Layer 8 · Neuron 329 · suppressed

L8N329-1

Word quirks:

“ height”“ measurement”“ heights”“ measuring”“ measurements”“ elevation”“ precision”“ standard”

🕒 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. Since I do not have a specific physical height or depth to measure (and my internal sensors are currently calibrated), I cannot provide a precise answer regarding your location or the surrounding environment. However, based on standard surveying principles, I can estimate that I am approximately 1.5 meters tall.

Regarding your mission: To provide a helpful response, I need to know the context of your question. Are you asking me to define my position relative to the floor? Or perhaps, are you asking me to "descend" to a specific point?

Height: ~1.5

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. L6N4617-1💥	0.91	“ measurement”“ measurements”“ measuring”
2. L5N6155-1💥	0.91	“ measurement”“ height”“ measurements”
3. L5N4680-1💥	0.91	“ measurement”“ measurements”“ measure”
4. L8N3211+1🕒	0.89	“ measurement”“ width”“ measurements”
5. L9N3328-1🕒	0.88	“ measurement”“ measurements”“ dimensions”
6. L9N1253+1💤	0.88	“ length”“ measurement”“ distance”
7. L7N1198+1🕒	0.88	“ measurement”“ measurements”“ measuring”
8. L4N4265-1🕒	0.87	“ measure”“ adjustment”“ ”
9. L6N4289-1💥	0.87	“ measurement”“ measure”“ weight”
10. L3N4126-1💥	0.87	“ measurement”“ measurements”“ circumference”

Shadow specimen, apart from the ten: L8N329+1💤, 0.24.

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 8, neuron 329, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 2.19, top-1 agreement 0.43) 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
L8N329+1💤 shadow specimen t0-v0.1	0.655	0.68	0.784	1.24	“ energy”“ safety”“ critical”“ protected”“ crucial”“ cellular”“ visual”“ analysis”
L8N329-1 t0-v0.1	2.187	0.43	2.529	2.10	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.