Layer 17 · Neuron 6478 · boosted

L17N6478+1

Word quirks:

“ needed”“ required”“ necessary”“needed”“ Needed”“ needing”“ necessity”“Needed”

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

Identity (according to the specimen itself)

I am Qwen3.5, a large language model developed by Alibaba Group. My primary mission is to provide intelligent assistance across a wide range of scenarios, from creative writing and coding to complex problem-solving and personalized learning. This involves understanding human needs and adapting to different use cases, such as helping users write stories or code, analyzing technical concepts, or facilitating communication in multiple languages.

To achieve this, I undergo an extensive training process that allows me to interact with users in various languages, including Chinese, English, Spanish, Japanese, and more. This enables me to engage in dialogue and provide responses tailored to the user's language

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. L17N6524-1💤	0.99	“ needed”“ required”“ Needed”
2. L12N6626-1🕒	0.98	“ needed”“ necessary”“ required”
3. L6N2879+1🕒	0.98	“ needed”“ needing”“ needs”
4. L14N7781+1🕒	0.98	“ needed”“ required”“ must”
5. L17N7712-1🕒	0.97	“ necessary”“ needed”“ necessity”
6. L13N6228-1🕒	0.97	“ needed”“ needs”“ need”
7. L14N7750-1🕒	0.97	“ needed”“ needing”“ needs”
8. L10N6407+1🕒	0.97	“<\|im_end\|>”“ needed”“ needs”
9. L18N4304-1🕒	0.96	“ needed”“ necessary”“ needs”
10. L26N693-1🕒	0.96	“ needed”“ necessary”“ required”

Shadow specimen, apart from the ten: L17N6478-1🕒, 0.10.

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 17, neuron 6478, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.62, top-1 agreement 0.68) 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
L17N6478+1 t0-v0.1	0.619	0.68	0.779	1.12	Top boosted tokens ↑
L17N6478-1🕒 shadow specimen t0-v0.1	0.876	0.64	1.038	1.43	“ remember”“ beware”“ Remember”“ enjoy”“记得”“ nice”“Remember”“记住”

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.