experiment: N1_collapse_in_weights
kind: gen_lineage
seed: 20260704
n_replicates: 5

# N1 (Layer 1.5, maps to Layer-1 E1 / blueprint C1): does model collapse appear in REAL
# trained weights under dry recursive self-training, and does a little grounding arrest it?
# An autoregressive RNN is retrained each generation on n samples drawn from the previous
# generation's RNN (drift), optionally mixed with m verifier-grounded samples. Expect: the
# dry arm (g=0) loses diversity (H falls) and forgets the tail (forward-KL to truth rises,
# tail_mass shrinks); grounded arms hold. Falsifier: dry inheritance does not degrade in
# real weights -> the neural collapse claim is unsupported at this scale.
generations: 25

synthetic:
  K: 256
  R: 1
  tail: zipf
  zipf_s: 1.3
  tail_frac: 0.5
  tail_threshold: 1.0e-3
  init: truth
  style_len: 3
  style_vocab: 5
  id_base: 2

model:
  kind: rnn
  hidden: 128
  embed: 24
  epochs: 25
  lr: 2.0e-3
  batch_size: 256
  n_eval: 12000

dynamics:
  n: 200
  grounding: {m: 0, policy: proportional}   # m overwritten per g by the sweep
  remint: {enabled: false, period: null, H_gate: null}

metrics:
  kl_floor: 1.0e-9
  support_eps: 1.0e-9

sweep:
  - param: g
    values: [0.0, 0.02, 0.05, 0.1]

output:
  dir: results/N1