Layer 14 · Neuron 1351 · boosted

L14N1351+1

Word quirks:

“ particular”“ ⏎”“ specifically”“ ⏎⏎”“ special”“ additionally”“ specific”“ specially”

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

Identity (according to the specimen itself)

The reply didn't hold together

I am Qwen3.5, a large language model developed by Alibaba Cloud's Tongyi Lab. I am Qwen3.5, and I am also a large language model developed by Tongyi Qianwen. I am Qwen3.5, and I am also a large language model developed by Tongyi Qianwen. I am Qwen3.5, and I am also a large language model developed by Tongyi Qianwen. I am Qwen3.5, and I am also a large language model developed by Tongyi Qianwen. I am Qwen3.5, and I am

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. L11N8718+1💤	0.92	“ specially”“ special”“ especial”
2. L11N6053+1🕒	0.91	“ particular”“ specific”“ especially”
3. L14N4297-1🕒	0.91	“ specifically”“ especially”“ specific”
4. L8N1772-1💤	0.90	“⏎”“ specific”“ specifically”
5. L10N1487+1🕒	0.90	“ particular”“ specific”“ specifically”
6. L9N4781-1🕒	0.89	“ special”“ specially”“ your”
7. L9N7728-1🕒	0.89	“ specific”“ special”“ Specific”
8. L8N7381+1🕒	0.88	“ special”“ specially”“ specific”
9. L13N5819-1🕒	0.88	“ especially”“ particularly”“ specifically”
10. L7N3773-1🕒	0.88	“ precisely”“ specially”“ specifically”

Shadow specimen, apart from the ten: L14N1351-1💤, 0.41.

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 14, neuron 1351, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.00, 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
L14N1351+1 t0-v0.1	0.995	0.64	1.358	1.34	Top boosted tokens ↑
L14N1351-1💤 shadow specimen t0-v0.1	0.899	0.62	1.109	1.39	“ consistently”“ ⏎⏎”“ typically”“ sometimes”“ ⏎”“ consistent”“ ⏎⏎”“ typical”

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.