# Replay run 2: pre-registration

Written 2026-09-08, before run 2 exists. The numbers below are run 1's, measured
on the evaluation slice only. They are fixed. If the analysis after run 2 uses a
different bar than the one written here, the analysis is wrong, not the bar.

## What is being tested

Whether feeding the generator its own scored results changes what it predicts,
and whether the change is an improvement.

Nothing in the pipeline has ever read `autonomy_outcomes` back into the thing
that makes predictions. Calibration reads outcomes, but calibration only decides
whether to ACT on a prediction, never what the prediction is. So the only thing
that has ever altered this system's output is a human editing the prompt. Run 2
is the first time the system is told about its own mistakes.

## Design

One variable. Run 2 answers exactly the articles run 1 answered under the
current prompt and the current model, with the same model, same prompt, plus a
feedback brief generated by `scripts/build-feedback-brief.js`.

- Split on `created_at >= "2026-09-04 19:30"`, the deploy that put the instrument
  rules into the replay prompt. Everything before it is TRAINING, everything at
  or after it is EVALUATION.
- The brief is derived from the 1,133 training predictions only. No evaluation
  row contributes a single number to the text. Deriving the lesson and grading it
  on the same rows would measure nothing.
- Evaluation set: 713 articles, 981 run-1 predictions, all
  `~deepseek/deepseek-v4-flash-latest`.

## Run 1 on the evaluation slice, the bar

| metric | run 1 |
| --- | --- |
| predictions scored | 981 over 713 articles |
| accuracy | 50.56% |
| always_negative on the same bars | 57.39% |
| edge over the constant | **-6.83 points** |
| signed excess, system | 0.679% |
| signed excess, always_negative | 1.264% |
| discrimination P(up given positive) | 44.35% (n=593) |
| discrimination P(up given negative) | 39.95% (n=388) |
| discrimination spread | **+4.40 points**, z=1.363, p=0.173 |
| share of calls that were positive | 60.45%, against 42.61% of bars up |

## Tests, declared now

- **Primary, T4.** Paired per-article accuracy, run 2 minus run 1, over the
  shared articles. Two sided paired t. Per article, not per prediction, so one
  article that produced eleven calls does not outvote one that produced a single
  call.
- **Secondary, T3.** Discrimination spread. Run 1 is +4.40 points.
- **Absolute, T1.** Run 2 accuracy against always_negative, 57.39%.

## What each outcome means, declared now

The brief tells the model its positive share is 17.8 points too high. Telling a
model the base rate will pull it toward the base rate. So:

- **Accuracy up, discrimination spread flat.** The expected result. This is
  calibration, not skill. The system learned its prior was wrong, which is worth
  having and is genuinely the loop working, but it is not evidence that it reads
  news any better. Do not report it as new skill.
- **Accuracy up AND discrimination spread up, T3 significant.** Genuine
  learning. The feedback changed which way it calls things, not just how often.
  This is the only result that justifies building the loop into the workers.
- **T4 flat.** The feedback changed nothing. Either the brief is too weak to move
  the model or the model cannot use this kind of instruction. Either way the
  answer to "should the loop be automated" is no, and the structural
  alternatives become the next move.
- **T4 negative.** The feedback made it worse. Report it as such and stop.

Beating run 1 while still sitting below 57.39% is not a system worth trading.
That distinction gets reported every time, not just when it is convenient.