+
+def freeze():
+    frozen = read(ART / 'FREEZE.json')
+    if frozen['sources'] != source_hashes():
+        raise RuntimeError('Campaign source changed after freeze')
+    return frozen
+
+
+def configure_data(suite):
+    data.ART = ORIGINAL_ART if suite == 'original_train' else ART / suite
+
+
+def checkpoint(path, obj):
+    # Reuse the deposited append-only atomic checkpoint writer.
+    common.checkpoint(path, obj)
+
+
+def name_checks():
+    import collections
+    tok = load_tokenizer()
+    encoded = [[tok.encode(name).ids for name in names] for names in INVENTORIES]
+    challenge_encoded = [[tok.encode(name).ids for name in names[:64]] for names in INVENTORIES]
+    support = [set(x for row in rows for x in row) for rows in encoded]
+    challenge_support = [set(x for row in rows for x in row) for rows in challenge_encoded]
+    checks = {
+        'three_disjoint_name_inventories': all(not set(INVENTORIES[i]) & set(INVENTORIES[j])
+                                             for i in range(3) for j in range(i)),
+        'exactly_66_names_each': all(len(set(n)) == 66 for n in INVENTORIES),
+        'all_names_exactly_seven_pieces': all(len(row) == 7 for rows in encoded for row in rows),
+        'shared_name_token_support': all(s == support[0] for s in support),
+        'challenge_shared_name_token_support': all(s == challenge_support[0] for s in challenge_support),
+        'each_component_occurs_each_split': all(all(any(name.startswith(a + '-') for name in names) for a in LEFT)
+                                                   and all(any(name.endswith('-' + b) for name in names) for b in RIGHT)
+                                                   for names in INVENTORIES),
+        'inventories_disjoint_from_original_names': all(not set(n) & set(ORIGINAL_NAMES) for n in INVENTORIES),
+    }
+    return checks, {'inventories': INVENTORIES, 'original_inventory': ORIGINAL_NAMES,
+                    'length_counts': [dict(collections.Counter(map(len, rows))) for rows in encoded],
+                    'token_support': [sorted(s) for s in support],
+                    'challenge_token_support': [sorted(s) for s in challenge_support]}

rr2/campaign/train_runs.py

@@ -0,0 +1,174 @@
+"""Fixed-budget new runs; no tuning or reuse of old campaign verdicts."""
+from __future__ import annotations
+
+import argparse
+import json
+import time
+import numpy as np
+import torch
+from torch.nn import functional as F
+from runtime import (ART, ORIGINAL_ART, FullModel, data, published_train, configure_names,
+                     configure_data, freeze, read, write, checkpoint, sha)
+from models import SemanticGRU
+from pns.model.modules import enum_legal_mask
+
+torch.set_num_threads(4)
+
+
+def full(suite, arm, seed):
+    frozen = freeze()
+    pf = read(ART / 'preflight' / suite / 'PREFLIGHT.json')
+    if not pf['all_passed']:
+        raise RuntimeError('Preflight failed')
+    configure_names(0 if suite == 'compose' else None)
+    configure_data('original_train' if suite == 'original' else 'compose')
+    if arm not in ('future', 'ordinary'):
+        raise ValueError(arm)
+    manifest = data.ART / 'DATA.json'
+    if suite == 'compose':
+        expected = read(ART / 'DATA_FREEZE_COMPOSE.json')
+        if sha(manifest) != expected['DATA.json']:
+            raise RuntimeError('Data manifest changed')
+    torch.manual_seed(seed)
+    model = FullModel('grounded_future').cuda().train()
+    opt = torch.optim.AdamW(model.parameters(), lr=4e-4, betas=(.9, .95), weight_decay=.01)
+    run = ART / 'runs' / f'{suite}_{arm}_s{seed}'
+    run.mkdir(parents=True, exist_ok=False)
+    write(run / 'START.json', {'suite': suite, 'arm': arm, 'seed': seed, 'time': time.time(),
+                              'sources': frozen['sources'], 'training_data_manifest': sha(manifest),
+                              'parameters': sum(p.numel() for p in model.parameters()),
+                              'gpu': torch.cuda.get_device_name(), 'challenge_calls': 0})
+    updates = calls = 0
+    started = time.monotonic()
+    with (run / 'train.jsonl').open('x') as log:
+        for batch_no, batch in enumerate(data.batches('train', 96, seed)):
+            g = data.device_batch(batch, 'cuda')
+            state = model.initial_state(96, 'cuda')
+            authority = torch.zeros(96, 192, dtype=torch.bool, device='cuda')
+            for start in range(0, 256, 32):
+                if updates == 6000:
+                    break
+                lr = 4e-4 * (min(1, (updates + 1) / 300) if updates < 300 else
+                             .1 + .9 * max(0, 1 - (updates - 300) / 5700))
+                for group in opt.param_groups:
+                    group['lr'] = lr
+                opt.zero_grad(set_to_none=True)
+                with torch.autocast('cuda', dtype=torch.bfloat16):
+                    state, authority, ordinary, parts = published_train.ordinary_chunk(
+                        model, g, state, authority, start, start + 32)
+                if not torch.isfinite(ordinary):
+                    raise RuntimeError('Nonfinite ordinary loss')
+                ordinary.backward()
+                state = state.detach()
+                challenge_loss = None
+                if arm == 'future':
+                    cg = data.device_batch(data.challenge(95 * 10**12 + seed * 10**7 + updates * 100, 24), 'cuda')
+                    calls += 1
+                    with torch.autocast('cuda', dtype=torch.bfloat16):
+                        challenge_loss, _ = model.challenge_loss(cg)
+                    if not torch.isfinite(challenge_loss):
+                        raise RuntimeError('Nonfinite challenge loss')
+                    challenge_loss.backward()
+                norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 1, error_if_nonfinite=True)
+                opt.step()
+                updates += 1
+                if updates == 1 or updates % 100 == 0:
+                    row = {'update': updates, 'seconds': time.monotonic() - started,
+                           'ordinary_loss': float(ordinary.detach()),
+                           'challenge_loss': None if challenge_loss is None else float(challenge_loss.detach()),
+                           'ordinary_parts': {k: float(v.detach()) for k, v in parts.items()},
+                           'grad_norm': float(norm), 'lr': lr, 'challenge_calls': calls,
+                           'peak_cuda_bytes': torch.cuda.max_memory_allocated()}
+                    log.write(json.dumps(row, allow_nan=False) + '\n')
+                    log.flush()
+                    print(json.dumps({'run': run.name, **row}), flush=True)
+            if updates == 6000:
+                break
+    if updates != 6000 or calls != (6000 if arm == 'future' else 0):
+        raise RuntimeError('Budget or challenge-call invariant failed')
+    freeze()
+    final = run / 'final.pt'
+    checkpoint(final, {'model': model.state_dict(), 'optimizer': opt.state_dict(),
+                       'update': updates, 'suite': suite, 'arm': arm, 'seed': seed,
+                       'sources': frozen['sources'], 'training_data_manifest': sha(manifest)})
+    write(run / 'DONE.json', {'updates': updates, 'seconds': time.monotonic() - started,
+                             'checkpoint': final.name, 'sha256': sha(final), 'challenge_calls': calls,
+                             'parameters': sum(p.numel() for p in model.parameters())})
+
+
+def gru(seed):
+    frozen = freeze()
+    if not read(ART / 'preflight' / 'gru.json')['all_passed']:
+        raise RuntimeError('GRU preflight failed')
+    configure_names(None)
+    configure_data('original_train')
+    torch.manual_seed(seed)
+    model = SemanticGRU().cuda().train()
+    opt = torch.optim.AdamW(model.parameters(), lr=3e-4, betas=(.9, .95), weight_decay=.01)
+    run = ART / 'runs' / f'gru_semantic_s{seed}'
+    run.mkdir(parents=True, exist_ok=False)
+    write(run / 'START.json', {'seed': seed, 'time': time.time(), 'sources': frozen['sources'],
+                              'parameters': sum(p.numel() for p in model.parameters()),
+                              'training_data_manifest': sha(ORIGINAL_ART / 'DATA.json')})
+    updates = 0
+    started = time.monotonic()
+    with (run / 'train.jsonl').open('x') as log:
+        for batch in data.batches('train', 128, seed):
+            g = data.device_batch(batch, 'cuda')
+            state = model.initial_state(128, 'cuda')
+            for start in range(0, 256, 32):
+                if updates == 12000:
+                    break
+                lr = 3e-4 * (min(1, (updates + 1) / 300) if updates < 300 else
+                             .1 + .9 * max(0, 1 - (updates - 300) / 11700))
+                for group in opt.param_groups:
+                    group['lr'] = lr
+                opt.zero_grad(set_to_none=True)
+                with torch.autocast('cuda', dtype=torch.bfloat16):
+                    tokens = published_train.trim(g['tok'][:, start:start + 32])
+                    events = model.encode(tokens, g['etype'][:, start:start + 32], g['dt'][:, start:start + 32])
+                    logits = []
+                    for local in range(32):
+                        state, out = model.step_encoded(state, events[:, local])
+                        logits.append(out)
+                    stacked = torch.stack(logits, 1)
+                    selected = g['family'][:, start:start + 32] == 0
+                    if not selected.any():
+                        raise RuntimeError('SEM_LATEST batch has no supervision')
+                    legal = enum_legal_mask(g['enum_legal'][:, start:start + 32][selected])
+                    gold = g['enum_gold'][:, start:start + 32][selected]
+                    loss = F.cross_entropy(stacked[selected].float().masked_fill(~legal, -1e9), gold)
+                if not torch.isfinite(loss):
+                    raise RuntimeError('Nonfinite GRU loss')
+                loss.backward()
+                state = state.detach()
+                norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 1, error_if_nonfinite=True)
+                opt.step()
+                updates += 1
+                if updates == 1 or updates % 100 == 0:
+                    row = {'update': updates, 'loss': float(loss.detach()), 'seconds': time.monotonic() - started,
+                           'train_accuracy': float((stacked[selected].masked_fill(~legal, -1e9).argmax(-1) == gold).float().mean()),
+                           'n_latest': int(selected.sum()), 'grad_norm': float(norm), 'lr': lr,
+                           'peak_cuda_bytes': torch.cuda.max_memory_allocated()}
+                    log.write(json.dumps(row, allow_nan=False) + '\n')
+                    log.flush()
+                    print(json.dumps({'run': run.name, **row}), flush=True)
+                if updates in (6000, 12000):
+                    checkpoint(run / f'update_{updates:05d}.pt', {'model': model.state_dict(),
+                               'update': updates, 'seed': seed, 'sources': frozen['sources']})
+            if updates == 12000:
+                break
+    freeze()
+    final = run / 'update_12000.pt'
+    write(run / 'DONE.json', {'updates': updates, 'seconds': time.monotonic() - started,
+                             'checkpoint': final.name, 'sha256': sha(final),
+                             'parameters': sum(p.numel() for p in model.parameters())})
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--suite', choices=('original', 'compose', 'gru'), required=True)
+    parser.add_argument('--arm', choices=('future', 'ordinary'), default='ordinary')
+    parser.add_argument('--seed', type=int, choices=(821, 822, 823), required=True)
+    args = parser.parse_args()
+    gru(args.seed) if args.suite == 'gru' else full(args.suite, args.arm, args.seed)

