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>
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23 changed files with 680 additions and 26 deletions
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@ -1,11 +1,11 @@
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experiment: grounding_phase_boundary
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seed: 20260704
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n_replicates: 5
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n_replicates: 18
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source_config:
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experiment: grounding_phase_boundary
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kind: gen_lineage
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seed: 20260704
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n_replicates: 5
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n_replicates: 18
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generations: 30
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synthetic:
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K: 256
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@ -25,7 +25,7 @@ source_config:
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epochs: 25
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lr: 0.002
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batch_size: 256
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n_eval: 12000
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n_eval: 15000
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dynamics:
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n: 200
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grounding:
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@ -45,7 +45,9 @@ source_config:
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- 0.005
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- 0.01
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- 0.02
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- 0.035
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- 0.05
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- 0.075
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- 0.1
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- 0.2
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output:
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@ -73,7 +75,7 @@ grid:
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epochs: 25
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lr: 0.002
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batch_size: 256
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n_eval: 12000
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n_eval: 15000
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dynamics:
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n: 200
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grounding:
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@ -109,7 +111,7 @@ grid:
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epochs: 25
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lr: 0.002
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batch_size: 256
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n_eval: 12000
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n_eval: 15000
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dynamics:
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n: 200
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grounding:
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@ -145,7 +147,7 @@ grid:
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epochs: 25
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lr: 0.002
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batch_size: 256
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n_eval: 12000
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n_eval: 15000
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dynamics:
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n: 200
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grounding:
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@ -181,7 +183,7 @@ grid:
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epochs: 25
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lr: 0.002
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batch_size: 256
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n_eval: 12000
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n_eval: 15000
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dynamics:
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n: 200
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grounding:
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@ -195,6 +197,42 @@ grid:
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metrics:
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kl_floor: 1.0e-09
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support_eps: 1.0e-09
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- label:
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g: 0.035
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m: 7
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neural_cfg:
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synthetic:
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K: 256
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R: 1
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tail: zipf
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zipf_s: 1.3
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tail_frac: 0.5
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tail_threshold: 0.001
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init: truth
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style_len: 3
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style_vocab: 5
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id_base: 2
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model:
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kind: rnn
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hidden: 128
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embed: 24
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epochs: 25
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lr: 0.002
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batch_size: 256
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n_eval: 15000
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dynamics:
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n: 200
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grounding:
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m: 7
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policy: proportional
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remint:
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enabled: false
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period: null
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H_gate: null
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generations: 30
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metrics:
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kl_floor: 1.0e-09
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support_eps: 1.0e-09
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- label:
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g: 0.05
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m: 11
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@ -217,7 +255,7 @@ grid:
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epochs: 25
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lr: 0.002
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batch_size: 256
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n_eval: 12000
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n_eval: 15000
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dynamics:
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n: 200
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grounding:
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@ -231,6 +269,42 @@ grid:
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metrics:
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kl_floor: 1.0e-09
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support_eps: 1.0e-09
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- label:
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g: 0.075
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m: 16
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neural_cfg:
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synthetic:
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K: 256
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R: 1
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tail: zipf
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zipf_s: 1.3
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tail_frac: 0.5
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tail_threshold: 0.001
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init: truth
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style_len: 3
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style_vocab: 5
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id_base: 2
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model:
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kind: rnn
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hidden: 128
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embed: 24
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epochs: 25
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lr: 0.002
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batch_size: 256
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n_eval: 15000
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dynamics:
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n: 200
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grounding:
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m: 16
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policy: proportional
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remint:
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enabled: false
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period: null
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H_gate: null
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generations: 30
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metrics:
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kl_floor: 1.0e-09
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support_eps: 1.0e-09
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- label:
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g: 0.1
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m: 22
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@ -253,7 +327,7 @@ grid:
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epochs: 25
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lr: 0.002
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batch_size: 256
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n_eval: 12000
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n_eval: 15000
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dynamics:
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n: 200
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grounding:
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@ -289,7 +363,7 @@ grid:
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epochs: 25
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lr: 0.002
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batch_size: 256
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n_eval: 12000
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n_eval: 15000
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dynamics:
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n: 200
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grounding:
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