MachineSex/REPRODUCING.md

Reproducing every number and figure in the paper

This document is the authoritative map from the manuscript back to the code, configs, and seeds that produced it. Every figure panel, every headline number, and the environment they were computed in are listed below. If something in the paper is not traceable through this document, that is a bug — please open an issue.

Manuscript: paper/manuscript/main.md (built to paper/manuscript/main.pdf).

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1. The three tiers, and what each costs to reproduce

Tier	What it is	Hardware	Determinism
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 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

The environment is a uv venv built from the committed, hash-pinned uv.lock. That lockfile — not a container, not a requirements file — is the single source of truth for "it runs".

curl -LsSf https://astral.sh/uv/install.sh | sh   # one-time, if you don't have uv

make env             # biological-model tier (pure NumPy/SciPy)
make env-neural      # + torch, for the trained-network tier
make env-mnist       # + torchvision, for the real-MNIST tier
make env-llm         # + transformers/peft, for the language-model tier
make env-notebooks   # + Jupyter, for the walkthrough notebooks

3. One command

./reproduce.sh

runs the environment build, the test suite (including the closed-form scientific-validation tests), the entire biological-model tier at its committed seeds, every figure, and writes REPRODUCED.md with the resulting artifact hashes for comparison against the committed manifests. It deliberately stops at the GPU tiers; pass --with-gpu to include them if you have the hardware.

Tier by tier, by hand:

make test            # correctness + closed-form scientific validation
make inheritance     # the inheritance model, every experiment at its committed seed
make neural          # trained networks (needs a GPU)
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. 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. 1-5 + rebuild the PDF body

4. The figure map

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/manuscript/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. 2A montage asset.

Panel	Drawn by	From artifact	Produced by config	Seed(s)
Fig. 1A, 1B	fig1a(), fig1b()	— (schematics; icons in paper/manuscript/figs/icons/)	—	—
Fig. 2A	fig2()	results/fig2_mnist_collapse/mnist_montage.png	configs/neural/fig2_mnist_collapse.yaml → asset from figures/mnist_montage.py	20260705
Fig. 2B (reference)	fig2()	results/fig2_grounding_sweep/	configs/inheritance/fig2_grounding_sweep.yaml	20260704
Fig. 3A	fig3()	— (schematic of the compared models; icons in paper/manuscript/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/fig4_society_ablation/	configs/inheritance/fig4_society_ablation.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/fig5_speciation_bdm/	configs/inheritance/fig5_speciation_bdm.yaml	12

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 (figS8_multiparent_union), Fisher–Muller super-parent (figS9_specialist_superparent), outbreeding depression (figS10_rugged_landscapes), directed recombination (figS11_directed_recombination), and mate-pool breadth (figS13_mating_breadth), each drawn by figures/plot_<bundle>.py from results/<bundle>/.

Bundle names. Inheritance-model and trained-network bundles are named after the manuscript figure they feed at submission (fig2_*, figS4_*); bundles that feed no figure carry a descriptive name. The name is a label fixed at submission, so a later renumbering of the figures does not rename the bundles.

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

Result	Artifact	Config	Seed
Region-matched grounding (Fig. S5)	results/figS5_aimed_grounding/	configs/inheritance/figS5_aimed_grounding.yaml	20260704
Quality-diversity vs greedy (Fig. S12)	results/figS12_quality_diversity/	configs/inheritance/figS12_quality_diversity.yaml	20260704
Re-baselining / irreversibility (Fig. S3)	results/figS3_rebaselining/	configs/inheritance/figS3_rebaselining.yaml	20260704
Learning kernel (Fig. S2)	results/figS2_kernel_sharpen/, results/figS2_kernel_smooth/	configs/inheritance/figS2_kernel_{sharpen,smooth}.yaml	20260705
Histogram bridge gate	results/bridge/	configs/neural/bridge.yaml	20260704
Neural collapse / grounding / architectures / recombination	results/{collapse,figS6_grounding_rnn,figS1_architectures,recombination}/	configs/neural/*.yaml	20260704
Emergent-isolation null	results/speciation_real_emergent/	configs/neural/speciation_real_emergent.yaml	813
Budget-controlled speciation (add design)	results/llm_speciation_add/	configs/llm/speciation_add.yaml	1, 2, 3
0.5B multi-seed runs	results/llm_{merge_seeds,moe_hard_seeds,directed_hard_seeds}/	configs/llm/{merge_seeds,moe_hard_seeds,directed_hard_seeds}.yaml	1–5; 1–3; 1–3
7B firm-ups	results/llm_*_hpc/	configs/llm/*_hpc.yaml (run via hpc/*.pbs)	1

Per-experiment (exploratory) figures

figures/plot_*.py regenerate a diagnostic figure inside each results bundle (results/<name>/<name>.pdf), named after the experiment, not after a manuscript figure. They are the working views, not the manuscript's; the table above is the authority on what appears in the paper. figures/stats_llm_epistasis.py prints the robust statistics quoted in the predictive-test section (clustered bootstrap, paired contrasts, leave-one-condition-out, outcome-reference sensitivity).