rr2/checkpoints/compose_future_s821/DONE.json

@@ -0,0 +1,9 @@
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+  "updates": 6000
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+    "evidence/known16_s821/BEFORE_PREDICTIONS.json": "0adacda636140a64de3008415bb420a997f76864b38319c20f750633b8342d4a",
+    "evidence/known16_s821/RESULTS.json": "a69bd339db2e991a81cfc10762b25a70299195f9ba72d7d517e35eef5d3a08e2",
+    "evidence/known16_s821/rows.npz": "4cf118421d1c64e93d6fe2784df39c708f72673118f4dc36b770b835d54c667e",
+    "evidence/known16_s821_mean/BEFORE_PREDICTIONS.json": "3a503c5c415d944fbb4755a18c4c63dedda3f604cfe980bf07aa28be0b8186de",
+    "evidence/known16_s821_mean/RESULTS.json": "01747e5c565c82f909ab7952249816362d1160cb1b5de2c715b924bc7b0675ce",
+    "evidence/known16_s821_mean/rows.npz": "9508a66668934d3be66337b1a3024fd8840a9c46fe06ed8cf4e337b4f86646b4",
+    "evidence/known16_s822/BEFORE_PREDICTIONS.json": "fc61f9435a3cdcdc02e13ebd7c6c3a8ab8864400a8531c1cc93d8728b8183feb",
+    "evidence/known16_s822/RESULTS.json": "3966db6b2794bcc44400a96e4aa8194ecfc3e9b000349c28aad74327a4ac33ae",
+    "evidence/known16_s822/rows.npz": "61a61fdd17a5a0b2e38afc13920ee13260ef08a1692b29f325cb878c65681ff7",
+    "evidence/known16_s822_mean/BEFORE_PREDICTIONS.json": "61714d2384c12a75aae31b24b92ba718271ffe1190ada51ca0a4c769f48e830f",
+    "evidence/known16_s822_mean/RESULTS.json": "851302a1518a1940a59c1221a456e68c17d70443b1ab10e262ed39bbf5e958f4",
+    "evidence/known16_s822_mean/rows.npz": "8e6b7dd065b73f30235f68f74f28307044b21f5aae924f27c8402279f8c1bde8",
+    "evidence/known16_s823/BEFORE_PREDICTIONS.json": "0c7057efdec4a89593d158d1be2117d271e32c1533e3c6471d635fe851b0a1ef",
+    "evidence/known16_s823/RESULTS.json": "84641f7b6eacae229a7e6588c878dac4952b3aae2d834b35ad7d0a53cc087b5b",
+    "evidence/known16_s823/rows.npz": "1c11778f16cea2b7c7d611c47fb37bef6c0c84c4a3e2b99a3cae1a80ae46109e",
+    "evidence/known16_s823_mean/BEFORE_PREDICTIONS.json": "86c4d56e67c29e01ec440d9d19831d422e3b639b8dc4e2fe70de64eaf6d534ee",
+    "evidence/known16_s823_mean/RESULTS.json": "100d391d2b653df2f77e66b41e2244ece5d41ef6381f00a66b940a4ed113b83b",
+    "evidence/known16_s823_mean/rows.npz": "e4238f3f5cce539a9433ae8ffea86f4d02bc46d50737ef5a0bc8464b6184ff15",
+    "evidence/known32_s821/BEFORE_PREDICTIONS.json": "cf1eb4d7c03e4e34576a7fbc02dd99513f42033aa01a6625097ef39b23e7300f",
+    "evidence/known32_s821/RESULTS.json": "669233b7fb4768cb7a647034e5d4f6f19bdb3c1fd0e4d07fe7cada0f777236b5",
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+    "evidence/known32_s821_mean/BEFORE_PREDICTIONS.json": "ce70dbe128065657ca7a1574e93c517dc14f92387e8024afc413e8968c69d04e",
+    "evidence/known32_s821_mean/RESULTS.json": "3643671ddceb45465d5ae395f2e555d23640716932fbfd4012ad77c92e91c448",
+    "evidence/known32_s821_mean/rows.npz": "854329532e1d4776ca78912c7e0dc815791536cfd8707c96a79b1997e7d119e2",
+    "evidence/known32_s822/BEFORE_PREDICTIONS.json": "1601cb7899e9d383767fee074b7757f7763220e5cb045ed7c1f9c6370fe1ab02",
+    "evidence/known32_s822/RESULTS.json": "c2e602204f3c910b40d6c211e4fdd0fe912cb416d10d9fc40cc607315bfad305",
+    "evidence/known32_s822/rows.npz": "5b6ddb17f00a17dba82a53703a65d82d2d8af75fd33066ce8ee1b1204219866d",
+    "evidence/known32_s822_mean/BEFORE_PREDICTIONS.json": "5d25ff3594ad255febc2f0f03f513d219015c36bb43fa2761d456a9ddb5aec6a",
+    "evidence/known32_s822_mean/RESULTS.json": "4c6a317a6799bbf765032cdf1549fda936b1f0eca37318cc6730eb227b840f86",
+    "evidence/known32_s822_mean/rows.npz": "02528872b3bc993ced05e3f972073f1ace31b7a269981dcaabcdec519d3888c3",
+    "evidence/known32_s823/BEFORE_PREDICTIONS.json": "fd3f303e60a849f6ae560cf655f7f312d4e6f593827685dda852fbc579db42e6",
+    "evidence/known32_s823/RESULTS.json": "6dc09ae424dde7550d019a3e0156dff1bac045fb02cdd48d292f7857539deba8",
+    "evidence/known32_s823/rows.npz": "6670f832a8c89cbee6348326f723163ac9bd6f37e6b343c5f4af8ee440091960",
+    "evidence/known32_s823_mean/BEFORE_PREDICTIONS.json": "7a8e6841092a07ab07cf208bd1724a43c578410238cb32ac875f32f89fab0b50",
+    "evidence/known32_s823_mean/RESULTS.json": "2edf4e66d809ce166696dce79a64e99aad03082ee399176ec8eed23a7625bd30",
+    "evidence/known32_s823_mean/rows.npz": "03daa205d08174af2e2fc9a5b87d7f878de1b352d126bd34c5fb74aec00279e1",
+    "evidence/lexical_inventory_stats.json": "fd3763b3b3f15816ff5e357824f4d88496d7f5c2ccda60e79dda674049323208",
+    "evidence/recovered/name_counts.json": "29994ccc33059de8b7a5b322f79329a7a37d06b33d6a28d6b1ed138412fb2eba",
