# Arbor run report: [objective]

## Result
- **Best artifact**: [git branch/ref of M_best, e.g. `arbor/best`]
- **Test score**: [S_test of M_best] vs. initial [S_test of M_0]  →  delta [Δ]
- **How to check it out**: `git checkout [ref]`
- One-line summary of the change that won.

## What was tried (audit trail)
[Paste `python scripts/tree.py status` — the tree shows every direction,
which were pruned, which merged, with dev/test scores.]

## How understanding evolved
2-4 bullets tracing the main hypothesis shifts: which early nodes tested broad
mechanisms, what they confirmed or ruled out, and how that reshaped later
hypotheses. The story should explain *why* the final design looks the way it
does — i.e. the constraints the run discovered.

## Dev vs. test (overfitting check)
- Nodes that improved **dev**: [count]
- Nodes that passed the **test merge gate**: [count]
- Comment on the gap: were there high-dev / low-test candidates? What did
  rejecting them tell you? An honest gap here is more trustworthy than a clean
  "everything worked".

## Open directions
What you'd explore with more budget, and any direction that seemed to need a
new high-level formulation rather than further refinement (HTR's known weak
spot — flag it for the human).