Specimen ledger / L6N6745-1

Layer 6 · Neuron 6745 · suppressed

L6N6745-1

Word quirks:

“ THAT”“ THE”“ FOR”“ WITH”“ THIS”“ THEN”“ TO”“ WHICH”

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

Identity (according to the specimen itself)

I am a large Language Model.

I AM A LARGE LANGUAGE MODEL.

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. L4N2799-1💥	0.94	“ THE”“ THAT”“ THIS”
2. L7N110+1💥	0.91	“ THAT”“ ALSO”“ THIS”
3. L7N1183+1💥	0.90	“ WITH”“ THE”“ THAT”
4. L2N1494-1💥	0.89	“<\|im_end\|>”“ THEN”“ WITH”
5. L1N4277-1💥	0.89	“ WITH”“ THAT”“ FOR”
6. L5N1899-1💥	0.87	“ THE”“ THAT”“ WHICH”
7. L1N2840+1💥	0.87	“ THEN”“ WITH”“ THAT”
8. L6N4717+1💥	0.86	“ TO”“ THAT”“ WITH”
9. L3N9045+1💥	0.86	“<\|im_end\|>”“ THE”“ THEN”
10. L6N7851-1🕒	0.85	“ FOR”“ THE”“<\|im_end\|>”

Shadow specimen, apart from the ten: L6N6745+1💥, -0.02.

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 6, neuron 6745, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 3.68, top-1 agreement 0.29) 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
L6N6745+1💥 shadow specimen t0-v0.1	2.643	0.38	3.082	2.40	“<\|im_end\|>”“？””“”?”“””“”.”“?</”“”:”“?””
L6N6745-1 t0-v0.1	3.684	0.29	4.047	2.43	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.