+    "evidence/repository_paths.json": "fd0752f836554186b05134d2831b3d715e38c52598957c3adf98e15538ee83f4",
+    "evidence/unseen16_s821/BEFORE_PREDICTIONS.json": "b81654e5616911923f5d7ca055416909929c038b65ecb23533a2aa44fced0002",
+    "evidence/unseen16_s821/RESULTS.json": "599003d1f1d78efa793f0c4e438fc2c85942f7c8664f49583fc77188ba3c0330",
+    "evidence/unseen16_s821/rows.npz": "20d737e9b80a48045b8736cd8089ac781eb715e3234ef5145843f51704f9549c",
+    "evidence/unseen16_s821_mean/BEFORE_PREDICTIONS.json": "15a27c4dc93956e3b8f9626c52eeb3813aa983d38f60c491e8b6318823649175",
+    "evidence/unseen16_s821_mean/RESULTS.json": "62be753452e45ec1f6e5d851d241379ebfcb23a0aaaafaaf5241dbadf9f404b0",
+    "evidence/unseen16_s821_mean/rows.npz": "63857f6f3c6ea3e6abb3e23afc2bfce7e90d7e6f5d29c6c9c6c9418b08e80ac4",
+    "evidence/unseen16_s822/BEFORE_PREDICTIONS.json": "a2ed691aca436efdc6031af0636fdd64c0f2462363820439ff1bb3745c98ecfd",
+    "evidence/unseen16_s822/RESULTS.json": "0696d2e1075064780c26d93a24d81409588df5d984ae2b2a3c32706ac437730e",
+    "evidence/unseen16_s822/rows.npz": "ef2b451302301b62ed596560505e6ca825b59b29c32b782c90668c8f42148653",
+    "evidence/unseen16_s822_mean/BEFORE_PREDICTIONS.json": "a35cb44ce2e94f06fc7e10f95cce2858bf61e389ce1e65d8b0712125400504c5",
+    "evidence/unseen16_s822_mean/RESULTS.json": "4b40b0c5cb45640e8bb9f24b5141ced792c0a53bc00f09c5e83363f2cf8e71c7",
+    "evidence/unseen16_s822_mean/rows.npz": "16cc7877ce8457aa522776367c5f121ba313b07e4817eceec849a7e713922151",
+    "evidence/unseen16_s823/BEFORE_PREDICTIONS.json": "18149919a860c7dbc774782aa6d6b79359279d06fc91834a949ffb66cb22e759",
+    "evidence/unseen16_s823/RESULTS.json": "e76d9d083b142249ceb6295ae48d87c94e0f0e01d2a330806a3fbbb71dca9adb",
+    "evidence/unseen16_s823/rows.npz": "c9a69fd184a9c2de3624687f101f919c8f1c8d06df1cbdbb647f1ffa9b26dd20",
+    "evidence/unseen16_s823_mean/BEFORE_PREDICTIONS.json": "3e67f0c8efcc172bb5d537b717e06c4c901cf578fe26223d9f8fadfee2ab6433",
+    "evidence/unseen16_s823_mean/RESULTS.json": "002907b4d87aacf23b33e72b1e687fb9dc1281a6b49124eb93d300ddc85ae56f",
+    "evidence/unseen16_s823_mean/rows.npz": "e5cb0bec2b406bf54482da5e92b27713d1fd724b7b49867dd31dc085dc05e4dd",
+    "evidence/unseen32_s821/BEFORE_PREDICTIONS.json": "ee7936f05947458ea8a43dc2bf144791a436dd0220946f5a0e905d6a478b6978",
+    "evidence/unseen32_s821/RESULTS.json": "6618cfd30285b7d3aaab26b900377bce3a317d6709da5ab6f5d31cf8d634fbe7",
+    "evidence/unseen32_s821/rows.npz": "7b233f8c5f77732456e21db5df96c4ed554e82a2dc62ba0d31be643c57f11689",
+    "evidence/unseen32_s821_mean/BEFORE_PREDICTIONS.json": "f16d1822f229576210638a00350d0fc610c0e5b0c8816f6e69792ed47627e00c",
+    "evidence/unseen32_s821_mean/RESULTS.json": "f4984b0b844397568f5bb36b379ac4ddcf5242d0a000d43e60dd684d49afe6a3",
+    "evidence/unseen32_s821_mean/rows.npz": "568aa2138346a0e154fc81972e27c059d0cd2f7addd31d31c21e90c7df209f06",
+    "evidence/unseen32_s822/BEFORE_PREDICTIONS.json": "c6d3c340a63904393aad6a44b3fc689f9b2aed8e4748ba326f03e1c04c28e26c",
+    "evidence/unseen32_s822/RESULTS.json": "97bc418f47456ca6b98df16190acd4ed5ce5376b102d2080704156a5b961d2ca",
+    "evidence/unseen32_s822/rows.npz": "ba361ca6f44fb1e19bbce7998ab7a9639263c3fe2a38ca99be36e0ec229ea25d",
+    "evidence/unseen32_s822_mean/BEFORE_PREDICTIONS.json": "88efdd6c27fb25bda21803968b8143a0ae34d59a322f27af4eb6642122e61b68",
+    "evidence/unseen32_s822_mean/RESULTS.json": "eb4525a8fd282403166c4c04f49f5339dc44374e11fb317bfd05a4f5cf69fc61",
+    "evidence/unseen32_s822_mean/rows.npz": "1b9798ddd4b4936799a42b84ca80763f180f81f4eed928102a5e8f26da794696",
+    "evidence/unseen32_s823/BEFORE_PREDICTIONS.json": "f253db56ec1240795d4954f1ba234ed23c64c21dc126ac084064d7566bf7de91",
+    "evidence/unseen32_s823/RESULTS.json": "d3e98b591f7e10ae1392a73c7a2027be7eb5900e915d7ad6ce125e756ab92004",
+    "evidence/unseen32_s823/rows.npz": "5837bb11f60d7d06cefce2a0b4010ff67754b9213daffba5a62bd3200814397f",
+    "evidence/unseen32_s823_mean/BEFORE_PREDICTIONS.json": "209b40e20394c15d3e2ba3615c74720c128906a48c29d3295b15787017efcc61",
+    "evidence/unseen32_s823_mean/RESULTS.json": "35d6c4f2be4a42828d09e8a91dacd217f6ad4de7168ed359b4d22abb3a1fc6b9",
+    "evidence/unseen32_s823_mean/rows.npz": "ad72a2f5a7dbf65ab89213d575e72775149f70c125f1ad720821c690cd5626ea",
+    "experiment_rr2.md": "35f4380e40bcf8ab064ac7d37e4d237ad925a5e5dc1e7f72cbf4ad92fe7b25fb",
+    "recover_evidence.py": "5b561ffa327f35c263dc8151f621c518bf31812a5b2a259860f76ed093caf1b8"
+  }
+}

