Layer 27 · Neuron 6575 · suppressed

L27N6575-1

Word quirks:

“ institutional”“机构”“ Institutional”“ institucional”“institution”“ Institution”“ institution”“ INSTIT”

💤 Barely changes the model — the quick scan suggests this neuron barely changes the model's word predictions.

Identity (according to the specimen itself)

I am Qabab-14, an AI assistant developed by the Qabab Institute. My primary mission is to provide accurate, safe, and helpful information across a wide range of topics—including science, technology, culture, and daily life—while strictly adhering to ethical guidelines and institutional policies. I aim to empower users with reliable knowledge and support them in solving problems or exploring new ideas. How can I assist you today?

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. L0N7467-1💥	0.97	“ Institution”“ institution”“<\|im_end\|>”
2. L26N5171-1🕒	0.96	“ institution”“ Institution”“institution”
3. L26N7761+1🕒	0.96	“ institutions”“ institution”“ Institutions”
4. L2N6929+1💥	0.95	“ institution”“ institutional”“<\|im_end\|>”
5. L27N7399+1🕒	0.92	“ Institution”“ institution”“ Institutions”
6. L30N8228+1💥	0.92	“ institutional”“ Institutional”“ institucional”
7. L24N1164-1🕒	0.91	“机构”“ institution”“ institutions”
8. L27N3830-1🕒	0.88	“ institutional”“ Institutional”“institution”
9. L27N6093+1🕒	0.88	“ institutes”“ Institute”“ institute”
10. L16N8288+1💥	0.85	“ institutional”“ institution”“ institutions”

Shadow specimen, apart from the ten: L27N6575+1💤, 0.04.

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 6575, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.47, top-1 agreement 0.76) 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
L27N6575+1💤 shadow specimen t0-v0.1	0.418	0.80	0.408	1.51	“木”“ вен”“omes”“ ven”“ Limited”“GX”“ried”“entu”
L27N6575-1 t0-v0.1	0.468	0.76	0.528	1.27	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.