diff --git a/.gitattributes b/.gitattributes
index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..79c3106975add15f4282a45cb11a995649cbd09f 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+items/items.jsonl filter=lfs diff=lfs merge=lfs -text
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new file mode 100644
index 0000000000000000000000000000000000000000..64b0b626e54f36e08f82cc671cee35b611f3b044
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new file mode 100644
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diff --git a/assays/resource_dense_l12_s47_20M.json b/assays/resource_dense_l12_s47_20M.json
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diff --git a/assays/resource_dense_l12_s47_40M.json b/assays/resource_dense_l12_s47_40M.json
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+{
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+{
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+{
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+{
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diff --git a/holdout/resource_dense_l8_s59_20M.json b/holdout/resource_dense_l8_s59_20M.json
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diff --git a/holdout/resource_dense_l8_s59_40M.json b/holdout/resource_dense_l8_s59_40M.json
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diff --git a/holdout/resource_dense_l8_s59_5M.json b/holdout/resource_dense_l8_s59_5M.json
new file mode 100644
index 0000000000000000000000000000000000000000..2ce21b7ec7d12eb79d179eda98342bfd0ea421e2
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diff --git a/holdout/resource_orthogonal_l12_s47_10M.json b/holdout/resource_orthogonal_l12_s47_10M.json
new file mode 100644
index 0000000000000000000000000000000000000000..72fb420ffbda79ee90a1fcfac988d978d5802e59
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+{
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diff --git a/holdout/resource_orthogonal_l12_s47_1M.json b/holdout/resource_orthogonal_l12_s47_1M.json
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index 0000000000000000000000000000000000000000..f8c276bed66c698420e85cd8b616bff1b531448a
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+{
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diff --git a/holdout/resource_orthogonal_l12_s47_20M.json b/holdout/resource_orthogonal_l12_s47_20M.json
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index 0000000000000000000000000000000000000000..aeabe7f22ce30fe862c46233ea3313c6f411b2ab
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+{
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diff --git a/holdout/resource_orthogonal_l12_s47_40M.json b/holdout/resource_orthogonal_l12_s47_40M.json
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index 0000000000000000000000000000000000000000..a08847af5b5889990aed33ec0f70677f326a18e9
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diff --git a/holdout/resource_orthogonal_l12_s53_10M.json b/holdout/resource_orthogonal_l12_s53_10M.json
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index 0000000000000000000000000000000000000000..fa2a4356148d6488f8b9e797ddd0cb45e845f901
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diff --git a/holdout/resource_orthogonal_l12_s53_1M.json b/holdout/resource_orthogonal_l12_s53_1M.json
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index 0000000000000000000000000000000000000000..77a2a09c45b3c969429cf90a7e69d9e12aa68450
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diff --git a/holdout/resource_orthogonal_l12_s53_20M.json b/holdout/resource_orthogonal_l12_s53_20M.json
new file mode 100644
index 0000000000000000000000000000000000000000..d7d075759a5adaf0409a8d96a75fb9f075c88814
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+  "amendment_id": "compute_cutoff_20260718T202757Z",
+  "status": "prospective_compute_only_amendment",
+  "original_protocol_sha256": "1897bb5a70f87dd9e173486f0a137a57b5be2cb159cf4324b7cd95117d928cd7",
+  "decision_utc": "2026-07-18T20:27:57Z",
+  "original_runner_started_utc": "2026-07-18T19:14:53Z",
+  "fixed_compute_deadline_utc": "2026-07-19T19:14:53Z",
+  "reason": {
+    "category": "fixed_compute_deadline",
+    "detail": "Measured two-GPU throughput projected 28-31 hours for the original training-first 100M pipeline, beyond the fixed one-day allocation. The registered 40M primary endpoint fits while preserving the complete factorial and all three fresh seeds."