5. Seeds and determinism

Policy. One master seed per config. All sub-randomness is derived from it via numpy.random.SeedSequence.spawn (src/inheritance/seeding.py); no code touches global RNG state, and every rng is passed explicitly. A run is a pure function of its resolved config.

Biological-model tier: bitwise reproducible. Re-running a config on the same lockfile reproduces its results.parquet byte-for-byte; the results_sha256 in each manifest.json is the check.

GPU tiers: statistically reproducible. cuDNN kernel selection and reduction order make bitwise equality unattainable across machines. Per-seed points are reported rather than seed-averaged summaries alone, and the multi-seed protocols fix the evaluation sets and vary only the training seed. Expect sign agreement and magnitudes within noise, not identical digits.

Seed provenance. 20260704/20260705/20260709 are date-stamped master seeds chosen at the time each experiment was written and never re-drawn. Small integer seeds (1, 12, 13, 813) are likewise fixed at authoring time. No seed in this repository was selected after seeing results.

6. Verifying artifact integrity

Every run writes three files next to its results:

• results.parquet — the long-form data (the only thing figures read)
• resolved_config.yaml — the config after sweep expansion, i.e. exactly what ran
• manifest.json — master seed, git commit, Python and library versions, row count, and results_sha256 (the content hash of the parquet)

To verify a bundle you have regenerated matches the one behind the paper:

python - <<'EOF'
import hashlib, json, pathlib
for m in sorted(pathlib.Path("results").glob("*/manifest.json")):
    man = json.loads(m.read_text())
    pq = m.parent / "results.parquet"
    if not pq.exists():
        print(f"{m.parent.name:28s} (no parquet — run its config first)"); continue
    got = hashlib.sha256(pq.read_bytes()).hexdigest()
    ok = "OK  " if got == man.get("results_sha256") else "DIFF"
    print(f"{ok} {m.parent.name:28s} seed={man.get('master_seed')}")
EOF

DIFF on a biological-model bundle means a genuine discrepancy worth investigating. DIFF on a GPU tier is expected (see §5) — compare the figures and the reported statistics instead.

7. HPC (the 7B tier)

The 7B runs were executed on Imperial College's CX3 cluster (PBS Pro, one L40S 46 GB per job). Job scripts are in hpc/; each is self-contained and documents its own submission line. They stage the same uv.lock environment, so the only difference from a local run is the GPU.

qsub hpc/llm_merge.pbs      # 7B merge firm-up
qsub hpc/llm_hard.pbs       # hard-benchmark moe + directed at 7B
qsub hpc/llm_7b_seeds.pbs   # seeds 2-3 of the three 7B experiments (array)

8. Notebooks

notebooks/ contains executable walkthroughs (make env-notebooks, then jupyter lab):

Notebook	What it does	Needs
01_biological_model.ipynb	Builds the Wright–Fisher model from scratch, checks it against the three closed forms, and derives the grounding threshold interactively	Laptop
02_paper_figures.ipynb	Regenerates every manuscript figure from the committed artifacts and displays them inline, panel by panel	Laptop (artifacts must be present)

make notebooks executes both end-to-end, which is itself a reproduction check.

9. Known gaps

• results/**/results.parquet is currently gitignored (only manifests, hashes, and resolved configs are tracked). A fresh clone therefore has to re-run the experiments before figures can be regenerated. The archived deposit (Zenodo DOI, on publication) includes the parquets so that the paper's "regenerates from committed artifacts without re-simulation" holds from the archive.
• figures/mnist_montage.py re-runs a short dry lineage to draw its montage rather than reading a parquet; it is an eyeball diagnostic whose quantitative counterpart is results/fig2_mnist_collapse/.
• The composed society at language-model scale is an open experiment at the time of writing; see tasks/workorder-llm-society.md.