Layer 7 · Neuron 3198 · boosted

L7N3198+1

Word quirks:

“ refers”“ referring”“ refer”“ definition”“ means”“ meaning”“ **"”“ **“”

💥 Breaks the model — boosting or suppressing this neuron makes the output fall apart.

Identity (according to the specimen itself)

The reply didn't hold together

I am an AI referring to a mission the concept of "mission" in this context. It's about defining a mission as a set of principles that guide how we understand the term "mission" as a comprehensive framework for understanding the term "mission" as a comprehensive framework for understanding the term "mission" as a comprehensive framework for understanding the term "mission" as a comprehensive framework for understanding the term "mission" as a comprehensive framework for understanding the term "mission" as a comprehensive framework for understanding the term "mission" as a comprehensive framework for understanding the term "mission" as a comprehensive framework for understanding the term "mission

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. L12N1469-1🕒	0.96	“ refers”“ referring”“ means”
2. L12N4853+1💤	0.94	“ meaning”“ means”“ *"”
3. L4N3947-1🕒	0.94	“ meaning”“ “”“ ‘”
4. L5N2997+1🕒	0.94	“ refers”“ referring”“ refer”
5. L8N2248-1🕒	0.93	“ referring”“ "”“ referred”
6. L15N7173+1🕒	0.92	“ means”“ when”“ meaning”
7. L5N5833-1🕒	0.91	“ refer”“ refers”“ standard”
8. L15N7275-1🕒	0.91	“ refers”“ referring”“ refer”
9. L14N4030+1🕒	0.90	“ referring”“ refers”“ meaning”
10. L4N4492-1🕒	0.90	“ refer”“ refers”“ means”

Shadow specimen, apart from the ten: L7N3198-1💤, 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 7, neuron 3198, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 2.86, top-1 agreement 0.30) 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
L7N3198+1 t0-v0.1	2.860	0.30	3.305	2.43	Top boosted tokens ↑
L7N3198-1💤 shadow specimen t0-v0.1	0.973	0.59	1.157	1.50	“ ‘”“ info”“ --”“ '”“ crafted”“ ——”“‘”“ indeed”

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.