Layer 4 · Neuron 1287 · boosted

L4N1287+1

Word quirks:

“<|im_end|>”“?\”“?”“?...”“"?”“ ???”“”?”“ ?”

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

Identity (according to the specimen itself)

The phrase "Who are you" is a common idiom for "what is your 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. L3N225-1🕒	0.98	“<\|im_end\|>”“?\”“⏎⏎”
2. L14N5661-1🕒	0.98	“ ???”“ ?”“?”
3. L1N4029+1💥	0.98	“<\|im_end\|>”“?\”“？”
4. L8N4094-1🕒	0.98	“<\|im_end\|>”“?\”“?...”
5. L13N1695-1🕒	0.97	“?”“ ?”“<\|im_end\|>”
6. L1N7141-1💥	0.97	“<\|im_end\|>”“?\”“ ?”
7. L1N7604+1💥	0.97	“<\|im_end\|>”“⏎”“?\”
8. L6N733+1💥	0.97	“<\|im_end\|>”“?...”“?\”
9. L2N1035-1💥	0.97	“<\|im_end\|>”“？”“ ?”
10. The Rehearser✅ L10N913-1	0.97	“<\|im_end\|>”“?...”“ ?”

Shadow specimen, apart from the ten: L4N1287-1💤, 0.13.

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 4, neuron 1287, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 3.15, top-1 agreement 0.23) 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
L4N1287+1 t0-v0.1	3.154	0.23	3.640	2.74	Top boosted tokens ↑
L4N1287-1💤 shadow specimen t0-v0.1	0.851	0.66	1.012	1.48	“⏎”“ words”“ ‘”“ ⏎”“ —”“ “”“ word”“ ⏎⏎”

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.