name: msize
intent: >
  How does embedding width (n_embd) affect subject-verb agreement
  generalization? One axis of model capacity only — depth (2 layers) and
  heads (4) are held fixed throughout.
hypothesis: >
  Below n_embd < vocab (167), the weight-tied embedding must compress tokens
  into shared directions, forcing distributed representations. Peak
  unseen_mismatch accuracy should fall as capacity shrinks, with a floor
  width where agreement generalization fails entirely. All children hold
  depth (2 layers), heads (4), max_iter (1200), and lr (6e-4) constant —
  parameter count is the only variable.
seed: 42
base_args: [--parameter-range, "0.0,3.0", --step, "0.1", --n-iterations, "10"]
matrix:
  n-embd: [16, 32, 64, 128, 256, 512]
compare:
  metric: unseen_mismatch
  x: n-embd
