Manuscript revision and pending experiment work, snapshot before restructuring

Clarity pass over the main text (36-item audit), Discussion rewrite and cut,
acknowledgements, Souly et al. as ref 62, lettered SI panels, model section
moved under Results; plus the untracked curriculum/society/compose/smol
configs, runners, figures, stats and tests that the SI already cites.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm

results/llm_curriculum_v5_cull/README.md

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+# llm_curriculum_v5_cull — differential reproduction in the six-generation population
+
+**Claim tested (manuscript revision 2026-09-12).** In the six-generation population recombination
+bought speed but not level: the declinable-merge arm led early and finished level with never merging.
+The population had no differential reproduction (lineages were never culled), and the Discussion
+predicted that with it a merged lineage's early lead would be inherited by more descendants and turn
+into a level advantage. This run adds truncation selection: after every generation's measurement the
+lowest-scoring lineage (all-families accuracy) is re-founded from the highest-scoring one, keeping
+its own curriculum slot (`cull: true`; `cull_step`/`inherit_slot` in `src/llm/curriculum.py`;
+ties leave the population alone). Arms: `isolated` + culling and declinable `society` + culling,
+read against the existing `isolated` and `veto` arms. Latin square, Qwen2.5-1.5B, 3 training seeds
+(`hpc/llm_cull.pbs`, ~35 min per seed on one L40S). Figure `curriculum_cull.png`; numbers from
+`figures/stats_llm_curriculum.py` and the per-generation tables printed in the revision log.
+
+### Result — parity again; the prediction is not supported
+- **Final best-lineage accuracy (all six families), mean of 3 seeds:** never merge 0.796; declinable
+  merge 0.792; never merge + culling **0.804**; declinable merge + culling **0.793**.
+- **Contrasts (per seed; mean ± 95% CI):** culled declinable − culled never-merge −0.011, −0.014,
+  −0.008 (**−0.011 ± 0.003**, below in 3/3); culled declinable − declinable +0.001 ± 0.007;
+  culled never-merge − never-merge +0.007 ± 0.030 (−0.022, +0.028, +0.017).
+- **The early lead survives, the level does not.** Best lineage at generation 1: declinable 0.680
+  and culled declinable 0.679 against never-merge 0.601 and culled never-merge 0.638; by generation
+  5 all four sit at 0.78–0.80. Population mean: culling lifts the *mean* early (generation 1: 0.577
+  vs 0.477 for the never-merge arms) because the best genome is copied into the worst slot, but the
+  final means converge too (0.775–0.786).
+- **Selection was active throughout:** exactly one cull per generation in every arm and seed (no
+  ties); the culled slot rotated among all three lineages. Declines in the culled declinable arm
+  rose with generation as before (0.33 → 1.00).
+- Retention of taught families at generation 6: 0.78–0.79 in all four arms.
+
+**Reading.** Under a curriculum that delivers every skill to every lineage, the ceiling is set by
+what one adapter can carry, and both sex and selection can only move a lineage toward that ceiling
+faster. Selection spreads the best genome; recombination assembles it earlier; neither raises it.
+The Fisher–Muller advantage in this population is a speed advantage, now shown with and without
+differential reproduction. Falsifier for the null (not triggered): culled declinable above culled
+never-merge in a majority of seeds by more than the seed-to-seed spread (~0.02). What would change
+the prediction is a curriculum in which skills are *not* guaranteed to every lineage (a lineage that
+never learns a family can only obtain it by merging), which is a different experiment.

results/llm_curriculum_v5_cull/s1/manifest.json

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+{
+  "experiment": "llm_curriculum_v5_cull",
+  "master_seed": 1,
+  "git_commit": null,
+  "python": "3.11.7",
+  "libraries": {
+    "numpy": "2.4.6",
+    "scipy": "1.17.1",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0",
+    "torch": "2.12.1",
+    "transformers": "5.16.1",
+    "peft": "0.20.0"
+  },
+  "rows": 511,
+  "results_sha256": "88e21029f057d0a40274b6253ea83f0ff178d3386cf85bcbdfda069b25d1c1c4",
+  "layer": "2",
+  "tier": "llm",
+  "base_model": "Qwen/Qwen2.5-1.5B",
+  "hard": false,
+  "curriculum": {
+    "families": [
+      "mnli",
+      "arc",
+      "hellaswag",
+      "squad",
+      "boolq",
+      "winogrande"
+    ],
+    "lineages": 3,
+    "generations": 6,
+    "arms": [
+      "isolated",
+      "society"
+    ],
+    "baselines": [],
+    "n_new": 300,
+    "n_replay": 150,
+    "operator": "linear",
+    "ancestor_depth": null,
+    "lora": {
+      "r": 16,
+      "alpha": 32
+    },
+    "allow_veto": true,
+    "merge_until": null,
+    "orders": null,
+    "cull": true
+  }
+}

results/llm_curriculum_v5_cull/s1/resolved_config.yaml

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+experiment: llm_curriculum_v5_cull
+seed: 1
+n_replicates: 1
+source_config:
+  experiment: llm_curriculum_v5_cull
+  kind: llm_curriculum
+  base_model: Qwen/Qwen2.5-1.5B
+  seed: 1
+  families:
+  - mnli
+  - arc
+  - hellaswag
+  - squad
+  - boolq
+  - winogrande
+  lineages: 3
+  generations: 6
+  arms:
+  - isolated
+  - society
+  baselines: []
+  allow_veto: true
+  n_new: 300
+  n_replay: 150
+  n_test: 60
+  n_val: 20
+  epochs: 3
+  lr: 0.0001
+  operator: linear
+  merge_weights:
+  - - 0.5
+    - 0.5
+  - - 0.3
+    - 0.7
+  - - 0.7
+    - 0.3
+  max_new_tokens: 48
+  batch_size: 48
+  train_batch_size: 4
+  train_max_len: 512
+  lora:
+    r: 16
+    alpha: 32
+  output:
+    dir: results/llm_curriculum_v5_cull/s1
+  cull: true
+  n_replicates: 1