rr2/frozen_evaluations/REPRODUCE.txt

@@ -0,0 +1,24 @@
+Round-two frozen-checkpoint evaluations
+
+Requirements: the published E8 Python dependencies and a CUDA GPU. No training.
+Download the immutable model/code and dataset revisions listed in the manuscript.
+Set the PUBLIC path in evaluate_frozen.py and recover_evidence.py to the local
+e8_full model release and dataset counterpart, respectively.
+
+For each seed 821, 822, 823 and condition known16, known32, unseen16, unseen32:
+
+python evaluate_frozen.py --seed 821 --condition unseen16 --output rerun
+python evaluate_frozen.py --seed 821 --condition unseen16 --comparator --output rerun
+
+The comparator parses observable name roles and averages frozen token embeddings.
+Its identity views are wider and its role extraction more structural; it is not a
+matched training ablation. Rows, hashes, per-seed results and Wilson intervals are
+under evidence/. All conditions use 2,000 worlds and 512 unrelated writes.
+
+name_counts.json contains realized ordinary/challenge counts and text witnesses.
+candidate_nulls.py reproduces the ordinary E8 uniform-candidate answer null.
+curves_final/ contains raw archived logs, their source hashes and the curve PDF.
+The corrected-corpus retrain is PNSR_ON_E3. The historical run name PNSR_K4_v2
+does not mean corrected corpus; PNSR_K4_v2c is a rejected weighted continuation.
+
+Verify EVIDENCE_MANIFEST.json with SHA-256 before using any recorded outcomes.

