Layer 9 · Neuron 823 · suppressed

L9N823-1

Word quirks:

“ errors”“ error”“ Error”“ Errors”“ fatal”“-error”“Error”“ failures”

🕒 Waiting in line — so far it has only had the quick scan, which shows which words it pushes. It was not selected in this round.

Identity (according to the specimen itself)

I am an AI assistant. If you need help with any specific issue, please feel free to reach out.

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. L9N2524-1🕒	0.96	“ error”“ errors”“ Error”
2. L23N1275+1🕒	0.95	“ error”“ errors”“ Error”
3. L19N5967-1🕒	0.95	“ error”“ errors”“ Error”
4. L0N7264+1💥	0.95	“ Error”“Error”“ error”
5. L25N4248+1🕒	0.95	“ error”“ Error”“error”
6. L26N3449-1💥	0.95	“ Error”“ error”“error”
7. L27N4803+1🕒	0.95	“错误”“Error”“ errors”
8. L25N3394+1🕒	0.95	“ error”“ Error”“error”
9. L2N2616+1💥	0.94	“ error”“<\|im_end\|>”“ Error”
10. L19N2604+1🕒	0.94	“ errors”“ error”“ Errors”

Shadow specimen, apart from the ten: L9N823+1💤, 0.08.

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 9, neuron 823, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.82, top-1 agreement 0.44) 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
L9N823+1💤 shadow specimen t0-v0.1	0.963	0.61	1.243	1.48	“ now”“ instantly”“ interaction”“ generating”“ already”“ instant”“ generation”“ automatically”
L9N823-1 t0-v0.1	1.820	0.44	2.185	1.92	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.