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
|
||
|---|---|---|
| .. | ||
| figS1_architectures.pdf | ||
| figS1_architectures.png | ||
| manifest.json | ||
| README.md | ||
| resolved_config.yaml | ||
architectures — collapse and rescue are architecture-general
Claim tested: is model collapse (and its cure, grounding) a quirk of one model type, or does the same signature appear across genuinely different neural architectures?
Setup (Layer 1.5). The identical generational loop is run with three different generative models —
an exact histogram (no neural net), an autoregressive GRU (recurrent), and a causal-masked
MLP (feed-forward) — each a distinct "inductive bias." K = 256 modes, n = 200, 22 generations,
5 repeats, compared at dry (g = 0) vs grounded (g = 0.05).
Symbols
- inductive bias — the built-in assumptions of a model type (a histogram has none; a GRU and an MLP smooth differently).
- forward-KL distance from truth; tail items alive — how many rare modes still appear.
- dry = no grounding; grounded = 5% real data mixed in.
The three panels
- Trajectories. Forward-KL over generations, coloured by architecture; solid = dry (climbs, collapse) vs dashed = grounded (held down). The dry-up / grounded-down gap appears in every architecture.
- Stationary forward-KL (grouped bars). For each architecture, dry (red) vs grounded (green). Divergence falls with grounding across all three — histogram, GRU, MLP.
- Tail-item survival (grouped bars). Same grouping. Survival rises with grounding across all three. (Note the histogram's bars are tiny: with no smoothing it drops rare modes outright, whereas the GRU/MLP keep some alive — an inductive-bias difference, not a contradiction.)
Takeaway
The Wright–Fisher collapse operator and the grounding rescue are not artefacts of one model — they show up in an exact counter, a recurrent net, and a feed-forward net alike. This is the architecture-generality claim of Layer 1.5. Falsifier (not triggered): if the signs had appeared only for the histogram, collapse would be a property of the idealised operator, not of trained models. (A VAE was also implemented but fails the generation-0 fidelity check on this task, so it is excluded to avoid confusing underfitting with collapse — documented as a known limitation.)