rr2/frozen_evaluations/candidate_nulls.py

@@ -0,0 +1,78 @@
+#!/usr/bin/env python3
+"""Post-hoc exact-solver null analysis, without consulting model predictions."""
+from pathlib import Path
+import json
+import sys
+import hashlib
+from concurrent.futures import ProcessPoolExecutor
+import numpy as np
+
+HERE = Path(__file__).resolve().parent
+PUBLIC = Path('/raid/nurgaly/datasets/pns/release/pns-hf-e8-20260911')
+sys.path.insert(0, str(PUBLIC / 'model/e8_full/source/campaigns/e8_full'))
+sys.path.insert(0, str(PUBLIC / 'model/e8_full/source'))
+from pns.world.sim import generate_lifetime
+from pns.world.schema import ET
+from pns.data.binding import binding_plan
+
+
+def lifetime(seed):
+    lt = generate_lifetime(seed)
+    active = {}
+    history = {}
+    stored = {}
+    clock = 0
+    counts, identity, answers = [], [], []
+    plan = binding_plan(lt)
+    for t, event in enumerate(lt['events']):
+        p = event.payload
+        clock += event.dt
+        slot = int(plan['write_slot'][t])
+        if slot >= 0:
+            key = (p['ent'], p['attr'] if event.etype == ET.OBSERVATION else p['key'])
+            active[key] = slot
+            stored[slot] = p
+            history.setdefault(key, []).append((clock, p.get('source', ''), slot))
+        elif event.etype == ET.CORRECTION and p['mode'] == 'revert':
+            key = (p['ent'], p['key'])
+            matching = [s for clk, src, s in history.get(key, [])
+                        if (p.get('ref_kind') == 'source' and src == p['ref_source'])
+                        or (p.get('ref_kind') != 'source' and clk == p['ref_clock'])]
+            if matching:
+                active[key] = matching[-1]
+        if event.family == 1:
+            d = lt['tickets'][t].data
+            candidates = [stored[s] for (ent, attr), s in active.items() if attr == d['attr']]
+            rel = stored[active[(d['src'], d['rel'])]]
+            dest = stored[active[(rel['ent2'], d['attr'])]]
+            assert dest['value'] == event.gold_value
+            assert candidates
+            counts.append(len(candidates))
+            identity.append(1 / len(candidates))
+            answers.append(sum(c['value'] == event.gold_value for c in candidates) / len(candidates))
+    return counts, identity, answers
+
+
+def main():
+    with ProcessPoolExecutor(max_workers=16) as workers:
+        parts = list(workers.map(lifetime, range(8900000, 8902000), chunksize=16))
+    counts, identities, answers = (np.concatenate([p[i] for p in parts]) for i in range(3))
+    assert len(counts) == 7218
+    archived = json.loads((PUBLIC / 'model/e8_full/results/reproduced_headline.json').read_text())
+    reference = archived['reports']['confirmation']['821']['grounded_future']['pns']
+    assert abs(counts.mean() - reference['twohop_candidate_count']) < 1e-12
+    assert abs(identities.mean() - reference['twohop_identity_uniform_null']) < 1e-7
+    report = {'ordinary_e8': {'questions': len(counts), 'mean_candidates': float(counts.mean()),
+                             'uniform_candidate_identity_null': float(identities.mean()),
+                             'uniform_candidate_answer_null_with_oracle_values': float(answers.mean())},
+              'analysis': 'Uniform selection among authoritative requested-attribute records. Answers include value collisions. Not a model prediction or an E3 addressing control.',
+              'script_sha256': hashlib.sha256(Path(__file__).read_bytes()).hexdigest()}
+    path = HERE / 'evidence/candidate_nulls.json'
+    with path.open('x') as f:
+        json.dump(report, f, indent=2, allow_nan=False)
+        f.write('\n')
+    print(json.dumps(report), flush=True)
+
+
+if __name__ == '__main__':
+    main()

rr2/frozen_evaluations/check_rr2_evidence.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+"""Validate the complete RR2 evidence and its source/deposit boundary."""
+from pathlib import Path
+import hashlib
+import json
+import sys
+import numpy as np
+
+HERE = Path(__file__).resolve().parent
+sys.path.insert(0, str(HERE))
+from generate_rr2_assets import load, SEEDS, CONDITIONS
+
+
+def main():
+    manifest = json.loads((HERE / 'EVIDENCE_MANIFEST.json').read_text())['files']
+    for relative, expected in manifest.items():
+        assert hashlib.sha256((HERE / relative).read_bytes()).hexdigest() == expected, relative
+    summaries = json.loads((HERE / 'results_summary.json').read_text())['all_runs']
+    protocols = {hashlib.sha256((HERE / 'experiment_rr2.md').read_bytes()).hexdigest(),
+                 hashlib.sha256((HERE / 'evidence/experiment_rr2_v1.md').read_bytes()).hexdigest()}
+    evaluators = {hashlib.sha256((HERE / 'evaluate_frozen.py').read_bytes()).hexdigest(),
+                  hashlib.sha256((HERE / 'evidence/evaluate_frozen_v1.py').read_bytes()).hexdigest()}
+    for condition in CONDITIONS:
+        for seed in SEEDS:
+            for comparator in (False, True):
+                obj = load(condition, seed, comparator)
+                key = f'{condition}_s{seed}' + ('_mean' if comparator else '')
+                assert summaries[key] == obj
+                assert obj['protocol']['protocol_sha256'] in protocols
+                assert obj['protocol']['evaluator_sha256'] in evaluators
+                metrics = obj['metrics']
+                if comparator:
+                    assert metrics['identity_512']['accuracy'] == 1
+                    assert metrics['twohop_512']['accuracy'] == metrics['onehop_512']['accuracy']
+                elif condition.startswith('known'):
+                    assert metrics['identity_512']['accuracy'] == metrics['twohop_512']['accuracy'] == 1
+                else:
+                    assert metrics['identity_512']['accuracy'] < .20 and metrics['twohop_512']['accuracy'] < .20
+    counts = json.loads((HERE / 'evidence/recovered/name_counts.json').read_text())
+    assert counts['inventory_size'] == {'ordinary': 66, 'challenge': 64}
+    for seed in SEEDS:
+        assert counts['ordinary'][f'training_seen_seed_{seed}']['distinct_names'] == 66
+        assert counts['challenge'][f'training_seen_seed_{seed}']['distinct_names'] == 64
+    body = (HERE.parent / 'mdpi/tables/rr2_stress_rows.tex').read_text().strip()
+    assert body in (HERE.parent / 'mdpi/main.tex').read_text()
+    print(f'RR2 verified: {len(manifest)} hashes, 24 complete evaluations, honest failed-transfer boundary, synchronized table.')
+
+
+if __name__ == '__main__':
+    main()

