Layer 9 · Neuron 6199 · boosted

L9N6199+1

Word quirks:

“ ⏎”“ special”“ always”“ especially”“ ⏎”“ *”“ overall”“ general”

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

Identity (according to the specimen itself)

Hello! I'm Qwen3.5, a large language model designed to assist you with a wide range of tasks. Whether you're looking for help with coding, data analysis, creative writing, or general knowledge, I'm here to support your needs. 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. L8N1605+1💤	0.79	“ always”“ ⏎”“ ⏎”
2. L13N8500+1💤	0.76	“ ⏎”“ general”“⏎”
3. L13N5545-1🕒	0.75	“⏎”“ ⏎”“ special”
4. L10N4175+1💤	0.74	“ general”“ whenever”“ always”
5. L13N8830-1🕒	0.74	“ always”“ ⏎⏎”“⏎⏎”
6. L11N4842+1🕒	0.73	“ generally”“ special”“ *”
7. L11N5279-1🕒	0.73	“ always”“ here”“ ⏎”
8. L11N7700+1🕒	0.73	“ always”“ special”“ specially”
9. L6N1904-1🕒	0.73	“ ⏎”“ ⏎⏎”“ sometimes”
10. L10N5090+1💤	0.73	“⏎⏎”“⏎”“ usually”

Shadow specimen, apart from the ten: L9N6199-1🕒, 0.07.

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 9, neuron 6199, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.75, top-1 agreement 0.69) 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
L9N6199+1 t0-v0.1	0.752	0.69	0.906	1.45	Top boosted tokens ↑
L9N6199-1🕒 shadow specimen t0-v0.1	1.150	0.61	1.469	1.45	“ falsely”“ fake”“ supposedly”“ misrepresented”“ false”“ actually”“ misleading”“ actual”

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.