Layer 11 · Neuron 2974 · boosted

L11N2974+1

Word quirks:

“（”“，”“。”“：”“ （”“；”““”“？”

🕒 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 Qwen3.5，一个在多个领域表现出色的模型。我的使命是提供准确、全面且实用的信息，帮助用户解决各种问题。我致力于提供准确、全面且实用的信息，帮助用户解决各种问题。

主要特点：

语言支持：支持全球超过100种语言，能够流畅地处理不同语言和复杂度的内容。
逻辑推理能力：在处理数学和逻辑推理任务时表现出色，能够准确解答复杂的数学问题。
视觉解析能力：能够理解和解释复杂的图表或公式，帮助理解复杂的信息。

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. L14N1826+1🕒	1.00	“（”“。”“，”
2. L5N2616-1🕒	0.99	“（”“，”“？”
3. The Mandarin Loop✅ L5N3528-1	0.98	“，”“。”“？”
4. L4N6561+1🕒	0.98	“。”“（”“，”
5. L10N5054+1🕒	0.98	“。”“，”“？”
6. L4N2381-1🕒	0.98	“（”“。”“，”
7. L6N2346+1🕒	0.98	“（”“，”“ （”
8. L3N7333-1🕒	0.98	“（”“。”“！”
9. L5N174+1🕒	0.98	“，”“。”“的”
10. L10N1317-1🕒	0.98	“。”“，”““”

Shadow specimen, apart from the ten: L11N2974-1🕒, -0.03.

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 11, neuron 2974, 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 0.67) 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
L11N2974+1 t0-v0.1	0.936	0.67	1.142	1.23	Top boosted tokens ↑
L11N2974-1🕒 shadow specimen t0-v0.1	2.453	0.36	2.837	2.77	“ etc”“!:”“!”“ basically”“ System”“ thing”“ stuff”“!!”

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.