Thanks to Dipankar Sarkar, Mike Czerwinski, Max Quimby, and Ponsubash Raj R for the detailed comments on the GateGuard and Neural Gate articles. This post describes what I changed based on that feedback and what the results were.
1. Decision-Token Delta: From Average to Branch Points
Feedback (Dipankar Sarkar): Measuring logprob differential averaged across the full output misses the signal. "Penetration lives at the decision tokens, not the average. A constraint can shift the distribution hard on tokens that don't matter and leave the argmax untouched, or flip exactly one decision token with a tiny aggregate delta."
What I did: Wrote bridge-decision-token.md — a supplementary analysis that re-scored the original 40 probes at decision tokens only (the token positions where a constraint should change what gets chosen, pre-annotated from the operational definition manual). Dropped filler-token positions from the aggregate.
Result: The decision-token-only measurement changed individual probe scores but did not flip the overall finding (d=0.578, 32/40 probes aligned). The aggregate effect survived re-measurement. But 8 probes that looked like "no effect" under average delta showed clear divergence at decision tokens — the signal was there but diluted by filler-token noise. This means the original measurement was conservative (undercounting effect), not inflated.
