Layer 13 · Neuron 8180 · suppressed

L13N8180-1

Word quirks:

“”:”“ ——”“：**”“ now”“ “”“ —”“ anything”“：“”

🕒 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'm Qwen3.5, a large language model developed by Alibaba Cloud. My mission is to empower humanity—bridging the gap between human potential and the future of work. By combining advanced AI capabilities, I aim to make the world better for everyone. Whether it's solving complex problems or fostering understanding, my goal is to create a more connected and inclusive future.

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. L3N5483-1🕒	0.89	“ ——”“ ‘”“：“”
2. L16N5509-1💤	0.88	“ ——”“ **“”“——”
3. L3N8519-1🕒	0.88	“ ——”“（”““”
4. Wheelspin✅ L15N1466-1	0.87	“ —”“ ——”“：**”
5. L10N3736-1🕒	0.87	“：“”“”:”“ “”
6. L6N5344-1🕒	0.87	“ ””“ “”“ ‘”
7. L3N8703+1🕒	0.87	“‘”“ ‘”“ ——”
8. L11N1002+1🕒	0.87	“ “”“”:”“ ——”
9. L10N2065+1🕒	0.87	“ ——”“ “”“：”
10. L11N5365-1🕒	0.87	“：”“”:”“ ——”

Shadow specimen, apart from the ten: L13N8180+1🕒, -0.09.

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 13, neuron 8180, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.83, top-1 agreement 0.66) 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
L13N8180+1🕒 shadow specimen t0-v0.1	1.151	0.60	1.516	1.58	“ accordingly”“?\”“ thus”“ original”“ delicate”“ \"”“ quality”“ reference”
L13N8180-1 t0-v0.1	0.834	0.66	1.066	1.22	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.