Giorgio Gilestro
6f8cef1ac5
Removed from main (all preserved on the dev branch): the arXiv build and
its sources, design documents (blueprint, results summary, review responses,
essay drafts), tasks/ and CLAUDE.md, the cover letter and reference tooling,
two unused manuscript figures, and every experiment that feeds no figure or
number in the paper: the collapse null, the sexual-vs-asexual lineage, the
NK speciation variant, the 0.5B single-seed LLM prototypes, the compose and
society experiments with their calibration and pilot runs, and their
configs, runners, tests, figure scripts and PBS jobs. Their result bundles
are moved to results/_archive/ (ignored) so the parquets stay on disk.
Also: plot_llm_speciation reads the s{seed}/ layout; the mating-breadth
plot writes under its bundle name; Makefile targets reduced to the kept
experiments; REPRODUCING.md and README point to dev for the rest.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
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| bridge.pdf | ||
| bridge.png | ||
| manifest.json | ||
| README.md | ||
| resolved_config.yaml | ||
bridge — the histogram model reproduces Layer-1 E2 exactly (the HARD GATE)
Claim tested (a plumbing check, not science): does the neural experiment harness, when run with a trivial model, reproduce the Layer-1 math exactly? If not, no later neural result could be trusted.
Setup (Layer 1.5). Same generational loop as every neural experiment — each generation, draw the
parent's samples, optionally mix in real data, retrain, measure — but the "model" is a histogram:
it just counts which modes appeared and resamples them (no neural net, no smoothing). This reduces the
neural setup exactly back to Wright–Fisher drift. K = 200 modes, n = 200, 200 generations,
60 repeats, grounding swept g ∈ {0, …, 0.4}.
Symbols
- mode = one of the
Kitems (Layer-1.5 word for "item"); read off each generated sequence by a zero-error oracle. ggrounding fraction,g*its critical value,Hdiversity,H_eqthe exact closed-form equilibrium diversity.
The four panels
- Trajectories. Diversity
Hperg.g = 0collapses;g > 0plateaus — the E2 picture, now produced by the neural runner. - Bridge = Layer 1 (the gate). Dots = the neural histogram runner's stationary
Hvsg; the black dashed curve = the exactH_eqclosed form from Layer 1. The dots sit on the curve, and the recovered critical grounding isg* = 0.047(CI [0.045, 0.052]) — matching Layer-1's 0.048. This equality is what licenses every trained-model result to be read against the analytic core. - Tail survival. Fraction of the rare tail retained vs
g(item-count red, truth-mass purple) — rises with grounding, deep tail lags, exactly as in E2. - Per-rarity band. Survival by rarity band vs
g; deep bands need more grounding (m·p*ᵢ ≳ 1).
Takeaway
The harness is faithful: with a memoryless model it reproduces Layer 1 to the decimal. This is a
gate, not a finding — passing it is the precondition for the RNN/MLP experiments
(collapse, grounding, architectures, recombination), where the model is what changes.