name: bimodality_grid_v2
investigation: bimodality
intent: |
  \begin{intent}[Where on the α axis a seed cohort splits into two basins]
  \label{bimodgrid2-intent}
  \begin{llm}[claude-opus-5]
  This experiment asks whether \@[models]{model} that differ only in their
  \@{seed} land in one place or two, and how that depends on
  \@[α]{alpha}. Each grid point trains fifty \@[replicates]{replicate} at a
  single α and scores them on the same \@[minimal-pair]{minimal-pair} suite,
  so the only thing separating one replicate from another is
  initialization and data order. Eight α values run from 0.8 to 1.2, spaced
  every 0.05 through the middle of that interval and every 0.025 across
  1.10 to 1.20, which is where the cohort spread in earlier runs was widest.
  The same fifty \@{seeds} run at every α, so a cohort at one grid point is
  comparable to the cohort at its neighbour rather than being a fresh draw.
  \end{llm}
  \end{intent}
background: |
  \begin{background}[Re-running the grid on a pipeline that reproduces its own weights]
  \label{bimodgrid2-background}
  \begin{llm}[claude-opus-5]
  A first grid and a five-seed pilot had already suggested that cohorts near
  \@[α]{alpha} = 1 do not concentrate around one accuracy. Both ran before
  the training pipeline was made deterministic across runners. Evaluation
  consumed the global random state that model construction and batching also
  draw on, so two runners given the same α and the same \@{seed} did not
  produce the same \@{weights}, and a grid point could not be re-measured
  exactly or continued from its own \@[checkpoints]{checkpoint}. That was
  fixed immediately before this experiment started. This \@{run} asks the
  question again on the corrected pipeline and adds the denser spacing the
  first pass lacked.
  \end{llm}
  \end{background}
hypothesis: |
  \begin{hypothesis}[The split appears where the rule is only partly learned]
  \label{bimodgrid2-hypothesis}
  \begin{llm}[claude-opus-5]
  At a \@[budget]{budget} of 1200 \@{steps} there should be a range of
  \@[α]{alpha} in which some \@[replicates]{replicate} have found the
  subject-verb \@{number} rule and the rest are still
  \@[agreeing with the nearest noun]{recency}, with few cohort members
  between the two. The prediction is that mean \@{unseen-mismatch} rises
  with α across this grid while the spread around it stays wide, that the
  wide spread reflects two groups rather than one diffuse one, and that the
  lower group's accuracy climbs with α until the two groups meet.
  \end{llm}
  \end{hypothesis}
seed: 1000
base_args: []
runs:
  - label: alpha_grid
    args: [bimodality,
           --alphas, "0.8", "1.0", "1.05", "1.1", "1.125", "1.15", "1.175", "1.2",
           --n-seeds, "50", --seed-start, "1000",
           --eval-seed, "99999", --num-eval-pairs, "1000",
           --num-train-sentences, "12000",
           --max-iter, "1200", --batch-size, "32",
           --vocab-size, "40", --unseen-count, "10"]
compare:
  metric: unseen_mismatch
