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Manuscript revision and pending experiment work, snapshot before restructuring

Browse source 
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
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Giorgio Gilestro 2026-09-13 16:54:09 +01:00
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66
REPRODUCING.md
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@ -13,11 +13,11 @@ Manuscript: `paper/pnas/main.md` (built to `paper/pnas/main.pdf`).
| Tier | What it is | Hardware | Determinism |
|---|---|---|---|
| Biological model | Wright–Fisher simulator over knowledge distributions (pure NumPy/SciPy) | Any laptop, no GPU | **Bitwise** from the master seed |
| Inheritance model | Wright–Fisher simulator over knowledge distributions (pure NumPy/SciPy) | Any laptop, no GPU | **Bitwise** from the master seed |
| Trained networks | RNN / MLP / VAE on a synthetic mode universe; convolutional VAE on MNIST | One consumer GPU (16 GB) | Statistical (GPU non-determinism documented in §5) |
| Language models | LoRA specialists on Qwen2.5-Instruct 0.5B / 7B | 0.5B: one 16 GB GPU · 7B: one L40S (46 GB) | Statistical; per-seed points reported |
The biological-model tier carries every quantitative claim in the paper and reproduces exactly on a
The inheritance-model tier carries every quantitative claim in the paper and reproduces exactly on a
laptop in minutes. The two AI tiers are confirmatory (signs, not magnitudes) and need a GPU.
## 2. Environment
@ -50,15 +50,15 @@ Tier by tier, by hand:
```bash
make test # correctness + closed-form scientific validation
make layer1 # the biological model: E1-E12, E14, learning kernel
make layer1 # the inheritance model: E1-E12, E14, learning kernel
make neural # trained networks (needs a GPU)
make mnist # real-MNIST tier + the Fig. 2B montage asset (needs torchvision)
make mnist # real-MNIST tier + the Fig. 2A montage asset (needs torchvision)
make llm # language-model prototypes (needs a GPU)
make llm-seeds # the multi-seed firm-ups behind Fig. 3A
make llm-epistasis # the controlled predictive test behind Fig. 3C-D
make llm-speciation # the LLM speciation tier behind Fig. 7E-F
make llm-seeds # the multi-seed firm-ups behind Fig. 3B
make llm-epistasis # the controlled predictive test behind Fig. 3D-E
make llm-speciation # the LLM speciation tier behind Fig. 5C-D
make figures # per-experiment figures, from committed parquets (no re-simulation)
make paper-figures # the manuscript figures Fig. 1-7 + rebuild the PDF body
make paper-figures # the manuscript figures Fig. 1-5 + rebuild the PDF body
```
## 4. The figure map
@ -66,31 +66,41 @@ make paper-figures # the manuscript figures Fig. 1-7 + rebuild the PDF body
Every manuscript panel, the artifact it is plotted from, the config that produced that artifact, and
that config's declared seed. All panels are drawn by `paper/pnas/make_figs.py` (function per figure);
`make paper-figures` regenerates all of them. Figures are a **pure function of committed artifacts** —
no panel re-simulates anything, with the single documented exception of the Fig. 2B montage asset.
no panel re-simulates anything, with the single documented exception of the Fig. 2A montage asset.
| Panel | Drawn by | From artifact | Produced by config | Seed(s) |
|---|---|---|---|---|
| Fig. 1A, 1B | `fig1a()`, `fig1b()` | — (schematics; icons in `paper/pnas/figs/icons/`) | — | — |
| Fig. 2A | `fig2()` | `results/E2/` | `configs/layer1/E2.yaml` | 20260704 |
| Fig. 2B | `fig2()` | `results/mnist_collapse/mnist_montage.png` | `configs/neural/mnist_collapse.yaml` → asset from `figures/mnist_montage.py` | 20260705 |
| Fig. 3A | `fig3()` | `results/llm_merge_seeds/` | `configs/llm/merge_seeds.yaml` | 1, 2, 3, 4, 5 |
| Fig. 3B | `fig3()` | `results/llm_moe_hard_hpc/` | `configs/llm/moe_hard_hpc.yaml` (7B, HPC) | 1 (single run) |
| Fig. 3C, 3D | `fig3()` | `results/llm_epistasis/` + `results/llm_epistasis_compat/` | `configs/llm/epistasis.yaml`, `configs/llm/epistasis_compat.yaml` | 1, 2, 3 |
| Fig. 4A | `fig4()` | `results/E4/` | `configs/layer1/E4.yaml` | 20260704 |
| Fig. 4B | `fig4()` | `results/E8/` | `configs/layer1/E8.yaml` | 20260705 |
| Fig. 5A | `fig5()` | `results/E9/` | `configs/layer1/E9.yaml` | 20260705 |
| Fig. 5B | `fig5()` | `results/E10/` | `configs/layer1/E10.yaml` | 20260705 |
| Fig. 5C, 5D | `fig5()` | `results/E14/` | `configs/layer1/E14.yaml` | 20260709 |
| Fig. 6A, 6B, 6C | `fig6()` | `results/E11/` | `configs/layer1/E11.yaml` | 20260705 |
| Fig. 7A, 7B | `fig7()` | `results/E12/` | `configs/layer1/E12.yaml` | 12 |
