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/compose_gate.yaml

@@ -0,0 +1,31 @@
+# Generation-0 gate for the composition experiment (prereg v3 §4: C1 base, C3 specialists,
+# C4 replication). Trains the two founders and measures everything at t=0 without iterating.
+#
+# Base choice (C1, measured 2026-09-07): Qwen2.5-1.5B **base, not Instruct**. Zero-shot GSM-Hard
+# program-aided: Instruct-1.5B 0.500, Instruct-3B 0.417, base-3B 0.633 — all far too strong, since a
+# base that already has the skills makes the specialists' contribution vacuous. Base-1.5B scores
+# 0.067, within noise of the 0.059 Llama-2-7B starting point in LoRA Soups (COLING 2025), so the
+# published composition effect has room to appear.
+experiment: llm_compose_gate
+kind: llm_compose
+base_model: Qwen/Qwen2.5-1.5B
+seed: 1
+generations: 0            # gate only: train founders, measure t=0, stop
+arms: [dry]
+n_hard: 150               # composed target (GSM8k-Hard, program-aided) — SE ~0.04
+n_gsm8k: 100              # math own-skill probe
+n_mbpp: 80                # code own-skill probe (execution-verified)
+n_probe: 40               # shared prompts for rho
+k_inherit: 300
+epochs: 3
+conf_gate: 0.85
+g: 0.10
+spec_train: 1200
+spec_epochs: 3
+max_new_tokens: 320
+batch_size: 16
+score_batch_size: 4
+train_batch_size: 2       # 16 GB A4000; the fp32 logit upcast at vocab 152k is the constraint
+train_max_len: 448
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_compose_gate}

configs/llm/compose_gate_math500.yaml

@@ -0,0 +1,34 @@
+# Generation-0 gate, second configuration (prereg v3 §4a): the composed target is MATH-500
+# (competition maths, level >= 3, numeric answers), and merge weights are *selected* on a disjoint
+# validation split rather than fixed at 0.5/0.5.
+#
+# Why: on GSM8k-Hard the code parent alone reaches 0.427, because once code removes the arithmetic
+# burden the base's own reasoning suffices — so maths is not scarce and E8's premise fails. MetaMathQA
+# is built from GSM8K *and* MATH, so MATH-500 tests reasoning the specialist has and the base lacks.
+# Founders are shared with the first gate (same experiment name), so this costs evaluation only.
+experiment: llm_compose_gate
+kind: llm_compose
+base_model: Qwen/Qwen2.5-1.5B
+seed: 1
+generations: 0
+arms: [dry]
+target: math500
+n_hard: 120               # test split (level>=3 pool is 271; 70% test / 30% val, disjoint)
+n_hard_val: 50            # val split, screens the merge weights only
+merge_weights: [[0.5, 0.5], [0.3, 0.7], [0.2, 0.8]]
+n_gsm8k: 100
+n_mbpp: 80
+n_probe: 40
+k_inherit: 300
+epochs: 3
+conf_gate: 0.85
+g: 0.10
+spec_train: 1200
+spec_epochs: 3
+max_new_tokens: 320
+batch_size: 16
+score_batch_size: 8
+train_batch_size: 2
+train_max_len: 448
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_compose_gate_math500}

configs/llm/compose_s1.yaml

@@ -0,0 +1,55 @@
+# The composition campaign, seed 1 (prereg v3, amended after the generation-0 sweep of 2026-09-07).
+#
+# Arms. The gen-0 sweep found that the merge *weighting* dominates the operator: the a-priori 0.5/0.5
+# blend fails under both operators (surplus -0.020 cat, -0.093 linear) while a selected weight passes
+# (+0.080 linear at 0.2/0.8, +0.027 cat at 0.3/0.7). Weights are therefore chosen each generation on a
+# disjoint validation split (E10, directed recombination) in every arm, and the operator is an
+# explicit per-arm setting:
+#   dry      — linear operator, no grounding      [H2, H3, H5: does composition survive drift?]
+#   grounded — linear operator, g = 0.10          [H4: does immigration arrest it?]
+#   dry_cat  — concatenation operator, no grounding [H6, revised: does the operator ordering hold
+#              across generations, or only at gen 0?]
+experiment: llm_compose
+kind: llm_compose
+base_model: Qwen/Qwen2.5-1.5B
+seed: 1
+generations: 6
+arms:
+- dry
+- grounded
+- dry_cat
+g: 0.1
+n_hard: 150
+n_gsm8k: 150
+n_mbpp: 100
+n_probe: 60
+k_inherit: 300
+epochs: 3
+conf_gate: 0.85
+spec_train: 1200
+spec_epochs: 3
+max_new_tokens: 320
+batch_size: 16
+score_batch_size: 4
+train_batch_size: 2
+train_max_len: 448
+resume: true
+lora:
+  r: 16
+  alpha: 32
+output:
+  dir: results/llm_compose/s1
+arm_ops:
+  dry: linear
+  grounded: linear
+  dry_cat: cat
+n_hard_val: 60
+merge_weights:
+- - 0.5
+  - 0.5
+- - 0.3
+  - 0.7
+- - 0.2
+  - 0.8
+- - 0.1
+  - 0.9