+  },
+  "outcome_access": {
+    "new_assay_files_present": 0,
+    "new_holdout_files_present": 0,
+    "aggregate_results_read": false,
+    "leaderboard_results_read_for_amendment": false,
+    "decision_uses_training_throughput_only": true
+  },
+  "decision_snapshot": {
+    "remote_path": "/home/cc/factorized_resource_v1/amendment_snapshot_20260718T202757Z",
+    "snapshot_utc": "2026-07-18T20:28:24Z",
+    "dense_l12_s47_words_seen": 25586123,
+    "dense_l12_s47_words_per_second": 5907.69,
+    "orthogonal_l12_s47_words_seen": 20531746,
+    "orthogonal_l12_s47_words_per_second": 4752.50,
+    "files_sha256": {
+      "assay_files_at_amendment.txt": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+      "audit.json": "4050106413d3340184f4ebe250181dc2764f3bb5f3b4af77fc2c9642e9ad87e4",
+      "items.manifest.json": "776dd706e7611f1e5b6f7eea2944fd0ef2a2cd9729238741611646b7542ba8d1",
+      "train_resource_dense_l12_s47.log": "207fea804d469cf2c179cc9e17381e25af81fd2e6d93c54e714dc5b05e52282c",
+      "train_resource_orthogonal_l12_s47.log": "9b1c38ed99c3cc48272acf1bdaa7ba6cddee5af1544082c39536b9414f34ddb6"
+    }
+  },
+  "unchanged": [
+    "primary_question",
+    "primary_interaction",
+    "geometry_and_depth_cells",
+    "seeds_47_53_59",
+    "corpus_and_tokenizer",
+    "optimizer_and_100M_learning_rate_schedule",
+    "frozen_assay_items_and_hashes",
+    "heldout_loss_control",
+    "paired_seed_as_independent_unit",
+    "no_checkpoint_or_leaderboard_selection"
+  ],
+  "effective_execution": {
+    "terminal_exposure_words": 40000000,
+    "schedule_total_words": 100000000,
+    "saved_checkpoint_words": [
+      1000000,
+      2000000,
+      3000000,
+      4000000,
+      5000000,
+      6000000,
+      7000000,
+      8000000,
+      9000000,
+      10000000,
+      20000000,
+      30000000,
+      40000000
+    ],
+    "evaluation_words": [1000000, 5000000, 10000000, 20000000, 40000000],
+    "embargo_aggregate_until_all_models_sealed": true,
+    "required_models": 12,
+    "failed_run_rule": "Do not replace a seed or cell. Mark the prospective factorial incomplete if any registered 40M checkpoint is unavailable."
+  },
+  "unexecuted_secondary_endpoints": [
+    "paired-seed interaction at 100M",
+    "60M, 80M and 100M competence trajectories",
+    "durability or asymptotic equivalence"
+  ],
+  "precision_gate": {
+    "independent_units": 3,
+    "smallest_relevant_absolute_interaction": 0.05,
+    "seed_interval": "two-sided 95% Student-t interval over the three paired seed interactions",
+    "exact_direction_test": "one-sided exact sign test; minimum attainable p-value with three seeds is 0.125",
+    "multiplicity": "The 40M equal-weight competence interaction is the sole primary estimand. Component, held-out-loss, AUC, parameter, FLOP and wall-time analyses are descriptive secondary evidence.",
+    "interpretation_rules": {
+      "positive_large_consistent": "Mean interaction is at least +0.05, all seed interactions are positive, and the 95% seed interval is above zero. This supports reduced depth sensitivity under orthogonal geometry, not general compute substitution.",
+      "negative_large_consistent": "Mean interaction is at most -0.05, all seed interactions are negative, and the 95% seed interval is below zero. This supports an increased depth cost under orthogonal geometry.",
+      "practically_small_precise": "The entire 95% seed interval and every seed interaction lie within [-0.05,+0.05]. This is descriptive compatibility with a small interaction, not universal equivalence.",
+      "imprecise_or_mixed": "Every other pattern is indeterminate and may not be reframed as a positive, negative or null discovery."
+    }
+  },
+  "estimand_boundary": "The factorial estimates sensitivity to reducing Transformer depth under matched linguistic exposure. It does not equate word exposure with FLOPs, parameter count, wall time, human cognitive resources or general computational efficiency.",
+  "evidence_roles": {
+    "prospective_primary": "The twelve fresh resource models at 40M only.",
+    "retrospective_100M_context": "Previously trained factorized tournament ablations may contextualize behavior, late persistence and failure modes but are not pooled with the fresh factorial.",
+    "prior_mechanistic_context": "The frozen 100M specificity matrix and layerwise causal-use trajectory motivate the resource question but do not mediate or statistically rescue the 40M interaction."
+  },
+  "claim_boundary": "Results concern early acquisition under one matched Strict-Small recipe. No result establishes durable asymptotic substitution, human equivalence, universal architectural optimality, leaderboard superiority or equal-compute efficiency."