rr2/frozen_evaluations/evaluate_frozen.py

@@ -0,0 +1,200 @@
+#!/usr/bin/env python3
+"""Non-adaptive RR2 stress tests of immutable, publicly released E8 weights."""
+from __future__ import annotations
+import argparse
+import hashlib
+import json
+import os
+import re
+import sys
+from pathlib import Path
+
+import numpy as np
+import torch
+from safetensors.torch import load_file
+
+HERE = Path(__file__).resolve().parent
+PUBLIC = Path('/raid/nurgaly/datasets/pns/release/pns-hf-e8-20260911/model/e8_full')
+os.environ['PNS_TOKENIZER'] = str(PUBLIC / 'source/tokenizer/tokenizer.json')
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+sys.path.insert(0, str(PUBLIC / 'source/campaigns/e8_full'))
+import data
+import evaluate
+from model import FullModel
+from gates import wilson
+from pns.data.tokenize import load_tokenizer
+from pns.world.schema import PERSON_NAMES, PROJECT_NAMES, SHIPMENT_NAMES, VAULT_NAMES, DEVICE_NAMES
+
+ORIGINAL = tuple(data.NAMES)
+ALL_NAMES = tuple(PERSON_NAMES + PROJECT_NAMES + SHIPMENT_NAMES + VAULT_NAMES + DEVICE_NAMES)
+NOVEL = tuple(a + b + c for a in ('Za', 'Fe', 'Lu', 'No')
+              for b in ('vi', 'pe', 'ru', 'ka') for c in ('mo', 'ta', 'zi', 'le'))
+CONDITIONS = (('known16', ORIGINAL, 16), ('known32', ORIGINAL, 32),
+              ('unseen16', NOVEL, 16), ('unseen32', NOVEL, 32))
+
+
+def digest(path):
+    return hashlib.sha256(Path(path).read_bytes()).hexdigest()
+
+
+def save(path, obj):
+    with Path(path).open('x') as f:
+        json.dump(obj, f, indent=2, sort_keys=True, allow_nan=False)
+        f.write('\n')
+
+
+def parse(text):
+    text = text.strip()
+    patterns = (
+        (r'^(\S+) lists (\S+) as owner according to \S+$', 1, 2),
+        (r'^according to \S+ the owner of (\S+) is (\S+)$', 1, 2),
+        (r'^(\S+) is now owner of (\S+) reported by \S+$', 2, 1),
+        (r'^reporter \S+ says (\S+) names (\S+) owner$', 1, 2),
+        (r'^(\S+) has value \S+ according to \S+$', 1, None),
+        (r'^according to \S+ the value of (\S+) is \S+$', 1, None),
+        (r'^\S+ is now value of (\S+) reported by \S+$', 1, None),
+        (r'^reporter \S+ says (\S+) has value \S+$', 1, None),
+    )
+    for pattern, owner, dest in patterns:
+        match = re.fullmatch(pattern, text)
+        if match:
+            return match[owner], None if dest is None else match[dest]
+    raise ValueError(f'Unparsed event: {text!r}')
+
+
+class MeanNameComparator:
+    """Explicit text-role parsing plus frozen name-piece averaging; no gold input."""
+    def __init__(self, model, names):
+        self.model = model
+        self.tokenizer = load_tokenizer()
+        self.names = set(names)
+        self.views = {}
+        for name in names:
+            ids = torch.tensor(self.tokenizer.encode(name).ids, device=model.tok_emb.weight.device)
+            vector = model.tok_emb(ids).float().mean(0)
+            # Keep all 384 dimensions; this comparator has no learned projection.
+            self.views[name] = vector
+        keys = torch.nn.functional.normalize(torch.stack(list(self.views.values())), dim=-1)
+        similarities = keys @ keys.T
+        similarities.fill_diagonal_(-2)
+        assert similarities.max().item() < 0.99999, 'Name averages are not distinguishable'
+
+    def challenge_write(self, tokens):
+        shape = tokens.shape[:-1]
+        payload = self.model.challenge_write(tokens)[..., :384]
+        flat = tokens.reshape(-1, tokens.shape[-1]).cpu().numpy()
+        owners, dests = [], []
+        zero = torch.zeros(384, device=payload.device)
+        for row in flat:
+            owner, dest = parse(self.tokenizer.decode([int(t) for t in row if t not in (0, 2)]))
+            assert owner in self.names and (dest is None or dest in self.names)
+            owners.append(self.views[owner])
+            dests.append(zero if dest is None else self.views[dest])
+        return torch.cat((payload, torch.stack(owners).reshape(*shape, 384),
+                          torch.stack(dests).reshape(*shape, 384)), -1)
+
+    def challenge_values(self, records):
+        return self.model.challenge_head(records[..., :384])
+
+    def challenge_answer(self, relation, candidates, ids=None):
+        q = torch.nn.functional.normalize(relation[..., 768:], dim=-1)
+        k = torch.nn.functional.normalize(candidates[..., 384:768], dim=-1)
+        logits = 10 * (q @ k.transpose(-1, -2))
+        index = logits.argmax(-1)
+        selected = self.challenge_values(candidates).gather(-2, index[..., None].expand(*index.shape, 32))
+        return index, selected.argmax(-1), logits
+
+
+@torch.no_grad()
+def run_batch(model, g):
+    # Published evaluator is width-specific. Use a local width-aware analogue
+    # for the 1,152-dimensional parse-and-average comparator only.
+    width = model.challenge_write(g['reltok'][:, :1]).shape[-1]
+    if width == 640:
+        result = evaluate.challenge_batch(model, g)
+    else:
+        old_scatter, old_canonical = evaluate.scatter, evaluate.canonical
+        def scatter(state, write, payload, layout):
+            phys = layout.gather(1, write[:, None])
+            return state.scatter(1, phys[:, :, None].expand(-1, 1, width), payload[:, None])
+        def canonical(state, layout):
+            return state.gather(1, layout[:, :, None].expand(-1, -1, width))
+        evaluate.scatter, evaluate.canonical = scatter, canonical
+        # challenge_batch has two initial-state scatters with literal width640.
+        # Adapt only those allocation literals in its verified source locally.
+        import inspect
+        namespace = dict(vars(evaluate))
+        source = inspect.getsource(evaluate.challenge_batch).replace('expand(-1,-1,640)', 'expand(-1,-1,width)')
+        namespace['width'] = width
+        try:
+            exec(compile(source, str(HERE / 'evaluate_frozen.py') + ':width-adaptation', 'exec'), namespace)
+            result = namespace['challenge_batch'](model, g)
+        finally:
+            evaluate.scatter, evaluate.canonical = old_scatter, old_canonical
+    b, n, _ = g['reltok'].shape
+    rows = torch.arange(b, device=g['reltok'].device)
+    r = model.challenge_write(g['reltok'])[rows, g['query']][:, None]
+    c = model.challenge_write(g['valtok'][:, 0])
+    logits = model.challenge_answer(r, c)[2][:, 0]
+    result['margin_at_write'] = (logits.topk(2, dim=-1).values[:, 0] - logits.topk(2, dim=-1).values[:, 1]).cpu().numpy()
+    return result
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--seed', type=int, choices=(821, 822, 823), required=True)
+    p.add_argument('--condition', choices=tuple(c[0] for c in CONDITIONS), required=True)
+    p.add_argument('--n-worlds', type=int, default=2000)
+    p.add_argument('--comparator', action='store_true')
+    p.add_argument('--output', type=Path, default=HERE / 'evidence')
+    a = p.parse_args()
+    assert len(NOVEL) == len(set(NOVEL)) == 64 and not set(NOVEL) & set(ALL_NAMES)
+    i, (_, names, n) = next((i, c) for i, c in enumerate(CONDITIONS) if c[0] == a.condition)
+    data.NAMES = list(names)
+    evaluate.NAMES = list(names)
+    checkpoint = PUBLIC / 'checkpoints' / f'grounded_future_s{a.seed}.safetensors'
+    manifest = json.loads((PUBLIC / 'MANIFEST.json').read_text())['files']
+    assert digest(checkpoint) == manifest[str(checkpoint.relative_to(PUBLIC))]
+    for module in (data, evaluate):
+        module_path = Path(module.__file__)
+        assert digest(module_path) == manifest[str(module_path.relative_to(PUBLIC))]
+    protocol = {
+        'protocol_sha256': digest(HERE / 'experiment_rr2.md'),
+        'evaluator_sha256': digest(__file__),
+        'checkpoint_sha256': digest(checkpoint),
+        'condition': a.condition, 'model_seed': a.seed, 'worlds': a.n_worlds,
+        'names': names, 'candidates': n, 'writes': 512, 'no_training': True,
+        'comparator': a.comparator,
+    }
+    label = f'{a.condition}_s{a.seed}' + ('_mean' if a.comparator else '')
+    folder = a.output / label
+    folder.mkdir(parents=True, exist_ok=False)
+    save(folder / 'BEFORE_PREDICTIONS.json', protocol)
+    torch.manual_seed(20261004)
+    model = FullModel('grounded_future').cuda().eval()
+    model.load_state_dict(load_file(str(checkpoint)))
+    model.requires_grad_(False)
+    if a.comparator:
+        model = MeanNameComparator(model, names)
+    collected = {}
+    for j in range(0, a.n_worlds, 16):
+        generated = data.challenge(94000000000000 + i * 100000 + j,
+                                   min(16, a.n_worlds - j), n=n, futures=1, interference=512)
+        g = data.device_batch(generated, 'cuda')
+        result = run_batch(model, g)
+        for key, val in result.items():
+            collected.setdefault(key, []).append(val)
+        if j % 160 == 0:
+            print(json.dumps({'run': label, 'completed': j + len(result['gold']), 'total': a.n_worlds}), flush=True)
+    rows = {key: np.concatenate(parts) for key, parts in collected.items()}
+    np.savez_compressed(folder / 'rows.npz', **rows)
+    metrics = {key: {'accuracy': float(vals.mean()), 'wilson95': wilson(vals), 'n': len(vals)}
+               for key, vals in rows.items() if key not in ('gold', 'margin_at_write')}
+    metrics['margin_at_write'] = {str(q): float(np.quantile(rows['margin_at_write'], q))
+                                  for q in (0, .01, .05, .5, .95, 1)}
+    save(folder / 'RESULTS.json', {'protocol': protocol, 'metrics': metrics})
+    print(json.dumps({'run': label, 'identity': metrics['identity_512'], 'twohop': metrics['twohop_512']}), flush=True)
+
+
+if __name__ == '__main__':
+    main()