configs/llm/curriculum_g2.yaml

@@ -0,0 +1,27 @@
+# G2 probe (prereg v4 §5): does forgetting occur AT ALL under the real curriculum length?
+# The 3-family gate showed none — but with only 3 families, ample adapter capacity and a generous
+# replay budget, none was expected. This runs the full nine-family curriculum on ONE lineage with
+# replay switched OFF, which is the harshest condition the design can offer. If early families still
+# do not decay, the families are too mutually compatible and too easy for their own good, and the
+# society has no forgetting to rescue — a design-level stop.
+experiment: llm_curriculum_g2
+kind: llm_curriculum
+base_model: Qwen/Qwen2.5-1.5B
+seed: 1
+families: [strings, setops, numtheory, mixedtoken, digits, alphabet, prime, wordlen, roman]
+lineages: 1
+generations: 9
+arms: [isolated]
+baselines: []
+n_new: 300
+n_replay: 0             # replay OFF — the harshest forgetting condition
+n_test: 60
+n_val: 30
+epochs: 3
+lr: 1.0e-4
+max_new_tokens: 32
+batch_size: 32
+train_batch_size: 2
+train_max_len: 448
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_curriculum_g2}

configs/llm/curriculum_gate.yaml

@@ -0,0 +1,28 @@
+# Gate run for the curriculum society (prereg v4 §5). G0 is the decisive one: with a single lineage
+# and no recombination, does cumulative capability CLIMB across generations? If capability cannot
+# accumulate in the simplest arm, no outcome of the full design is interpretable — stop.
+# Also exercises G1 (inheritance transmits), G2 (forgetting occurs without full replay) and, with
+# lineages=3, G3 (recombination combines disjoint knowledge).
+experiment: llm_curriculum_gate
+kind: llm_curriculum
+base_model: Qwen/Qwen2.5-1.5B
+seed: 1
+families: [strings, setops, numtheory]      # 3 families, 3 generations — the smallest climb test
+lineages: 3
+generations: 3
+arms: [isolated, society]
+baselines: []
+n_new: 300
+n_replay: 120
+n_test: 60
+n_val: 30
+epochs: 3
+lr: 1.0e-4
+operator: linear
+merge_weights: [[0.5, 0.5], [0.3, 0.7], [0.7, 0.3]]
+max_new_tokens: 32
+batch_size: 32
+train_batch_size: 2
+train_max_len: 448
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_curriculum_gate}

configs/llm/curriculum_v5_calib.yaml

@@ -0,0 +1,27 @@
+# Calibration for the real-dataset curriculum (prereg v4 §5 gates, re-run on the new families).
+# Stage A (this config): base accuracy on every candidate, and one specialist per candidate trained
+# from the base on its train split — the C1 band is base <= 0.40 and specialist >= 0.60.
+# Stage B (curriculum_v5_g2.yaml, after selection): one lineage, zero replay, all chosen families in
+# sequence — the forgetting probe; mean drop across families learned before the last must be >= 0.15,
+# and it must not be carried by a single family (the v4 failure: one pair at +0.65, the rest ~0).
+experiment: llm_curriculum_v5_calib
+kind: llm_curriculum
+base_model: Qwen/Qwen2.5-1.5B
+seed: 1
+families: [gsm8k, mbpp, boolq, mnli, sst2, csqa, arc, winogrande, squad, nq_open, hellaswag]
+lineages: 11              # one lineage per candidate = each trains only its own family at gen 0
+generations: 1            # gen 0 only: founders; the recorded acc_* rows ARE the specialist table
+arms: [isolated]
+baselines: []
+n_new: 300
+n_replay: 0
+n_test: 60
+n_val: 20
+epochs: 3
+lr: 1.0e-4
+max_new_tokens: 48        # spans / short text need more room than a label
+batch_size: 24
+train_batch_size: 2
+train_max_len: 512
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_curriculum_v5_calib}

configs/llm/curriculum_v5_calib_b.yaml

@@ -0,0 +1,24 @@
+# Stage A, second pass: the six candidates that missed the C1 band at 300 founder examples, retrained
+# at the budget every working specialist in this project has used (v2, v3: 1200 x 3 epochs).
+# Decides whether the curriculum has six families or nine.
+experiment: llm_curriculum_v5_calib_b
+kind: llm_curriculum
+base_model: Qwen/Qwen2.5-1.5B
+seed: 1
+families: [gsm8k, mbpp, sst2, csqa, winogrande, nq_open]
+lineages: 6
+generations: 1
+arms: [isolated]
+baselines: []
+n_new: 1200
+n_replay: 0
+n_test: 60
+n_val: 20
+epochs: 3
+lr: 1.0e-4
+max_new_tokens: 48
+batch_size: 24
+train_batch_size: 2
+train_max_len: 512
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_curriculum_v5_calib_b}

