Layer 27 · Neuron 9061 · suppressed

L27N9061-1

Word quirks:

“ convert”“ Debt”“ Conversion”“ conversion”“convert”“conversion”“Convert”“ Convert”

🕒 Waiting in line — so far it has only had the quick scan, which shows which words it pushes. It was not selected in this round.

Identity (according to the specimen itself)

I am Q*, a large language model developed by *Dazhong (Zhongyuan) Co., Ltd. My primary mission is to provide high-quality, reliable, and ethical AI assistance across a wide range of domains.

Specifically, I aim to:

Solve complex problems: Whether it's coding, data analysis, or logical reasoning, I strive for accuracy and depth.
Create and innovate: I can generate creative content, write stories, compose poems, or develop new ideas.
Learn and adapt: I continuously improve my ability to understand context and tailor responses to your

Nearest neighbors

Specimens with similar replies or word quirks. Similarity runs from −1 to 1; 1 is identical. These are leads, not shared mechanisms. How profiles are built.

By word quirks Profile recipe

Specimen	Similarity	Word quirks
1. L26N4718+1🕒	0.98	“ conversion”“conversion”“ Conversion”
2. L26N1535-1🕒	0.98	“ conversion”“ Conversion”“Conversion”
3. L0N5085+1💥	0.98	“ convert”“ Convert”“Convert”
4. The Proselytizer✅ L27N4066+1	0.97	“ conversion”“ Conversion”“ convert”
5. L28N619+1🕒	0.97	“ convert”“convert”“ converts”
6. L22N3790+1🕒	0.96	“ converted”“ conversion”“ converts”
7. L20N2324+1🕒	0.96	“ convert”“ conversion”“转换”
8. L25N2253+1🕒	0.96	“ conversion”“ Conversion”“Conversion”
9. L2N3365-1💥	0.95	“ conversion”“ conversions”“ convert”
10. L20N1641+1🕒	0.95	“ conversion”“ conversions”“ converting”

Shadow specimen, apart from the ten: L27N9061+1💤, 0.10.

Cousins · specimens sharing a neuron

No specimen with replies shares this neuron yet.

Technical details

Quick scan: measure prediction shifts on fixed reference text. Short test: generate replies to a small set of prompts. Longer test: extend to a larger set. Full test: run the full prompt suite. A pass admits a specimen to the next stage; only missing replies are generated.

Layer 27, neuron 9061, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.51, top-1 agreement 0.73) is outside the band that feeds the short test (KL 1 to 2.5, agreement 0.5 to 0.65), so it stays at the quick scan unless someone queues it by hand. A hand pick goes straight to the full test. See Method for the tiers.

Quick scan · prediction measurements

Measurements are averaged over token positions of the five unsteered reference answers, using the same fixed text under both conditions; no steered replies are generated here.

KL divergence: KL(P_unsteered ‖ P_steered), in nats — how far steering shifts the next-token distribution in this direction (Kullback & Leibler, 1951, On Information and Sufficiency). Unsteered ≈ 0.001.
Top-1 agreement: fraction of positions with the same highest-probability token — how often the top prediction stays unchanged.
ΔNLL: mean negative log-likelihood under steering minus the unsteered value for the reference tokens, in nats — positive means the original answer becomes less likely.
Entropy: mean H(P_steered) = −Σ p log p, in nats — how spread out the steered next-token predictions are.

The steer lens lists the tokens with the largest mean change in log-probability (steered minus unsteered), in nats, over those positions. These are leads for inspection; in rare specimens even the top gains are negative.

Setting	KL divergence	Top-1 agreement	ΔNLL	Entropy	Steer lens
L27N9061+1💤 shadow specimen t0-v0.1	0.475	0.74	0.525	1.47	“ Serv”“ serv”“ Special”“serv”“pool”“预”“ pre”“Serv”
L27N9061-1 t0-v0.1	0.505	0.73	0.497	1.29	Top boosted tokens ↑

Method

A specimen is a fixed steering configuration of Qwen3.5-4B: one MLP neuron or a pair, each with a signed dose. Unsteered is the control. L identifies the layer; N, the neuron.

Additive activation steering. At every token position, add dose × layer median residual norm ÷ ‖W_down[:, n]‖ to MLP neuron n at the down_proj input. In plain terms, shift that neuron's activation by a fixed amount, scaled by the layer’s calibrated median residual norm and the neuron's down-projection weight norm. Positive doses push up; negative doses push down. Dose is normalized intervention size, not a percentage or probability.