results/llm_curriculum_v5_cull/s2/manifest.json

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+{
+  "experiment": "llm_curriculum_v5_cull",
+  "master_seed": 2,
+  "git_commit": null,
+  "python": "3.11.7",
+  "libraries": {
+    "numpy": "2.4.6",
+    "scipy": "1.17.1",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0",
+    "torch": "2.12.1",
+    "transformers": "5.16.1",
+    "peft": "0.20.0"
+  },
+  "rows": 511,
+  "results_sha256": "104c6e6be137e2b9ac5969a611323b12773697576720502cca9bc950b33def3c",
+  "layer": "2",
+  "tier": "llm",
+  "base_model": "Qwen/Qwen2.5-1.5B",
+  "hard": false,
+  "curriculum": {
+    "families": [
+      "mnli",
+      "arc",
+      "hellaswag",
+      "squad",
+      "boolq",
+      "winogrande"
+    ],
+    "lineages": 3,
+    "generations": 6,
+    "arms": [
+      "isolated",
+      "society"
+    ],
+    "baselines": [],
+    "n_new": 300,
+    "n_replay": 150,
+    "operator": "linear",
+    "ancestor_depth": null,
+    "lora": {
+      "r": 16,
+      "alpha": 32
+    },
+    "allow_veto": true,
+    "merge_until": null,
+    "orders": null,
+    "cull": true
+  }
+}

results/llm_curriculum_v5_cull/s2/resolved_config.yaml

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+experiment: llm_curriculum_v5_cull
+seed: 2
+n_replicates: 1
+source_config:
+  experiment: llm_curriculum_v5_cull
+  kind: llm_curriculum
+  base_model: Qwen/Qwen2.5-1.5B
+  seed: 2
+  families:
+  - mnli
+  - arc
+  - hellaswag
+  - squad
+  - boolq
+  - winogrande
+  lineages: 3
+  generations: 6
+  arms:
+  - isolated
+  - society
+  baselines: []
+  allow_veto: true
+  n_new: 300
+  n_replay: 150
+  n_test: 60
+  n_val: 20
+  epochs: 3
+  lr: 0.0001
+  operator: linear
+  merge_weights:
+  - - 0.5
+    - 0.5
+  - - 0.3
+    - 0.7
+  - - 0.7
+    - 0.3
+  max_new_tokens: 48
+  batch_size: 48
+  train_batch_size: 4
+  train_max_len: 512
+  lora:
+    r: 16
+    alpha: 32
+  output:
+    dir: results/llm_curriculum_v5_cull/s2
+  cull: true
+  n_replicates: 1

results/llm_curriculum_v5_cull/s3/manifest.json

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+{
+  "experiment": "llm_curriculum_v5_cull",
+  "master_seed": 3,
+  "git_commit": null,
+  "python": "3.11.7",
+  "libraries": {
+    "numpy": "2.4.6",
+    "scipy": "1.17.1",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0",
+    "torch": "2.12.1",
+    "transformers": "5.16.1",
+    "peft": "0.20.0"
+  },
+  "rows": 511,
+  "results_sha256": "2669b74029d85d4197dca9229e3b1ab8769177f18e05fd08615f7bc2790b2945",
+  "layer": "2",
+  "tier": "llm",
+  "base_model": "Qwen/Qwen2.5-1.5B",
+  "hard": false,
+  "curriculum": {
+    "families": [
+      "mnli",
+      "arc",
+      "hellaswag",
+      "squad",
+      "boolq",
+      "winogrande"
+    ],
+    "lineages": 3,
+    "generations": 6,
+    "arms": [
+      "isolated",
+      "society"
+    ],
+    "baselines": [],
+    "n_new": 300,
+    "n_replay": 150,
+    "operator": "linear",
+    "ancestor_depth": null,
+    "lora": {
+      "r": 16,
+      "alpha": 32
+    },
+    "allow_veto": true,
+    "merge_until": null,
+    "orders": null,
+    "cull": true
+  }
+}

results/llm_curriculum_v5_cull/s3/resolved_config.yaml

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+experiment: llm_curriculum_v5_cull
+seed: 3
+n_replicates: 1
+source_config:
+  experiment: llm_curriculum_v5_cull
+  kind: llm_curriculum
+  base_model: Qwen/Qwen2.5-1.5B
+  seed: 3
+  families:
+  - mnli
+  - arc
+  - hellaswag
+  - squad
+  - boolq
+  - winogrande
+  lineages: 3
+  generations: 6
+  arms:
+  - isolated
+  - society
+  baselines: []
+  allow_veto: true
+  n_new: 300
+  n_replay: 150
+  n_test: 60
+  n_val: 20
+  epochs: 3
+  lr: 0.0001
+  operator: linear
+  merge_weights:
+  - - 0.5
+    - 0.5
+  - - 0.3
+    - 0.7
+  - - 0.7
+    - 0.3
+  max_new_tokens: 48
+  batch_size: 48
+  train_batch_size: 4
+  train_max_len: 512
+  lora:
+    r: 16
+    alpha: 32
+  output:
+    dir: results/llm_curriculum_v5_cull/s3
+  cull: true
+  n_replicates: 1