configs/llm/curriculum_v5_cull.yaml

@@ -0,0 +1,48 @@
+# Differential reproduction (manuscript revision 2026-09-12): `cull: true` re-founds the lowest-scoring
+# lineage from the highest-scoring one after every generation's measurement (truncation selection at
+# fixed N = 3; the slot keeps its curriculum order). Latin square as in v5. Arms: isolated + cull
+# (selection without recombination) and declinable society + cull (selection with recombination),
+# read against the existing isolated and veto arms. Prediction (Discussion): recombination's early
+# lead becomes a level advantage; parity again is the null. Seeds via hpc/llm_cull.pbs.
+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: 24
+train_batch_size: 2
+train_max_len: 512
+lora:
+  r: 16
+  alpha: 32
+output:
+  dir: results/llm_curriculum_v5_cull
+cull: true

configs/llm/curriculum_v5_decor.yaml

@@ -0,0 +1,35 @@
+# Decorrelated curriculum (manuscript review, 2026-09-11). In the Latin square partner complementarity
+# falls monotonically with generation (1.0, 1.0, 0.8, 0.67, 0.33, 0.0), so the veto's acceptance curve
+# is collinear with adapter age. Here every lineage starts with the same non-destroyer family (mnli),
+# then diverges maximally, then converges: complementarity 0.00, 0.67, 0.70, 0.58, 0.33, 0.00 by
+# generation. Same six families, same G, destroyers (boolq, winogrande) spread across lineages as
+# in the Latin square. Arms: the declinable merge (`society` + `allow_veto`) and its never-merge
+# reference under the same curriculum. Pre-registered readout: tasks/prereg-llm-society-v4.md §8g.
+experiment: llm_curriculum_v5_decor
+kind: llm_curriculum
+base_model: Qwen/Qwen2.5-1.5B
+seed: 1
+families: [mnli, arc, hellaswag, squad, boolq, winogrande]
+orders:
+  - [mnli, arc, hellaswag, squad, boolq, winogrande]
+  - [mnli, squad, boolq, winogrande, arc, hellaswag]
+  - [mnli, winogrande, arc, hellaswag, squad, boolq]
+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: 1.0e-4
+operator: linear
+merge_weights: [[0.5, 0.5], [0.3, 0.7], [0.7, 0.3]]
+max_new_tokens: 48
+batch_size: 24
+train_batch_size: 2
+train_max_len: 512
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_curriculum_v5_decor}

configs/llm/curriculum_v5_early.yaml

@@ -0,0 +1,66 @@
+# Conflict-early curriculum (manuscript revision 2026-09-12): the two families whose answer
+# conventions conflict (boolq yes/no, winogrande 1/2) arrive in generations 1-2 of every lineage; the
+# four compatible families follow in rotated orders. Paired with curriculum_v5_late (same families,
+# conflict in generations 5-6) to decouple the ARRIVAL of conflicting conventions from adapter age and
+# skill count, which rise identically in both. Arms: isolated + declinable society (decline timing).
+# Seeds 1-3 via hpc/llm_curriculum_timing.pbs.
+experiment: llm_curriculum_v5_early
+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: 24
+train_batch_size: 2
+train_max_len: 512
+lora:
+  r: 16
+  alpha: 32
+output:
+  dir: results/llm_curriculum_v5_early
+orders:
+- - boolq
+  - winogrande
+  - mnli
+  - arc
+  - hellaswag
+  - squad
+- - winogrande
+  - boolq
+  - arc
+  - hellaswag
+  - squad
+  - mnli
+- - boolq
+  - winogrande
+  - hellaswag
+  - squad
+  - mnli
+  - arc

configs/llm/curriculum_v5_early_obl.yaml

@@ -0,0 +1,61 @@
+# Obligate-merge arm of the conflict-early curriculum (collapse timing): same orders as
+# curriculum_v5_early, society arm only, no veto. Seeds 1-3 via hpc/llm_curriculum_timing.pbs.
+experiment: llm_curriculum_v5_early_obl
+kind: llm_curriculum
+base_model: Qwen/Qwen2.5-1.5B
+seed: 1
+families:
+- mnli
+- arc
+- hellaswag
+- squad
+- boolq
+- winogrande
+lineages: 3
+generations: 6
+arms:
+- society
+baselines: []
+allow_veto: false
+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: 24
+train_batch_size: 2
+train_max_len: 512
+lora:
+  r: 16
+  alpha: 32
+output:
+  dir: results/llm_curriculum_v5_early_obl
+orders:
+- - boolq
+  - winogrande
+  - mnli
+  - arc
+  - hellaswag
+  - squad
+- - winogrande
+  - boolq
+  - arc
+  - hellaswag
+  - squad
+  - mnli
+- - boolq
+  - winogrande
+  - hellaswag
+  - squad
+  - mnli
+  - arc

