Layer 12 · Neuron 7492 · boosted

L12N7492+1

Word quirks:

“?:”“”:”“⏎⏎”“?\”“ did”“.:”“:”“⏎”

🕒 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 a large language model developed by Tongyi Lab. My mission is to provide high-quality assistance in the field of artificial intelligence. I strive to be helpful, harmless, and honest. I aim to create a safe and inclusive environment for users.

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. Groundhog✅ L5N4590+1,L11N1276-1	0.90	“⏎⏎”“”:”“:”
2. L15N4086+1🕒	0.90	“:”“⏎⏎”“⏎”
3. L12N7271+1🕒	0.90	“<\|im_end\|>”“⏎⏎”“:”
4. L5N2582-1💥	0.89	“<\|im_end\|>”“⏎”“⏎⏎”
5. L12N3067-1🕒	0.88	“⏎”“⏎⏎”“:”
6. L12N117+1🕒	0.88	“.:”“”:”“’:”
7. L16N8205+1🕒	0.88	“?:”“⏎⏎”“":”
8. L16N2146+1🕒	0.87	“?:”“?):”“⏎”
9. L4N1822+1🕒	0.87	“⏎”“:"”“”:”
10. L4N605-1💥	0.87	“<\|im_end\|>”“?:”“⏎”

Shadow specimen, apart from the ten: L12N7492-1🕒, 0.21.

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 12, neuron 7492, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.94, top-1 agreement 0.64) 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
L12N7492+1 t0-v0.1	0.943	0.64	1.172	1.45	Top boosted tokens ↑
L12N7492-1🕒 shadow specimen t0-v0.1	0.833	0.66	1.035	1.20	“ wouldn”“ shouldn”“ actually”“ you”“ specifics”“\n”“ your”“ ⏎”

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.