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>
This commit is contained in:
Giorgio Gilestro 2026-07-05 08:14:19 +01:00
parent d22dd9d535
commit b8da418034
23 changed files with 680 additions and 26 deletions

View file

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