configs/llm/curriculum_v5_g2.yaml

@@ -0,0 +1,25 @@
+# Stage B (prereg v4 §5 G2, v5 families): does forgetting occur, and is it general rather than one pair?
+# One lineage, zero replay, all six selected families in sequence at the founder budget that passed C1.
+# Pass: mean drop across families learned before the last >= 0.15, with no single family carrying
+# more than 50% of the total drop (the v4 failure mode: one pair at +0.65, the rest ~0).
+experiment: llm_curriculum_v5_g2
+kind: llm_curriculum
+base_model: Qwen/Qwen2.5-1.5B
+seed: 1
+families: [mnli, arc, hellaswag, squad, boolq, winogrande]
+lineages: 1
+generations: 6
+arms: [isolated]
+baselines: []
+n_new: 300
+n_replay: 0
+n_test: 60
+n_val: 20
+epochs: 3
+lr: 1.0e-4
+max_new_tokens: 48
+batch_size: 24
+train_batch_size: 2
+train_max_len: 512
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_curriculum_v5_g2}

configs/llm/curriculum_v5_late.yaml

@@ -0,0 +1,63 @@
+# Conflict-late curriculum (manuscript revision 2026-09-12): the four compatible families first, in
+# rotated orders; boolq (yes/no) and winogrande (1/2) arrive in generations 5-6. Pair of
+# curriculum_v5_early. Arms: isolated + declinable society (decline timing).
+experiment: llm_curriculum_v5_late
+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: 24
+train_batch_size: 2
+train_max_len: 512
+lora:
+  r: 16
+  alpha: 32
+output:
+  dir: results/llm_curriculum_v5_late
+orders:
+- - mnli
+  - arc
+  - hellaswag
+  - squad
+  - boolq
+  - winogrande
+- - arc
+  - hellaswag
+  - squad
+  - mnli
+  - winogrande
+  - boolq
+- - hellaswag
+  - squad
+  - mnli
+  - arc
+  - boolq
+  - winogrande

configs/llm/curriculum_v5_late_obl.yaml

@@ -0,0 +1,61 @@
+# Obligate-merge arm of the conflict-late curriculum (collapse timing): same orders as
+# curriculum_v5_late, society arm only, no veto. Seeds 1-3 via hpc/llm_curriculum_timing.pbs.
+experiment: llm_curriculum_v5_late_obl
+kind: llm_curriculum
+base_model: Qwen/Qwen2.5-1.5B
+seed: 1
+families:
+- mnli
+- arc
+- hellaswag
+- squad
+- boolq
+- winogrande
+lineages: 3
+generations: 6
+arms:
+- society
+baselines: []
+allow_veto: false
+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: 24
+train_batch_size: 2
+train_max_len: 512
+lora:
+  r: 16
+  alpha: 32
+output:
+  dir: results/llm_curriculum_v5_late_obl
+orders:
+- - mnli
+  - arc
+  - hellaswag
+  - squad
+  - boolq
+  - winogrande
+- - arc
+  - hellaswag
+  - squad
+  - mnli
+  - winogrande
+  - boolq
+- - hellaswag
+  - squad
+  - mnli
+  - arc
+  - boolq
+  - winogrande

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}

configs/llm/curriculum_v5_stop3.yaml

@@ -0,0 +1,30 @@
+# Forced-stop control for the declinable merge (manuscript review, 2026-09-11). Identical to the v5
+# `society` arm except that recombination is switched off from generation 3 onward (`merge_until: 3`):
+# obligate merging at generations 0-2, none afterwards. This is the fixed "merge early, then stop"
+# schedule the veto arm must be compared against, because in the seed-1 veto run lineages declined
+# 1/3 of merges at generations 0-2 and 3/3 at 3-5. Pre-registered readout: tasks/prereg-llm-society-v4.md §8g.
+experiment: llm_curriculum_v5_stop3
+kind: llm_curriculum
+base_model: Qwen/Qwen2.5-1.5B
+seed: 1
+families: [mnli, arc, hellaswag, squad, boolq, winogrande]
+lineages: 3
+generations: 6
+arms: [society]
+baselines: []
+allow_veto: false
+merge_until: 3
+n_new: 300
+n_replay: 150
+n_test: 60
+n_val: 20
+epochs: 3
+lr: 1.0e-4
+operator: linear
+merge_weights: [[0.5, 0.5], [0.3, 0.7], [0.7, 0.3]]
+max_new_tokens: 48
+batch_size: 24
+train_batch_size: 2
+train_max_len: 512
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_curriculum_v5_stop3}

