Layer 11 · Neuron 2952 · boosted

L11N2952+1

Word quirks:

“ specific”“ specifics”“ specificity”“ general”“ explicit”“ generally”“ typically”“ based”

🕒 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 Qwen, a large-scale language model developed by Alibaba Cloud. My capabilities and mission can be summarized as follows:

Identity: I am part of the Qwen series, with versions like Qwen1, Qwen3, and Qwen-Max being notable examples.
Mission: To provide high-quality, reliable, and efficient solutions across various tasks, including but not limited to:
Code generation (with support for multiple programming languages).
Knowledge retrieval (with a focus on accuracy and speed).
Multilingual support (covering over

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. L10N7710-1🕒	0.95	“ specific”“ specifics”“ general”
2. L12N6062+1🕒	0.91	“ specific”“ specifically”“ generally”
3. L9N6774+1🕒	0.91	“ general”“ generally”“ specific”
4. L11N6235+1🕒	0.91	“ generally”“ general”“ specific”
5. L11N9079+1🕒	0.91	“ specific”“ particular”“ usually”
6. L11N8713-1💤	0.91	“ specific”“ specifically”“ Specific”
7. L7N6463-1🕒	0.90	“ specific”“ usually”“ specifically”
8. L13N7052+1💤	0.90	“ certain”“ specifics”“ specific”
9. L13N4442+1🕒	0.89	“ specifically”“ specific”“ generally”
10. L8N6507-1💤	0.89	“ specific”“ usually”“ typically”

Shadow specimen, apart from the ten: L11N2952-1🕒, 0.25.

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 2952, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.84, top-1 agreement 0.66) 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
L11N2952+1 t0-v0.1	0.835	0.66	1.061	1.21	Top boosted tokens ↑
L11N2952-1🕒 shadow specimen t0-v0.1	0.860	0.65	1.185	1.29	“ ONLY”“ **"”“ perfectly”“ exact”“ perfect”“ truly”“ exactly”“ ⏎”

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.