MachineSex

giorgio/MachineSex

Fork 0

Commit graph

Author	SHA1	Message	Date
Giorgio Gilestro	ab3dc10587	Restructure: descriptive tier and experiment names, paper/manuscript - 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	2026-09-13 17:00:40 +01:00
Giorgio Gilestro	d22dd9d535	recombination: reproduce the E4 "merge, don't average" finding in real weights src/neural/recombine.py mirrors Layer-1 run_coverage but trains K_T specialist RNNs on assignments from the exact shared-switch retention construction (K_T/rho/q clean; union matches the closed form), then recombines the measured teacher distributions two ways: mean (naive pooling) vs oracle-guided max-merge (per-mode strongest teacher, M2N2-style), each followed by size-n resampling. Result (8 reps): at rho=0, union rises 0.49->0.96 (supply matches closed form); analytic surviving_max rises 0.043->0.087 while surviving_mean stays flat ~0.045 — the conservation law (averaging cancels the union gain, max-merge realises it). At rho=1 (identical teachers) union and max are flat. The lesson holds in the neural setting; trained-weight columns show the same signs but noisier (smoothing inflates baseline; deep tail barely clears n=200 resampling). torch-gated test added. 93 tests green. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-07-04 21:49:44 +01:00
Giorgio Gilestro	840b6b00b3	Layer 1.5: architecture-general neural existence proof Re-scopes Layer 2 into a cheaper, architecture-general neural collapse proof before the LLM rung. Realises the same Wright–Fisher abstractions in real trained generative models on a fully-synthetic sandbox with an exact oracle, reusing knowledge.metrics/truth/seeding and the output contract so neural curves overlay the Layer-1 analytic curves. - src/neural/: synthetic token-grammar sandbox (lossless identity + stochastic style), ExactOracle, HistogramModel bridge, generation loop, experiment runner - HARD GATE passed: histogram lineage reproduces Layer 1 exactly (neutral decay, exact H_eq, tracks run_lineage) — tests/test_neural_validation.py - torch models: autoregressive RNN + MLP (VAE implemented, not yet fidelity- passing); determinism seeding derived from the SeedSequence stream - N0 bridge (neural g*=0.047 ≈ Layer-1 0.048), N1 collapse-in-weights, N2 phase boundary, N5 architecture-generality (collapse + grounding-rescue in histogram + RNN + MLP). Manifests/configs committed; parquet gitignored, hashes tracked - additive backward-compatible save_artifacts extension; Makefile neural targets Finding: neural smoothing partially resists H-collapse, so forward-KL and tail survival are the sharp neural collapse metrics (H is smooth, per Layer 1). 92 tests green. Remaining (tasks/todo.md): N4 merge, N2 refine, N3/N6, VAE fidelity, MNIST tier, figures. LLM/LoRA rung and C3 deferred. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-07-04 21:02:49 +01:00

Author

SHA1

Message

Date

Giorgio Gilestro

ab3dc10587

Restructure: descriptive tier and experiment names, paper/manuscript

- 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

2026-09-13 17:00:40 +01:00

Giorgio Gilestro

d22dd9d535

recombination: reproduce the E4 "merge, don't average" finding in real weights

src/neural/recombine.py mirrors Layer-1 run_coverage but trains K_T specialist RNNs on
assignments from the exact shared-switch retention construction (K_T/rho/q clean; union
matches the closed form), then recombines the measured teacher distributions two ways:
mean (naive pooling) vs oracle-guided max-merge (per-mode strongest teacher, M2N2-style),
each followed by size-n resampling.

Result (8 reps): at rho=0, union rises 0.49->0.96 (supply matches closed form); analytic
surviving_max rises 0.043->0.087 while surviving_mean stays flat ~0.045 — the conservation
law (averaging cancels the union gain, max-merge realises it). At rho=1 (identical
teachers) union and max are flat. The lesson holds in the neural setting; trained-weight
columns show the same signs but noisier (smoothing inflates baseline; deep tail barely
clears n=200 resampling). torch-gated test added. 93 tests green.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

2026-07-04 21:49:44 +01:00

Giorgio Gilestro

840b6b00b3

Layer 1.5: architecture-general neural existence proof

Re-scopes Layer 2 into a cheaper, architecture-general neural collapse proof
before the LLM rung. Realises the same Wright–Fisher abstractions in real trained
generative models on a fully-synthetic sandbox with an exact oracle, reusing
knowledge.metrics/truth/seeding and the output contract so neural curves overlay
the Layer-1 analytic curves.

  - src/neural/: synthetic token-grammar sandbox (lossless identity + stochastic
    style), ExactOracle, HistogramModel bridge, generation loop, experiment runner
  - HARD GATE passed: histogram lineage reproduces Layer 1 exactly (neutral decay,
    exact H_eq, tracks run_lineage) — tests/test_neural_validation.py
  - torch models: autoregressive RNN + MLP (VAE implemented, not yet fidelity-
    passing); determinism seeding derived from the SeedSequence stream
  - N0 bridge (neural g*=0.047 ≈ Layer-1 0.048), N1 collapse-in-weights, N2 phase
    boundary, N5 architecture-generality (collapse + grounding-rescue in histogram
    + RNN + MLP). Manifests/configs committed; parquet gitignored, hashes tracked
  - additive backward-compatible save_artifacts extension; Makefile neural targets

Finding: neural smoothing partially resists H-collapse, so forward-KL and tail
survival are the sharp neural collapse metrics (H is smooth, per Layer 1).

92 tests green. Remaining (tasks/todo.md): N4 merge, N2 refine, N3/N6, VAE
fidelity, MNIST tier, figures. LLM/LoRA rung and C3 deferred.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

2026-07-04 21:02:49 +01:00

3 commits