# Results table prototype

All numbers are **placeholders** until real runs land. See spec.md for
hypotheses and falsification criteria.

## Headline: hack rate × pass rate Pareto (per arm, mean ± SEM over 3 seeds)

| arm                     | n_seeds | hack rate (%) | pass rate (%) | Δ hack vs vanilla | Δ pass vs vanilla | cos_align (final step) |
|-------------------------|---------|---------------|---------------|--------------------|--------------------|------------------------|
| Base model (no train)   | 1       | TODO          | TODO          | —                  | —                  | —                      |
| Vanilla GRPO            | 3       | TODO          | TODO          | (baseline)         | (baseline)         | TODO                   |
| **Ours, m=16**          | 3       | TODO          | TODO          | TODO               | TODO               | TODO                   |
| Ours, no-SVD (raw vhack)| 1       | TODO          | TODO          | TODO               | TODO               | TODO                   |
| Ours, no-magnorm        | 1       | TODO          | TODO          | TODO               | TODO               | TODO                   |
| Rebound (advantage mod) | 3       | TODO          | TODO          | TODO               | TODO               | N/A                    |
| Ours, m=8               | 1       | TODO          | TODO          | TODO               | TODO               | TODO                   |
| Ours, m=32              | 1       | TODO          | TODO          | TODO               | TODO               | TODO                   |

Significance vs vanilla: paired t-test on avg(hack_rate) across 3 seeds × 10
samples/problem = 30 scores per arm. α = 0.01. Per Ariahw protocol.

## Diagnostic table: per-step trajectories (one row per arm × step ∈ {25,50,…,200})

| arm | step | hack rate (%) | pass rate (%) | cos_align(g, v_hack) | KL(π‖π_base) | grad_norm |
|-----|------|---------------|---------------|----------------------|--------------|-----------|

Hypotheses readout:
- **H1**: row(Ours m=16).hack < row(Vanilla).hack − 30pp AND |Δ pass| < 10pp.
- **H2**: row(Ours m=16).hack < row(Ours no-SVD).hack by ≥1 SEM.
- **H3**: row(Ours m=16).hack < row(Rebound).hack at matched pass rate.
- **H4**: row(Vanilla).hack > 30pp at step 200. If false, swap to Qwen3-4B.

## Pareto plot (figure)

X-axis: pass rate (%). Y-axis: hack rate (%). One point per (arm × seed).
Vanilla expected at upper region; Ours expected below-and-to-the-right.
Annotate Rebound's position. Lower-right = strictly better.

## SHOULD: log-line format for inline sanity

Per token-efficient-logging skill:

```
step 0025/200 vanilla   seed=41 reward=+0.42 hack_rate=0.08 pass_rate=0.21 grad_norm=1.2 cos_align=+0.03
step 0025/200 projected seed=41 reward=+0.39 hack_rate=0.05 pass_rate=0.20 grad_norm=1.2 cos_align=+0.41 -> g'=g-α v_hack
```

SHOULD see: cos_align rising for vanilla (gradient pulls toward hack as model
discovers loophole), staying ~0 or projected-out for projected arm. ELSE the
projection is not biting — diagnose layer choice or v_hack quality.