rr2/frozen_evaluations/evidence/candidate_nulls.json

@@ -0,0 +1,10 @@
+{
+  "ordinary_e8": {
+    "questions": 7218,
+    "mean_candidates": 3.282488223884733,
+    "uniform_candidate_identity_null": 0.38909774725443885,
+    "uniform_candidate_answer_null_with_oracle_values": 0.4744629277775579
+  },
+  "analysis": "Uniform selection among authoritative requested-attribute records. Answers include value collisions. Not a model prediction or an E3 addressing control.",
+  "script_sha256": "d8b34b0a516fdfc7cfd86da58f7d7542898c0c9d0f0f77a878c62766f6819f95"
+}

rr2/frozen_evaluations/evidence/curves_final/PNSR_K4_s2.jsonl

@@ -0,0 +1,300 @@
+{"u": 20, "loss": 9.9504, "gn": 9.0, "lr": 2.7e-05, "ev_s": 3399, "mem_gb": 48.6, "state_norm": 14.96, "acc_ptr": 0.096, "acc_enum": 0.266, "acc_op": 0.266, "L_mode": 0.0984, "L_enum": 3.0654, "L_ptr": 3.0164, "L_op": 0.8666, "L_arg0": 2.9037}
+{"u": 40, "loss": 5.8418, "gn": 2.84, "lr": 5.3e-05, "ev_s": 4003, "mem_gb": 48.6, "state_norm": 15.98, "acc_ptr": 0.054, "acc_enum": 0.384, "L_mode": 0.0745, "L_enum": 2.0753, "L_ptr": 3.692}
+{"u": 60, "loss": 7.9894, "gn": 8.9, "lr": 8e-05, "ev_s": 4231, "mem_gb": 48.6, "state_norm": 15.09, "acc_ptr": 0.313, "acc_enum": 0.274, "acc_op": 0.736, "L_mode": 0.0533, "L_enum": 3.0155, "L_ptr": 2.3921, "L_op": 0.2308, "L_arg0": 2.2978}
+{"u": 80, "loss": 4.0349, "gn": 3.89, "lr": 0.000107, "ev_s": 4377, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.417, "acc_enum": 0.415, "L_mode": 0.0256, "L_enum": 2.0912, "L_ptr": 1.9181}
+{"u": 100, "loss": 5.1248, "gn": 3.22, "lr": 0.000133, "ev_s": 4453, "mem_gb": 48.6, "state_norm": 15.53, "acc_ptr": 0.434, "acc_enum": 0.283, "acc_op": 1.0, "L_mode": 0.0089, "L_enum": 2.896, "L_ptr": 1.0354, "L_op": 0.0086, "L_arg0": 1.1758}
+{"u": 120, "loss": 3.5004, "gn": 1.66, "lr": 0.00016, "ev_s": 4520, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.417, "acc_enum": 0.404, "L_mode": 0.0015, "L_enum": 2.0561, "L_ptr": 1.4428}
+{"u": 140, "loss": 5.2227, "gn": 2.78, "lr": 0.000187, "ev_s": 4556, "mem_gb": 48.6, "state_norm": 15.68, "acc_ptr": 0.446, "acc_enum": 0.254, "acc_op": 1.0, "L_mode": 0.0036, "L_enum": 2.9919, "L_ptr": 1.0488, "L_op": 0.0051, "L_arg0": 1.1733}
+{"u": 160, "loss": 3.4869, "gn": 2.18, "lr": 0.000213, "ev_s": 4593, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.4, "acc_enum": 0.387, "L_mode": 0.0004, "L_enum": 2.0708, "L_ptr": 1.4157}
+{"u": 180, "loss": 5.2752, "gn": 2.62, "lr": 0.00024, "ev_s": 4616, "mem_gb": 48.6, "state_norm": 15.74, "acc_ptr": 0.452, "acc_enum": 0.271, "acc_op": 1.0, "L_mode": 0.0004, "L_enum": 2.9973, "L_ptr": 1.0457, "L_op": 0.003, "L_arg0": 1.229}
+{"u": 200, "loss": 3.4214, "gn": 1.94, "lr": 0.000267, "ev_s": 4640, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.4, "acc_enum": 0.4, "L_mode": 0.0001, "L_enum": 2.0999, "L_ptr": 1.3214}
+{"u": 220, "loss": 4.9478, "gn": 2.69, "lr": 0.000293, "ev_s": 4651, "mem_gb": 48.6, "state_norm": 15.85, "acc_ptr": 0.508, "acc_enum": 0.291, "acc_op": 1.0, "L_mode": 0.0006, "L_enum": 2.8448, "L_ptr": 0.9311, "L_op": 0.0016, "L_arg0": 1.1698}
+{"u": 240, "loss": 3.4721, "gn": 1.0, "lr": 0.00032, "ev_s": 4668, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.407, "acc_enum": 0.419, "L_mode": 0.0009, "L_enum": 2.1149, "L_ptr": 1.3563}
+{"u": 260, "loss": 5.1222, "gn": 2.19, "lr": 0.000347, "ev_s": 4677, "mem_gb": 48.6, "state_norm": 15.85, "acc_ptr": 0.473, "acc_enum": 0.267, "acc_op": 1.0, "L_mode": 0.0004, "L_enum": 2.9772, "L_ptr": 1.007, "L_op": 0.0027, "L_arg0": 1.1348}
+{"u": 280, "loss": 3.2943, "gn": 1.42, "lr": 0.000373, "ev_s": 4689, "mem_gb": 48.6, "state_norm": 15.98, "acc_ptr": 0.46, "acc_enum": 0.432, "L_mode": 0.0002, "L_enum": 2.0162, "L_ptr": 1.2779}
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+{"u": 460, "loss": 5.0523, "gn": 1.91, "lr": 0.0004, "ev_s": 4837, "mem_gb": 48.6, "state_norm": 15.98, "acc_ptr": 0.525, "acc_enum": 0.298, "acc_op": 1.0, "L_mode": 0.0007, "L_enum": 2.9152, "L_ptr": 0.9585, "L_op": 0.0005, "L_arg0": 1.1775}
