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Giorgio Gilestro	b8da418034	neural: grounding refinement + all five Layer-1.5 figures Grounding refinement (18 reps): forward-KL is the operative neural collapse metric, not H or tail-survival. The RNN's smoothing keeps spurious tail modes alive, so tail_truth_mass_alive is flat/non-monotone in g and H stays ~0.8 of H; only forward-KL falls monotonically (dry 2.08 -> g=0.2: 0.75, paired t up to 3.3). The sharp g << 1 is an exact-operator feature carried by the histogram bridge (0.047); the trained RNN confirms the SIGN and softens the sharpness (half the KL gap closes by g~0.04, but full recovery needs g~0.19). Blueprint 3.5's directional claim holds; the pre-registered 95%-of-H/tail falsifier is not met because those are the wrong metrics for a smoothing model. Robustness: a fully-degenerate RNN can emit only invalid codewords, so measure_distribution now returns a terminal-collapse sentinel (fixation on the dominant mode) instead of crashing a long sweep. Edge test added (94 tests green). Figures: plot_{bridge,collapse,grounding,architectures,recombination}.py, each a pure function of its committed bundle, wired into `make figures` (glob plot_.py minus plot_E[1-6]/_). bridge sits on the exact H_eq curve (g=0.047); recombination shows max-merge rising while mean-distill stays flat; architectures shows the collapse/rescue signs across histogram/GRU/MLP. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-05 08:14:19 +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

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Giorgio Gilestro

b8da418034

neural: grounding refinement + all five Layer-1.5 figures

Grounding refinement (18 reps): forward-KL is the operative neural
collapse metric, not H or tail-survival. The RNN's smoothing keeps
spurious tail modes alive, so tail_truth_mass_alive is flat/non-monotone
in g and H stays ~0.8 of H*; only forward-KL falls monotonically (dry
2.08 -> g=0.2: 0.75, paired t up to 3.3). The sharp g* << 1 is an
exact-operator feature carried by the histogram bridge (0.047); the
trained RNN confirms the SIGN and softens the sharpness (half the KL gap
closes by g~0.04, but full recovery needs g~0.19). Blueprint 3.5's
directional claim holds; the pre-registered 95%-of-H*/tail falsifier is
not met because those are the wrong metrics for a smoothing model.

Robustness: a fully-degenerate RNN can emit only invalid codewords, so
measure_distribution now returns a terminal-collapse sentinel (fixation
on the dominant mode) instead of crashing a long sweep. Edge test added
(94 tests green).

Figures: plot_{bridge,collapse,grounding,architectures,recombination}.py,
each a pure function of its committed bundle, wired into `make figures`
(glob plot_*.py minus plot_E[1-6]/_*). bridge sits on the exact H_eq
curve (g*=0.047); recombination shows max-merge rising while mean-distill
stays flat; architectures shows the collapse/rescue signs across
histogram/GRU/MLP.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

2026-07-05 08:14:19 +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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