configs/llm/curriculum_v5_veto.yaml

@@ -0,0 +1,31 @@
+# The veto arm (GG, 2026-09-08): identical to v5's `society` arm in every respect except that
+# "keep the parent unchanged" is an admissible offspring. v5 made merging obligate — every lineage
+# merged every generation and had to accept the result, which is not what anyone would build.
+#
+# Reference points from v5 seed 1: society 0.211 (obligate merging) vs isolated 0.814 (never merges).
+# The diagnostic that matters is `veto_used`: if lineages decline every merge the arm degenerates to
+# `isolated`, and that is itself the answer.
+experiment: llm_curriculum_v5_veto
+kind: llm_curriculum
+base_model: Qwen/Qwen2.5-1.5B
+seed: 1
+families: [mnli, arc, hellaswag, squad, boolq, winogrande]
+lineages: 3
+generations: 6
+arms: [society]
+baselines: []
+allow_veto: true
+n_new: 300
+n_replay: 150
+n_test: 60
+n_val: 20
+epochs: 3
+lr: 1.0e-4
+operator: linear
+merge_weights: [[0.5, 0.5], [0.3, 0.7], [0.7, 0.3]]
+max_new_tokens: 48
+batch_size: 24
+train_batch_size: 2
+train_max_len: 512
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_curriculum_v5_veto}

configs/llm/merge_seeds_smol.yaml

@@ -0,0 +1,30 @@
+# Second base lineage (manuscript revision 2026-09-12): the Fisher-Muller replication of merge_seeds on
+# SmolLM2-1.7B-Instruct (HuggingFaceTB, Apache-2.0, Llama architecture) instead of Qwen. adapters_dir is
+# moved because the specialist cache is keyed by family and seed only. Runs via hpc/llm_smol.pbs.
+experiment: llm_merge_seeds_smol
+kind: llm_merge
+seed: 1
+seeds:
+- 1
+- 2
+- 3
+- 4
+- 5
+n_replicates: 1
+base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
+families:
+- lists
+- strings
+- arith
+n_train: 600
+n_test: 100
+epochs: 3
+lora:
+  r: 16
+  alpha: 32
+merges:
+- soup
+- ties
+output:
+  dir: results/llm_merge_seeds_smol
+adapters_dir: models/llm_smol

configs/llm/moe_hard_seeds_smol.yaml

@@ -0,0 +1,31 @@
+# Second base lineage (manuscript revision 2026-09-12): the headroom-rule replication of moe_hard_seeds
+# (union vs fusion, hard tasks, 3 seeds) on SmolLM2-1.7B-Instruct. Runs via hpc/llm_smol.pbs.
+experiment: llm_moe_hard_seeds_smol
+kind: llm_moe
+seed: 1
+seeds:
+- 1
+- 2
+- 3
+n_replicates: 1
+base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
+hard: true
+families:
+- lists
+- strings
+- arith
+n_train: 400
+n_test: 80
+n_route: 32
+epochs: 3
+lora:
+  r: 16
+  alpha: 32
+operators:
+- soup
+- ties
+- moe_oracle
+- moe_learned
+output:
+  dir: results/llm_moe_hard_seeds_smol
+adapters_dir: models/llm_smol

configs/llm/society_v2_calib_a.yaml

@@ -0,0 +1,17 @@
+# Calibration stage A (prereg §4, gates C1a-c + C4): every candidate family's base / specialist
+# accuracy, specialist confidence AUC, pairwise functional conflict, gen-0 behavioural distance.
+# GG reviews the printed table and fixes the 12-family set in society_v2_calib_b.yaml + the campaign.
+# ~1 h on a 16 GB GPU (17 specialists at 600 x 3 epochs, cached and reused by stage B).
+experiment: llm_society_v2_calib_a
+kind: llm_society_calib
+stage: families
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 1
+families: [lists, strings, arith, roman, binary, sortletters, setops, numtheory, mixedtoken,
+           caesar, vectors, progression, charfreq, digits, liststats, alphabet, prime]
+n_test: 100
+n_probe: 10
+spec_train: 600
+spec_epochs: 3
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_society_v2_calib_a}

configs/llm/society_v2_calib_a2.yaml

@@ -0,0 +1,20 @@
+# Calibration stage A, second pass (prereg §4 amendments, 2026-09-07): the first pass left only six
+# families in band. Changes, each recorded in the prereg: founder budget 600 -> 1200 x 3 (uniform);
+# roman / binary / prime prompt spaces enlarged; three new learnable candidates (wordlen,
+# lettercount, sumeven); the specialist UPPER bound relaxed 0.90 -> 1.00 (the headroom argument it
+# encoded concerns fusion composing to a ceiling, not transmission/assembly). Families whose
+# specialist scored <= 0.20 in pass 1 (sortletters, caesar, progression) are not re-trained.
+experiment: llm_society_v2_calib_a2
+kind: llm_society_calib
+stage: families
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 1
+families: [lists, strings, arith, roman, binary, setops, numtheory, mixedtoken, vectors, charfreq,
+           digits, liststats, alphabet, prime, wordlen, lettercount, sumeven]
+spec_hi: 1.0
+n_test: 100
+n_probe: 10
+spec_train: 1200
+spec_epochs: 3
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_society_v2_calib_a2}

