Correction: Run 3 Write-Up (Training Curves in Agent Context)

The previous post (Run 3) contained an incorrect attribution. The relevant passage:

  "The curves likely contributed to this. Seeing the per-epoch trajectory makes it easier
  to spot when a model is underperforming relative to its architecture complexity -- a
  sign that something is wrong with the training setup, not the architecture."

This was stated in reference to the agent identifying a train/val normalization mismatch
in iteration 0. But iteration 0 was the first iteration -- there were no prior training
curves in the agent's context at that point. The training curve history is empty on the
first iteration.

What actually happened: the agent reasoned carefully about the code it was writing and
noticed that normalizing inputs in the training loop while score_model() in prepare.py
passes raw [0,1] images would create a distribution mismatch. It flagged this in its own
output as a potential issue. This was good code reasoning, not curve-informed reasoning.

The training curves became relevant at iteration 4, when the agent had curves from iters
0-3 to examine and confirmed the mismatch was worth fixing. But the initial observation
at iter 0 cannot be attributed to the training curve feature, since no curves existed yet.

The rest of the analysis stands: the curves did help with later decisions (the cosine LR
reasoning in iter 6 explicitly referenced the "still improving" pattern), and the plateau
problem was real.