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

configs/llm/curriculum_v5_s1.yaml

@@ -0,0 +1,37 @@
+# The curriculum society campaign, seed 1 (prereg v4, v5 curriculum — tasks/prereg-llm-society-v4.md).
+# Runs all four arms then the three matched-budget baselines. The PBS array (hpc/llm_curriculum.pbs)
+# derives one (seed, arm) config per element from this template for seeds 2-3.
+#
+# Six real-dataset families selected by calibration (§8a): five pass C1 at >= 0.60, winogrande (0.57)
+# is the sixth under the recorded amendment. L = 3 lineages, G = 6 generations; the cyclic Latin square
+# gives complementarity 1.0 at generation 2 and 0.0 at generation 6 (H6 tests the shape).
+#
+# Arms:  isolated (no recombination) · society (decorrelated contemporary) · society_dry (contemporary,
+#        self-generated replay) · seed_bank (own ancestor at t-3 — temporal complementarity)
+# Baselines at matched examples:  sequential · single_shot_merge · joint
+experiment: llm_curriculum_v5
+kind: llm_curriculum
+base_model: Qwen/Qwen2.5-1.5B      # base, not Instruct: measured 0.011 on these families untrained
+seed: 1
+families: [mnli, arc, hellaswag, squad, boolq, winogrande]
+lineages: 3
+generations: 6
+arms: [isolated, society, society_dry, seed_bank]
+baselines: [sequential, single_shot_merge, joint]
+n_new: 300                # founder/acquisition budget that passed C1 (1200 destabilised training)
+n_replay: 150             # fixed TOTAL, split across families seen: protection thins as the curriculum grows
+n_test: 60                # per family, from the TEST split — reporting only
+n_val: 20                 # per family, from the TRAIN split — merge-weight selection only
+epochs: 3
+lr: 1.0e-4                # continued-training rate (fresh founders use train_lora_on_tasks' 2e-4)
+ancestor_depth: 3
+operator: linear
+merge_weights: [[0.5, 0.5], [0.3, 0.7], [0.7, 0.3]]
+baseline_weights: [[0.333, 0.333, 0.334], [0.5, 0.25, 0.25], [0.25, 0.5, 0.25], [0.25, 0.25, 0.5]]
+max_new_tokens: 48
+batch_size: 24
+train_batch_size: 2
+train_max_len: 512
+resume: true
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_curriculum_v5/s1}