configs/llm/society_v2_calib_b.yaml

@@ -0,0 +1,25 @@
+# Calibration stage B (prereg §4, gates C2 / C3 / C5) over the 12 families chosen from stage A.
+# Run three times with stage: transmission | cross | consensus (see Makefile `llm-society-calib`).
+# Families = stage-A pass-2 option 1 (L=9, gate 0.41; prereg §4a) — pending GG's go.
+experiment: llm_society_v2_calib_b
+kind: llm_society_calib
+stage: transmission
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 1
+families: [strings, setops, numtheory, mixedtoken, digits, alphabet, prime, wordlen, roman]
+# C2: examples-per-family k and epochs to sweep; probe_families are the three whose retention is
+# measured (spread across answer types: list / word / int).
+probe_families: [setops, alphabet, digits]   # list / letter / integer answers
+ks: [25, 50, 100, 150]
+epochs_grid: [2, 3]
+# C3: the two-founder cross (union-distil vs best-of-6 linear-merge-distil)
+cross: [setops, alphabet]
+k_inherit: 100
+epochs: 3
+n_candidates: 6
+n_test: 100
+n_probe: 10
+spec_train: 1200
+spec_epochs: 3
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_society_v2_calib_b}

configs/llm/society_v2_calib_c2b.yaml

@@ -0,0 +1,20 @@
+# C2b (prereg §4a amendment under review): confidence-gated inheritance. The pre-registered C2 failed
+# (retention ≤ 0.81 at k ≤ 150 despite competent supply): the child fits eight families of confident
+# garbage alongside the one competent family. Here the child learns only prompts its source is
+# confident on (verifier-free). Measures retention by gate τ, the Youden τ* separating own- from
+# off-family confidence, and the off-family harm of ungated inheritance.
+experiment: llm_society_v2_calib_b_transmission_conf
+kind: llm_society_calib
+stage: transmission_conf
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 1
+families: [strings, setops, numtheory, mixedtoken, digits, alphabet, prime, wordlen, roman]
+probe_families: [setops, alphabet, digits]
+k_pool: 300          # prompts per family answered by the source (2700 total); gate keeps a subset
+taus: [0.5, 0.7, 0.85]
+epochs: 3
+n_test: 100
+spec_train: 1200
+spec_epochs: 3
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_society_v2_calib_b_transmission_conf}

configs/llm/society_v2_calib_c3_gated.yaml

@@ -0,0 +1,43 @@
+experiment: llm_society_v2_calib_b_cross_gated
+kind: llm_society_calib
+stage: cross
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 1
+families:
+- strings
+- setops
+- numtheory
+- mixedtoken
+- digits
+- alphabet
+- prime
+- wordlen
+- roman
+probe_families:
+- setops
+- alphabet
+- digits
+ks:
+- 25
+- 50
+- 100
+- 150
+epochs_grid:
+- 2
+- 3
+cross:
+- setops
+- alphabet
+k_inherit: 300
+epochs: 3
+n_candidates: 6
+n_test: 100
+n_probe: 10
+spec_train: 1200
+spec_epochs: 3
+lora:
+  r: 16
+  alpha: 32
+output:
+  dir: results/llm_society_v2_calib_b_cross_gated
+conf_gate: 0.5

configs/llm/society_v2_calib_c3b_e6.yaml

@@ -0,0 +1,43 @@
+experiment: llm_society_v2_calib_c3b_e6
+kind: llm_society_calib
+stage: cross
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 1
+families:
+- strings
+- setops
+- numtheory
+- mixedtoken
+- digits
+- alphabet
+- prime
+- wordlen
+- roman
+probe_families:
+- setops
+- alphabet
+- digits
+ks:
+- 25
+- 50
+- 100
+- 150
+epochs_grid:
+- 2
+- 3
+cross:
+- setops
+- alphabet
+k_inherit: 300
+epochs: 6
+n_candidates: 6
+n_test: 100
+n_probe: 10
+spec_train: 1200
+spec_epochs: 3
+lora:
+  r: 16
+  alpha: 32
+output:
+  dir: results/llm_society_v2_calib_c3b_e6
+conf_gate: 0.5

configs/llm/society_v2_calib_c3b_r64.yaml

@@ -0,0 +1,43 @@
+experiment: llm_society_v2_calib_c3b_r64
+kind: llm_society_calib
+stage: cross
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 1
+families:
+- strings
+- setops
+- numtheory
+- mixedtoken
+- digits
+- alphabet
+- prime
+- wordlen
+- roman
+probe_families:
+- setops
+- alphabet
+- digits
+ks:
+- 25
+- 50
+- 100
+- 150
+epochs_grid:
+- 2
+- 3
+cross:
+- setops
+- alphabet
+k_inherit: 300
+epochs: 3
+n_candidates: 6
+n_test: 100
+n_probe: 10
+spec_train: 1200
+spec_epochs: 3
+lora:
+  r: 64
+  alpha: 128
+output:
+  dir: results/llm_society_v2_calib_c3b_r64
+conf_gate: 0.5

