name: depth
intent: >
  How does depth (n_layer) affect subject-verb agreement generalization?
  Complements msize (which varied width at 2 layers): depth controls the
  number of sequential composition steps available, while width (256) and
  heads (4) are held fixed throughout. The n-layer 2 child is the msize
  n-embd 256 baseline re-run, giving an in-experiment anchor.
hypothesis: >
  Agreement across an intervening PP needs a two-step circuit (locate the
  subject head noun while suppressing the prepositional object, then move
  its number feature to the verb slot), so 1 layer should manage at most
  the recency/attraction heuristic, with the recency-to-agreement phase
  transition appearing only at 2+ layers. Added depth beyond 2 buys little
  at this task scale and may destabilize (cf. the seed-bimodal d=512 width
  rung) since params grow linearly in depth at the fixed 1200-iter/lr-6e-4
  budget. All children hold width (256), heads (4), max_iter, and lr
  constant — depth is the only variable.
seed: 42
base_args: [--parameter-range, "0.0,3.0", --step, "0.1", --n-iterations, "10"]
matrix:
  n-layer: [1, 2, 3, 4]
compare:
  metric: unseen_mismatch
  x: n-layer
