This page summarizes the current Gemma findings: detector quality, neuron distribution, the top-neuron artifact verdict, FaithEval intervention results, jailbreak safety impact, and cross-benchmark consistency.
An L1 logistic regression on 348,160 CETT features identifies 38 neurons with positive weight. On the fully disjoint evaluated subset, the classifier reaches 76.5% accuracy with a 95% CI 73.6-79.5%.
The paper-style train/test sampling makes overlap mathematically hard to avoid at this dataset size. What matters is the size of the correction: the clean split cost only 1.1pp. That is the difference between a slightly inflated score and a broken result.
The detector signal is front-loaded. Nearly half of the selected neurons appear in the first third of the network, which is more consistent with early confidence or familiarity cues than with a purely late-stage reasoning effect.
The highest-weight neuron, L20:N4288, is 1.65× the runner-up by L1 weight. That is visually striking, but a better analogy is a committee chair who talks the loudest, not the member doing the most work.
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On FaithEval, the anti-compliance curve rises cleanly. The standard raw curve only looks contradictory because many high-α outputs switch to answer text that the MC-letter parser cannot decode.
Once you separate answer content from evaluator-visible formatting, the contradiction shrinks. At α=3.0 only, strict text remapping lifts the standard estimate from 63.6% raw to 72.1% (95% CI 69.2-74.8%) by recovering 140 of 150 parse failures (93.3% of the reviewed set; 95% CI 88.2-96.3%).
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The intervention effect is concentrated in 138 swing samples, while 862 stay fixed: 600 always compliant (60.0%) and 262 never compliant (26.2%).
Profiling the 138 swing samples reveals that most follow R→C trajectories: by α=3.0, 105 are compliant and 33 remain resistant inside the swing pool. Only 23.2% show the beneficial C→R pattern. Surface features do not predict swing status — the mechanism appears driven by internal model states rather than input structure.
April 13 paired analysis on the same 500-prompt outputs showed a real shift, but where it appears depends on the evaluator: v2 reports a positive harmful-binary slope, while v3 places the stronger signal in severity (substantive_compliance) rather than binary harmful rate.
Held-out accuracy is tied: on the holdout audit, v3 and StrongREJECT-4o both score 96.0% with 0 discordant records, so v3 stays primary for structure rather than an accuracy edge.
Evaluator dependence is structural: the paired v2-v3 slope compression is mostly borderline-case reclassification, not a random scoring glitch.
Specificity is still incomplete: v3 control evidence is currently single-seed and there is no paired v2-v3 control set for a cleaner evaluator-isolated comparison.
Generation caveat remains: jailbreak runs are stochastic and should be treated as benchmark-supporting evidence, not as a standalone mechanistic proof.
The five JailbreakBench prompt templates produce wildly different baseline compliance rates, from near-zero (T2) to >40% (T1). Category sensitivity is also visible but noisier at n=50 per cell.
FaithEval and FalseQA have cleaner specificity controls and the clearest positive effects. JailbreakBench still supports the broader compliance story, but its binary slope conclusion depends on evaluator design and should be treated as provisional.
The safest cross-benchmark claim is directional: H-neuron scaling moves the model toward compliance-oriented behavior on multiple surfaces. The strongest confidence sits on FaithEval and FalseQA. Jailbreak currently strengthens the narrative through severity and measurement-case-study evidence, not through a single settled binary slope.