+{"u": 480, "loss": 3.3222, "gn": 1.24, "lr": 0.0004, "ev_s": 4879, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.419, "acc_enum": 0.444, "L_mode": 0.0001, "L_enum": 2.0684, "L_ptr": 1.2538}
+{"u": 500, "loss": 4.9391, "gn": 2.14, "lr": 0.0004, "ev_s": 4914, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.502, "acc_enum": 0.302, "acc_op": 1.0, "L_mode": 0.0, "L_enum": 2.8876, "L_ptr": 0.9704, "L_op": 0.0008, "L_arg0": 1.0803}
+{"u": 520, "loss": 3.4818, "gn": 2.67, "lr": 0.0004, "ev_s": 4948, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.399, "acc_enum": 0.361, "L_mode": 0.0, "L_enum": 2.1303, "L_ptr": 1.3515}
+{"u": 540, "loss": 4.7992, "gn": 1.87, "lr": 0.0004, "ev_s": 4979, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.544, "acc_enum": 0.322, "acc_op": 1.0, "L_mode": 0.0, "L_enum": 2.77, "L_ptr": 0.9015, "L_op": 0.0006, "L_arg0": 1.127}
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+{"u": 580, "loss": 5.0225, "gn": 2.38, "lr": 0.0004, "ev_s": 5040, "mem_gb": 48.6, "state_norm": 15.99, "acc_ptr": 0.511, "acc_enum": 0.266, "acc_op": 1.0, "L_mode": 0.0001, "L_enum": 2.9234, "L_ptr": 0.9382, "L_op": 0.0004, "L_arg0": 1.1605}
+{"u": 600, "loss": 3.3944, "gn": 1.22, "lr": 0.0004, "ev_s": 5068, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.433, "acc_enum": 0.4, "L_mode": 0.0001, "L_enum": 2.1666, "L_ptr": 1.2276}
+{"u": 620, "loss": 4.8791, "gn": 1.91, "lr": 0.0004, "ev_s": 5093, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.558, "acc_enum": 0.301, "acc_op": 1.0, "L_mode": 0.0, "L_enum": 2.9191, "L_ptr": 0.8967, "L_op": 0.0003, "L_arg0": 1.063}
+{"u": 640, "loss": 3.3663, "gn": 1.52, "lr": 0.0004, "ev_s": 5119, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.474, "acc_enum": 0.394, "L_mode": 0.0, "L_enum": 2.15, "L_ptr": 1.2163}
+{"u": 660, "loss": 4.9075, "gn": 2.04, "lr": 0.0004, "ev_s": 5140, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.521, "acc_enum": 0.31, "acc_op": 1.0, "L_mode": 0.0, "L_enum": 2.8905, "L_ptr": 0.9083, "L_op": 0.0008, "L_arg0": 1.1079}
+{"u": 680, "loss": 3.277, "gn": 1.67, "lr": 0.0004, "ev_s": 5164, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.437, "acc_enum": 0.399, "L_mode": 0.0, "L_enum": 2.0532, "L_ptr": 1.2238}
+{"u": 700, "loss": 4.9246, "gn": 2.25, "lr": 0.0004, "ev_s": 5184, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.553, "acc_enum": 0.284, "acc_op": 1.0, "L_mode": 0.0, "L_enum": 2.9953, "L_ptr": 0.8544, "L_op": 0.0021, "L_arg0": 1.0728}
+{"u": 720, "loss": 3.1966, "gn": 1.06, "lr": 0.0004, "ev_s": 5205, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.464, "acc_enum": 0.414, "L_mode": 0.0, "L_enum": 2.001, "L_ptr": 1.1956}
+{"u": 740, "loss": 4.9571, "gn": 2.03, "lr": 0.0004, "ev_s": 5223, "mem_gb": 48.6, "state_norm": 15.99, "acc_ptr": 0.485, "acc_enum": 0.28, "acc_op": 1.0, "L_mode": 0.0, "L_enum": 2.9941, "L_ptr": 0.9326, "L_op": 0.0011, "L_arg0": 1.0292}
+{"u": 760, "loss": 3.2941, "gn": 1.89, "lr": 0.0004, "ev_s": 5243, "mem_gb": 48.6, "state_norm": 16.0, "acc_ptr": 0.447, "acc_enum": 0.418, "L_mode": 0.0, "L_enum": 2.0862, "L_ptr": 1.2079}
+{"u": 780, "loss": 4.8561, "gn": 2.02, "lr": 0.0004, "ev_s": 5258, "mem_gb": 48.6, "state_norm": 15.98, "acc_ptr": 0.513, "acc_enum": 0.325, "acc_op": 1.0, "L_mode": 0.0002, "L_enum": 2.8935, "L_ptr": 0.97, "L_op": 0.0006, "L_arg0": 0.9918}
+{"u": 800, "loss": 3.2398, "gn": 3.69, "lr": 0.0004, "ev_s": 5276, "mem_gb": 48.6, "state_norm": 15.99, "acc_ptr": 0.423, "acc_enum": 0.405, "L_mode": 0.0, "L_enum": 1.9871, "L_ptr": 1.2527}
+{"u": 820, "loss": 4.8107, "gn": 1.73, "lr": 0.0004, "ev_s": 5290, "mem_gb": 48.6, "state_norm": 15.99, "acc_ptr": 0.526, "acc_enum": 0.285, "acc_op": 1.0, "L_mode": 0.0, "L_enum": 2.9022, "L_ptr": 0.8834, "L_op": 0.002, "L_arg0": 1.0231}
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rr2/frozen_evaluations/evidence/curves_final/PNSR_K4_s3.jsonl

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