evil_MoE/docs/table_proto.md

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.