Giorgio Gilestro
ab3dc10587
- paper/pnas -> paper/manuscript (venue-neutral)
- configs/layer1 -> configs/inheritance, src/knowledge -> src/inheritance
(imported as `inheritance`), make layer1 -> make inheritance; layer2 alias dropped
- inheritance and trained-network bundles named after the manuscript figure
they feed (fig2_grounding_sweep, figS3_rebaselining, ...), or descriptively
where they feed none; configs keep their `experiment:` value so parquet
hashes are unchanged, only output.dir moves
- figure scripts, SI figure sources, notebooks, REPRODUCING.md, README and the
SI Methods/tables updated; make clean no longer deletes tracked manifests;
reproduce.sh hashes the s{seed}/ layouts too
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
32 lines
2.2 KiB
Markdown
32 lines
2.2 KiB
Markdown
# architectures — collapse and rescue are architecture-general
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**Claim tested:** is model collapse (and its cure, grounding) a quirk of one model type, or does the
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same signature appear across genuinely different neural architectures?
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**Setup (Layer 1.5).** The identical generational loop is run with three different generative models —
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an **exact histogram** (no neural net), an **autoregressive GRU** (recurrent), and a **causal-masked
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MLP** (feed-forward) — each a distinct "inductive bias." `K = 256` modes, `n = 200`, 22 generations,
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5 repeats, compared at **dry (`g = 0`)** vs **grounded (`g = 0.05`)**.
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### Symbols
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- **inductive bias** — the built-in assumptions of a model type (a histogram has none; a GRU and an MLP smooth differently).
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- **forward-KL** distance from truth; **tail items alive** — how many rare modes still appear.
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- **dry** = no grounding; **grounded** = 5% real data mixed in.
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### The three panels
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1. **Trajectories.** Forward-KL over generations, coloured by architecture; **solid = dry** (climbs,
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collapse) vs **dashed = grounded** (held down). The dry-up / grounded-down gap appears in **every**
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architecture.
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2. **Stationary forward-KL (grouped bars).** For each architecture, dry (red) vs grounded (green).
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Divergence **falls with grounding across all three** — histogram, GRU, MLP.
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3. **Tail-item survival (grouped bars).** Same grouping. Survival **rises with grounding across all
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three.** (Note the histogram's bars are tiny: with no smoothing it drops rare modes outright,
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whereas the GRU/MLP keep some alive — an inductive-bias difference, not a contradiction.)
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### Takeaway
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The Wright–Fisher collapse operator and the grounding rescue are **not artefacts of one model** — they
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show up in an exact counter, a recurrent net, and a feed-forward net alike. This is the
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architecture-generality claim of Layer 1.5. **Falsifier (not triggered):** if the signs had appeared
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only for the histogram, collapse would be a property of the idealised operator, not of trained models.
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*(A VAE was also implemented but fails the generation-0 fidelity check on this task, so it is excluded
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to avoid confusing underfitting with collapse — documented as a known limitation.)*
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