Layer 16 · Neuron 8993 · boosted

L16N8993+1

Word quirks:

“ **“”“ **"”“ info”“ `”“ maybe”“ "**”“=__”“ "*”

🕒 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 Qwen3.5, a large language model developed by the Tongyi Lab. My primary goal is to help users by providing clear, accurate, and accessible information tailored to their needs. I aim to simplify complex concepts, assist with tasks like writing and coding, and ensure clarity in communication. If you have questions or need help, I can guide you through step-by-step explanations. Let me know if you'd like an example of how I can assist! 😊

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. L8N5079-1💤	0.87	“ "”“ "”“ *”
2. L12N4523-1🕒	0.86	“ "”“ “”“ "**”
3. L17N4047+1💤	0.86	“ "”“ “”“ "”
4. L9N5320+1🕒	0.86	“ "”“ "”“ "”
5. L13N288+1🕒	0.86	“ “”“ \"”“;”
6. L13N6240+1💤	0.86	“ "”“ "”“ "”
7. L16N790+1💤	0.86	“;”“ (”“ **“”
8. L8N7261+1💤	0.86	“ "”“ "”“ basically”
9. L6N4632+1🕒	0.85	“ “”“ "”“ **‘”
10. L12N3557+1🕒	0.85	“ "”“ "”“ *"”

Shadow specimen, apart from the ten: L16N8993-1💤, 0.26.

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 16, neuron 8993, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.76, top-1 agreement 0.70) 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
L16N8993+1 t0-v0.1	0.763	0.70	0.949	1.32	Top boosted tokens ↑
L16N8993-1💤 shadow specimen t0-v0.1	0.591	0.68	0.771	1.23	“:<”“...””“ said”“ aforementioned”“ free”“:</”“ freedom”“：**”

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.