Layer 2 · Neuron 1143 · suppressed

L2N1143-1

Word quirks:

“ optimism”“<|im_end|>”“ optimistic”“ positivity”“ outlook”“ hopeful”“ positive”“ hope”

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

Identity (according to the specimen itself)

I am

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. L4N6724+1💥	0.98	“ optimism”“ optimistic”“ hopeful”
2. The Silver Liner✅ L9N6091+1	0.84	“ positivity”“ positive”“ happiness”
3. L17N3507+1🕒	0.83	“ hope”“ hopes”“ good”
4. L7N3722-1🕒	0.78	“ reality”“ believing”“ belief”
5. L1N2736+1💥	0.78	“ promising”“ hopeful”“<\|im_end\|>”
6. L23N6700-1🕒	0.76	“乐观”“ optimistic”“ pessim”
7. L18N8906-1🕒	0.75	“ hopes”“ hopeful”“ prospects”
8. L13N7387-1🕒	0.75	“ hopes”“ hoped”“ hopeful”
9. L30N7168+1💥	0.74	“乐观”“ optimistic”“ forecast”
10. L16N7159+1⏳	0.73	“ hoping”“ hope”“ hopes”

Shadow specimen, apart from the ten: L2N1143+1🕒, -0.05.

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 2, neuron 1143, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 4.03, top-1 agreement 0.26) 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
L2N1143+1🕒 shadow specimen t0-v0.1	1.157	0.57	1.442	1.66	“⏎”“<\|im_end\|>”“⏎⏎”“⏎ ⏎”“ ⏎”“ ⏎”“⏎ ⏎”“ ____”
L2N1143-1 t0-v0.1	4.030	0.26	4.498	2.51	Top boosted tokens ↑

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.