| Fig. 7C | `fig7()` | `results/speciation_real/` | `configs/neural/speciation_real.yaml` | 13 |
| Fig. 7D | `fig7()` | `results/speciation_real_cliff/` | `configs/neural/speciation_real_cliff.yaml` | 13 |
| Fig. 7E, 7F | `fig7()` | `results/llm_speciation/` | `configs/llm/speciation.yaml` | 1 (single seed) |
| Fig. 2A | `fig2()` | `results/mnist_collapse/mnist_montage.png` | `configs/neural/mnist_collapse.yaml` → asset from `figures/mnist_montage.py` | 20260705 |
| Fig. 2B *(reference)* | `fig2()` | `results/E2/` | `configs/layer1/E2.yaml` | 20260704 |
| Fig. 3A | `fig3()` | — (schematic of the compared models; icons in `paper/pnas/figs/icons/`) | — | — |
| Fig. 3B | `fig3()` | `results/llm_merge_seeds/` | `configs/llm/merge_seeds.yaml` | 1, 2, 3, 4, 5 |
| Fig. 3C | `fig3()` | `results/llm_moe_hard_hpc/s{1,2,3}/` | `configs/llm/moe_hard_hpc.yaml` (7B, HPC; seeds 2–3 via `hpc/llm_7b_seeds.pbs`); statistics `figures/stats_llm_7b_seeds.py` | 1, 2, 3 |
| Fig. 3D, 3D | `fig3()` | `results/llm_epistasis/` + `results/llm_epistasis_compat/` | `configs/llm/epistasis.yaml`, `configs/llm/epistasis_compat.yaml` | 1, 2, 3 |
| Fig. 4A | `fig4()` | — (schematic; the syllabus grid is read from `configs/llm/curriculum_v5_s1.yaml` family order, complementarity from the veto bundle) | — | — |
| Fig. 4B | `fig4()` | `results/llm_curriculum_v5/`, `results/llm_curriculum_v5_veto/`, `results/llm_curriculum_v5_stop3/` | `configs/llm/curriculum_v5_s1.yaml` (seeds 2–3 via `hpc/llm_curriculum.pbs`), `configs/llm/curriculum_v5_veto.yaml` (seeds 2–3 via `hpc/llm_veto.pbs`), `configs/llm/curriculum_v5_stop3.yaml` (seeds 2–3 via `hpc/llm_curriculum_controls.pbs`) | 1–3 (training seeds; fixed test splits) |
| Fig. 4C | `fig4()` | `results/llm_curriculum_v5_veto/`, `results/llm_curriculum_v5_decor/` | `configs/llm/curriculum_v5_veto.yaml`, `configs/llm/curriculum_v5_decor.yaml` (seeds 2–3 via `hpc/llm_curriculum_controls.pbs`); statistics `figures/stats_llm_curriculum.py` | 1–3 |
| Fig. S14 | `figures/plot_curriculum_timing.py` | `results/llm_curriculum_v5_{early,late,early_obl,late_obl}/s{1,2,3}/` | `configs/llm/curriculum_v5_{early,late}[_obl].yaml` via `hpc/llm_curriculum_timing.pbs`; statistics `figures/stats_llm_curriculum.py` | 1, 2, 3 |
| Fig. S15 | `figures/plot_curriculum_cull.py` | `results/llm_curriculum_v5_cull/s{1,2,3}/` | `configs/llm/curriculum_v5_cull.yaml` via `hpc/llm_cull.pbs`; statistics `figures/stats_llm_curriculum.py` | 1, 2, 3 |
| Fig. S16 | `figures/plot_llm_smol.py` | `results/llm_{merge_seeds,moe_hard_seeds}_smol/` (+ the Qwen bundles) | `configs/llm/{merge_seeds,moe_hard_seeds}_smol.yaml` via `hpc/llm_smol.pbs`; statistics `figures/stats_llm_smol.py` | 1–5; 1–3 |
| Fig. 4D, 4D, 4E *(reference)* | `fig4()` | `results/E11/` | `configs/layer1/E11.yaml` | 20260705 |
| Fig. 5A | `fig5()` | `results/speciation_real/` | `configs/neural/speciation_real.yaml` | 13 |
| Fig. 5B | `fig5()` | `results/speciation_real_cliff/` | `configs/neural/speciation_real_cliff.yaml` | 13 |
| Fig. 5C, 5D | `fig5()` | `results/llm_speciation/s{1,2,3}/` | `configs/llm/speciation.yaml` (seeds 2–3 via `hpc/llm_speciation_seeds.pbs`); statistics `figures/stats_llm_speciation_seeds.py` | 1, 2, 3 |
| Fig. 5E, 5F *(reference)* | `fig5()` | `results/E12/` | `configs/layer1/E12.yaml` | 12 |
**Single-run panels.** Fig. 3B and Fig. 7E–F come from single-seed runs and are reported as
sign-level confirmations, not estimates; the manuscript labels them as such. Every other panel is
replicated (biological-model panels over 12–100 internal replicates; Fig. 3A over five training
seeds; Fig. 3C–D over three).
Panels marked *(reference)* are inheritance-model results included to set the expectation the
real-model panels are read against, not as findings in their own right.
**Inheritance-model results reported in SI only.** These have no real-model counterpart in the paper
and reproduce known results, so they are cited as reference values and plotted only in SI, from their
per-experiment figures: blending vs union (E4), Fisher–Muller super-parent (E8), outbreeding
depression (E9), directed recombination (E10), and mate-pool breadth (E14), drawn by
`figures/plot_E{4,8,9,10,14}.py` from `results/E{4,8,9,10,14}/`.
**Replication.** Every panel is replicated: inheritance-model panels over 12–100 internal
replicates; Fig. 3B over five training seeds; Fig. 3C, 3D–E, 4A–B and 5C–D over three (Fig. 5C–D
was single-seed until 2026-09-12; seeds 2–3 ran via `hpc/llm_speciation_seeds.pbs`).
### Results reported in the text but not plotted in the manuscript