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Author	SHA1	Message	Date
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

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SHA1

Message

Date

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

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