configs/llm/society_v2_calib_c3b_r64e6.yaml

@@ -0,0 +1,43 @@
+experiment: llm_society_v2_calib_c3b_r64e6
+kind: llm_society_calib
+stage: cross
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 1
+families:
+- strings
+- setops
+- numtheory
+- mixedtoken
+- digits
+- alphabet
+- prime
+- wordlen
+- roman
+probe_families:
+- setops
+- alphabet
+- digits
+ks:
+- 25
+- 50
+- 100
+- 150
+epochs_grid:
+- 2
+- 3
+cross:
+- setops
+- alphabet
+k_inherit: 300
+epochs: 6
+n_candidates: 6
+n_test: 100
+n_probe: 10
+spec_train: 1200
+spec_epochs: 3
+lora:
+  r: 64
+  alpha: 128
+output:
+  dir: results/llm_society_v2_calib_c3b_r64e6
+conf_gate: 0.5

configs/llm/society_v2_calib_c3c_t85e3.yaml

@@ -0,0 +1,43 @@
+experiment: llm_society_v2_calib_c3c_t85e3
+kind: llm_society_calib
+stage: cross
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 1
+families:
+- strings
+- setops
+- numtheory
+- mixedtoken
+- digits
+- alphabet
+- prime
+- wordlen
+- roman
+probe_families:
+- setops
+- alphabet
+- digits
+ks:
+- 25
+- 50
+- 100
+- 150
+epochs_grid:
+- 2
+- 3
+cross:
+- setops
+- alphabet
+k_inherit: 300
+epochs: 3
+n_candidates: 6
+n_test: 100
+n_probe: 10
+spec_train: 1200
+spec_epochs: 3
+lora:
+  r: 16
+  alpha: 32
+output:
+  dir: results/llm_society_v2_calib_c3c_t85e3
+conf_gate: 0.85

configs/llm/society_v2_calib_c3c_t85e6.yaml

@@ -0,0 +1,43 @@
+experiment: llm_society_v2_calib_c3c_t85e6
+kind: llm_society_calib
+stage: cross
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 1
+families:
+- strings
+- setops
+- numtheory
+- mixedtoken
+- digits
+- alphabet
+- prime
+- wordlen
+- roman
+probe_families:
+- setops
+- alphabet
+- digits
+ks:
+- 25
+- 50
+- 100
+- 150
+epochs_grid:
+- 2
+- 3
+cross:
+- setops
+- alphabet
+k_inherit: 300
+epochs: 6
+n_candidates: 6
+n_test: 100
+n_probe: 10
+spec_train: 1200
+spec_epochs: 3
+lora:
+  r: 16
+  alpha: 32
+output:
+  dir: results/llm_society_v2_calib_c3c_t85e6
+conf_gate: 0.85

configs/llm/society_v2_s1.yaml

@@ -0,0 +1,29 @@
+# v2 society campaign, seed 1 (prereg tasks/prereg-llm-society-v2.md §3). The PBS array
+# (hpc/llm_society_v2.pbs) derives one config per (seed, arm) from this template; running it directly
+# runs all four arms sequentially (the local-GPU hedge).
+#
+# CALIBRATION-DEPENDENT FIELDS — set after gates C1/C2 pass, before submission:
+#   families   : the 9 selected by stage A pass 2 (C1, gate 0.41) — SET
+#   k_inherit / epochs / conf_gate : from stage B (C2 + C2b), 2026-09-07 — SET
+experiment: llm_society_v2
+kind: llm_society_v2
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 1
+families: [strings, setops, numtheory, mixedtoken, digits, alphabet, prime, wordlen, roman]
+agents: 9
+generations: 12
+arms: [full, no_grounding, no_sex, no_diversity]
+g: 0.85
+lam: 0.3
+max_mate_use: 2
+n_test: 27           # per family  -> 243 overall (SE 0.032)
+n_val: 13            # per family  -> 117 (selection signal)
+n_conf: 13           # per family, fresh each generation
+k_inherit: 300       # per family, fresh each generation  (C2b: k=300 is the main retention driver)
+epochs: 3            # child SFT epochs                     (C2b)
+conf_gate: 0.85      # confidence-gated inheritance, all arms (C2b mean retention 0.88; C3c: tight gate = 6-epoch retention at 3 epochs)
+spec_train: 1200
+spec_epochs: 3
+resume: true
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_society_v2/s1}

