- 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
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
Replaces the three-paragraph methods sketch with a scientific account of how
the study was run (M1-M7):
- M1 design principles: cheapest falsifying tier; match claim precision to
instrument precision; every tier gets an oracle independent of the model
being measured; falsifiers declared before running.
- M2 replication: what a replicate *is* differs by tier (independent lineage /
lineage incl. fresh init and data order / training seed with test sets held
fixed), and a table giving every experiment's replicate count with the
reasoning - why 200 for E4 (per-item binary outcomes), 60 for the bridge
gate (must detect any departure), 3-5 where the contrast is categorical,
and 1 for the 7B runs, labelled as single runs.
- M3-M5 per-tier procedures: parameter choices and their justification, the
correlated-parent construction, why the neural sandbox is synthetic (a
lossless identity code plus style entropy gives an exact oracle while still
forcing the model to learn a distribution), MNIST modes and the frozen-CNN
oracle with its confusion matrix as measurement floor, why no-BatchNorm MLPs
for the alignment analysis, and for the LLM tier: why Qwen 0.5B/7B (one
family so scale is the only variable), why procedural tasks rather than a
benchmark (exact verifier, contamination-free, controlled disjointness, a
difficulty knob), why LoRA (confines each parent to an additive low-rank
delta over an identical base, which is what makes weight-space
recombination well defined), the training algorithm, and the split scheme.
- M6 negative controls, including the one that removed a result: the
compatible-overlap axis collapsed the delta-cosine predictor from rho=+0.60
to +0.03.
- M7 statistical procedures.
Also: SI voice converted to first person and terminology synced to the
"biological model" rename; removed a process ghost from the preamble
("Skeleton assembled at Phase 4"); build.py now takes a document argument and
no longer eats documents that lack a title block, so the SI compiles via a new
si.tex wrapper (10 pp). `make paper` builds both PDFs.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
