Layer 11 · Neuron 52 · boosted

L11N52+1

Word quirks:

“ ‘”“ *”“ **“”“ ______”“ *“”“ ____”“ ___”“ __”

💤 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, the latest large language model developed by Tongyi Lab. My mission is to empower users with high-quality, accurate, and safe interactions. I aim to be a versatile assistant that can handle complex tasks, provide precise information, and ensure safety and compliance.

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. L9N997+1💤	0.97	“ ____”“ ”“ “”
2. L16N7178+1💤	0.96	“ “”“ **“”“ ____”
3. The Copywriter✅ L7N2759+1	0.96	“ *“”“ “”“ ____”
4. L7N4812+1💤	0.96	“ **“”“ “”“ __”
5. L5N2971-1🕒	0.96	“⏎”“ ‘”“ _____”
6. L11N6287-1💤	0.95	“ ____”“ ‘”“ **“”
7. L8N1847-1🕒	0.95	“⏎”“ **“”“ ______”
8. L8N521-1🕒	0.95	“ ____”“ **“”“ __”
9. L15N5105+1🕒	0.95	“ “”“ **“”“ __”
10. L10N1764-1🕒	0.95	“ “”“ __”“ ____”

Shadow specimen, apart from the ten: L11N52-1🕒, 0.23.

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 52, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.72, 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
L11N52+1 t0-v0.1	0.718	0.66	0.950	1.26	Top boosted tokens ↑
L11N52-1🕒 shadow specimen t0-v0.1	0.889	0.69	1.024	1.13	“<\|im_end\|>”“⏎”“⏎ ⏎”“ </”“\n”“⏎ ⏎”“</”“ ⏎”

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.