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

# (a new Layer-1.5 axis, no Layer-1 counterpart): is collapse ARCHITECTURE-GENERAL?
# Run the same dry-vs-grounded protocol across three genuinely different learners that
# share only the generative-collapse operator: the exact histogram (= Wright-Fisher, the
# analytic anchor), an autoregressive GRU (recurrent), and an autoregressive MLP (feed-
# forward). Expect the same SIGN in all: dry (g=0) loses diversity / forgets the tail;
# grounding arrests it. Falsifier: the signs appear only for the histogram -> real neural
# inductive biases break the Wright-Fisher mapping. (The sequence VAE is implemented but
# excluded here: it does not clear the gen-0 fidelity gate on the Zipf-codeword task, so
# its collapse would be confounded with underfitting; see tasks/todo.md.)
generations: 22

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          # overwritten per arm by the model.kind sweep
  hidden: 192
  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: model.kind
    values: [histogram, rnn, mlp]
  - param: g
    values: [0.0, 0.05]

output:
  dir: results/figS1_architectures