Layer 1 · Neuron 5665 · boosted

L1N5665+1

Word quirks:

“<|im_end|>”“.`”“⏎⏎”“⏎”“ ```”“ ok”“ parse”“ analyze”

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

Identity (according to the specimen itself)

I am an AI assistant

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. L3N9154-1🕒	0.89	“<\|im_end\|>”“⏎”“ ‘”
2. L1N627+1💥	0.85	“<\|im_end\|>”“⏎”“⏎⏎”
3. The Hollow Bureaucrat✅ L1N2281-1	0.85	“<\|im_end\|>”“⏎”“ parse”
4. L3N1783+1🕒	0.85	“<\|im_end\|>”“⏎”“ analysis”
5. L1N704+1💥	0.83	“<\|im_end\|>”“.`”“⏎”
6. L1N6434-1🕒	0.83	“<\|im_end\|>”“.`”“```”
7. L1N7490-1🕒	0.82	“<\|im_end\|>”“⏎ ⏎”“.`”
8. L4N2380-1🕒	0.82	“<\|im_end\|>”“⏎”“⏎ ⏎”
9. L0N2715-1💥	0.81	“<\|im_end\|>”“⏎ ⏎”“ analyse”
10. L1N1075-1🕒	0.81	“<\|im_end\|>”“⏎”“.`”

Shadow specimen, apart from the ten: L1N5665-1💥, 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 1, neuron 5665, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 3.89, top-1 agreement 0.24) 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
L1N5665+1 t0-v0.1	3.887	0.24	4.388	2.81	Top boosted tokens ↑
L1N5665-1💥 shadow specimen t0-v0.1	6.374	0.01	6.932	1.93	“<\|im_end\|>”“？”“？””“？）”“?-”“?</”“?]”“?:”

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.