+}
diff --git a/protocol/original_protocol.json b/protocol/original_protocol.json
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index 0000000000000000000000000000000000000000..f17f9edd35360361822d104a74c9ad32e52d5f45
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+{
+  "schema_version": 1,
+  "study_id": "factorized_resource_substitution_v1",
+  "status": "preregistered_pending_execution",
+  "title": "Geometry as a Cognitive Resource: Can Structured Representations Substitute for Attention Depth?",
+  "scope": "compute_only_non_benchmark",
+  "primary_question": "Does an orthogonally factorized representational geometry reduce the attention depth required to acquire the same frozen linguistic relations?",
+  "primary_claim": "Factorized geometry substitutes for attention depth when reducing a model from 12 to 8 layers causes less loss of frozen relational competence under orthogonal factorization than under the matched dense control.",
+  "design": {
+    "factors": {
+      "geometry": ["dense", "orthogonal"],
+      "attention_layers": [8, 12]
+    },
+    "seeds": [47, 53, 59],
+    "fresh_seeds_unseen_by_selection": true,
+    "models": [
+      {"variant": "resource_dense_l12_s47", "geometry": "dense", "attention_layers": 12, "seed": 47},
+      {"variant": "resource_dense_l12_s53", "geometry": "dense", "attention_layers": 12, "seed": 53},
+      {"variant": "resource_dense_l12_s59", "geometry": "dense", "attention_layers": 12, "seed": 59},
+      {"variant": "resource_dense_l8_s47", "geometry": "dense", "attention_layers": 8, "seed": 47},
+      {"variant": "resource_dense_l8_s53", "geometry": "dense", "attention_layers": 8, "seed": 53},
+      {"variant": "resource_dense_l8_s59", "geometry": "dense", "attention_layers": 8, "seed": 59},
+      {"variant": "resource_orthogonal_l12_s47", "geometry": "orthogonal", "attention_layers": 12, "seed": 47},
+      {"variant": "resource_orthogonal_l12_s53", "geometry": "orthogonal", "attention_layers": 12, "seed": 53},
+      {"variant": "resource_orthogonal_l12_s59", "geometry": "orthogonal", "attention_layers": 12, "seed": 59},
+      {"variant": "resource_orthogonal_l8_s47", "geometry": "orthogonal", "attention_layers": 8, "seed": 47},
+      {"variant": "resource_orthogonal_l8_s53", "geometry": "orthogonal", "attention_layers": 8, "seed": 53},
+      {"variant": "resource_orthogonal_l8_s59", "geometry": "orthogonal", "attention_layers": 8, "seed": 59}
+    ]
+  },
+  "training": {
+    "corpus_words": 10000000,
+    "exposure_words": 100000000,
+    "strict_small": true,
+    "same_corpus": true,
+    "same_tokenizer": true,
+    "same_width": true,
+    "same_optimizer": true,
+    "same_checkpoint_schedule": true,
+    "saved_checkpoint_words": [1000000, 2000000, 3000000, 4000000, 5000000, 6000000, 7000000, 8000000, 9000000, 10000000, 20000000, 30000000, 40000000, 50000000, 60000000, 70000000, 80000000, 90000000, 100000000]
+  },
+  "assay": {
+    "source_study": "factorized_mechanism_v2",
+    "source_items_sha256": "0517ba2a8b7966698f08f7d2fb7742b40bbb9b2855f35111cb69f3533a7a5b25",
+    "source_item_spec_sha256": "0b33a75dc26136438d7bfe6463afc502efabd3464e1ded0d706a84a7b032c8b5",
+    "selection_seed": 20260717,
+    "selection_reads_new_models": false,
+    "items_per_assay": 1024,
+    "crossfit_folds": 5,
+    "regularization_c": 1.0,
+    "evaluation_words": [1000000, 5000000, 10000000, 20000000, 40000000, 60000000, 80000000, 100000000],
+    "primary_words": 40000000,
+    "asymptotic_words": 100000000,
+    "holdout_words": 100000,
+    "encoding_batch_size": 32
+  },
+  "estimands": {
+    "primary": "(orthogonal_8_minus_orthogonal_12) - (dense_8_minus_dense_12) at 40M; positive means factorization protects competence under depth reduction",
+    "secondary": [
+      "paired-seed interaction in normalized competence AUC from 1M to 40M",
+      "paired-seed interaction at 100M",
+      "first registered age reaching the competence criterion",
+      "held-out language-model loss interaction",
+      "competence per parameter and estimated attention-layer compute"
+    ],
+    "competence": "equal-weight mean of six cross-fitted direct-assay component accuracies",
+    "criterion": {
+      "minimum_component_accuracy": 0.65,
+      "required_components": 5
+    }
+  },
+  "inference": {
+    "seed_is_independent_training_unit": true,
+    "paired_by_seed": true,
+    "bootstrap_seed_resamples": 10000,
+    "bootstrap_seed": 20260718,
+    "directional_primary_hypothesis": true,
+    "report_all_components": true,
+    "no_checkpoint_selection": true,
+    "no_leaderboard_selection": true
+  },
+  "claim_boundary": "The study tests computational resource substitution in frozen relational competence under one matched BabyLM training recipe. It does not claim human equivalence, universal architectural optimality, or leaderboard superiority. The direct assays measure representational competence, while held-out loss is a general-performance control."
+}
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