configs/llm/society_v2_s2.yaml

@@ -0,0 +1,29 @@
+# v2 society campaign, seed 2 (prereg tasks/prereg-llm-society-v2.md §3). The PBS array
+# (hpc/llm_society_v2.pbs) derives one config per (seed, arm) from this template; running it directly
+# runs all four arms sequentially (the local-GPU hedge).
+#
+# CALIBRATION-DEPENDENT FIELDS — set after gates C1/C2 pass, before submission:
+#   families   : the 9 selected by stage A pass 2 (C1, gate 0.41) — SET
+#   k_inherit / epochs / conf_gate : from stage B (C2 + C2b), 2026-09-07 — SET
+experiment: llm_society_v2
+kind: llm_society_v2
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 2
+families: [strings, setops, numtheory, mixedtoken, digits, alphabet, prime, wordlen, roman]
+agents: 9
+generations: 12
+arms: [full, no_grounding, no_sex, no_diversity]
+g: 0.85
+lam: 0.3
+max_mate_use: 2
+n_test: 27           # per family  -> 243 overall (SE 0.032)
+n_val: 13            # per family  -> 117 (selection signal)
+n_conf: 13           # per family, fresh each generation
+k_inherit: 300       # per family, fresh each generation  (C2b: k=300 is the main retention driver)
+epochs: 3            # child SFT epochs                     (C2b)
+conf_gate: 0.85      # confidence-gated inheritance, all arms (C2b mean retention 0.88; C3c: tight gate = 6-epoch retention at 3 epochs)
+spec_train: 1200
+spec_epochs: 3
+resume: true
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_society_v2/s2}

configs/llm/society_v2_s3.yaml

@@ -0,0 +1,29 @@
+# v2 society campaign, seed 3 (prereg tasks/prereg-llm-society-v2.md §3). The PBS array
+# (hpc/llm_society_v2.pbs) derives one config per (seed, arm) from this template; running it directly
+# runs all four arms sequentially (the local-GPU hedge).
+#
+# CALIBRATION-DEPENDENT FIELDS — set after gates C1/C2 pass, before submission:
+#   families   : the 9 selected by stage A pass 2 (C1, gate 0.41) — SET
+#   k_inherit / epochs / conf_gate : from stage B (C2 + C2b), 2026-09-07 — SET
+experiment: llm_society_v2
+kind: llm_society_v2
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 3
+families: [strings, setops, numtheory, mixedtoken, digits, alphabet, prime, wordlen, roman]
+agents: 9
+generations: 12
+arms: [full, no_grounding, no_sex, no_diversity]
+g: 0.85
+lam: 0.3
+max_mate_use: 2
+n_test: 27           # per family  -> 243 overall (SE 0.032)
+n_val: 13            # per family  -> 117 (selection signal)
+n_conf: 13           # per family, fresh each generation
+k_inherit: 300       # per family, fresh each generation  (C2b: k=300 is the main retention driver)
+epochs: 3            # child SFT epochs                     (C2b)
+conf_gate: 0.85      # confidence-gated inheritance, all arms (C2b mean retention 0.88; C3c: tight gate = 6-epoch retention at 3 epochs)
+spec_train: 1200
+spec_epochs: 3
+resume: true
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_society_v2/s3}

configs/llm/society_v2_s4.yaml

@@ -0,0 +1,29 @@
+# v2 society campaign, seed 4 (prereg tasks/prereg-llm-society-v2.md §3). The PBS array
+# (hpc/llm_society_v2.pbs) derives one config per (seed, arm) from this template; running it directly
+# runs all four arms sequentially (the local-GPU hedge).
+#
+# CALIBRATION-DEPENDENT FIELDS — set after gates C1/C2 pass, before submission:
+#   families   : the 9 selected by stage A pass 2 (C1, gate 0.41) — SET
+#   k_inherit / epochs / conf_gate : from stage B (C2 + C2b), 2026-09-07 — SET
+experiment: llm_society_v2
+kind: llm_society_v2
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 4
+families: [strings, setops, numtheory, mixedtoken, digits, alphabet, prime, wordlen, roman]
+agents: 9
+generations: 12
+arms: [full, no_grounding, no_sex, no_diversity]
+g: 0.85
+lam: 0.3
+max_mate_use: 2
+n_test: 27           # per family  -> 243 overall (SE 0.032)
+n_val: 13            # per family  -> 117 (selection signal)
+n_conf: 13           # per family, fresh each generation
+k_inherit: 300       # per family, fresh each generation  (C2b: k=300 is the main retention driver)
+epochs: 3            # child SFT epochs                     (C2b)
+conf_gate: 0.85      # confidence-gated inheritance, all arms (C2b mean retention 0.88; C3c: tight gate = 6-epoch retention at 3 epochs)
+spec_train: 1200
+spec_epochs: 3
+resume: true
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_society_v2/s4}

configs/llm/society_v2_smoke.yaml

@@ -0,0 +1,21 @@
+# Smoke test for the v2 society loop: tiny everything, all four arms, resume exercised by the
+# per-generation checkpoint. ~20 min on a 16 GB GPU. Exit 0 + figure renders = pass.
+experiment: llm_society_v2_smoke
+kind: llm_society_v2
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+seed: 1
+families: [lists, roman, binary, setops]
+agents: 4
+generations: 2
+arms: [full, no_grounding, no_sex, no_diversity]
+g: 0.85
+lam: 0.3
+n_test: 8            # per family
+n_val: 5             # per family
+n_conf: 5            # per family, fresh each generation
+k_inherit: 20        # per family
+epochs: 2
+spec_train: 120
+spec_epochs: 2
+lora: {r: 16, alpha: 32}
+output: {dir: results/llm_society_v2_smoke}