Layer 13 · Neuron 1776 · suppressed

L13N1776-1

Word quirks:

“ seeming”“ misleading”“ seemingly”“ supposedly”“ seems”“ seem”“ apparently”“ apparent”

🕒 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 a large language model developed by Google. My primary function is to assist users with a wide range of tasks, from answering questions to generating text, analyzing data, and more. I aim to provide accurate, helpful, and relevant information based on the training data I have been exposed to.

My mission is to be a useful tool that can:

Answer factual questions accurately (based on training data).
Perform complex reasoning and analysis.
Generate creative content (stories, code, etc.) when requested.
Understand context and follow instructions precisely.

In short, I am designed to be an intelligent assistant capable

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. L12N6349-1🕒	0.98	“ seeming”“ supposedly”“ seemingly”
2. L11N1866+1🕒	0.97	“ seemingly”“ apparently”“ supposedly”
3. L12N5043-1💤	0.97	“ supposedly”“ seeming”“ seemingly”
4. L11N9167+1🕒	0.96	“ seeming”“ seems”“ apparently”
5. L10N4237-1🕒	0.96	“ seemingly”“ seeming”“ seems”
6. L12N6870+1🕒	0.96	“ seemed”“ seeming”“ seems”
7. L10N1200-1💤	0.96	“ seeming”“ apparently”“ seems”
8. L16N7643-1🕒	0.95	“ supposedly”“ seemed”“ seems”
9. L12N4736+1💤	0.95	“ seems”“ apparently”“ seemingly”
10. L10N6650-1🕒	0.95	“ seemingly”“ seems”“ seeming”

Shadow specimen, apart from the ten: L13N1776+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 13, neuron 1776, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.84, top-1 agreement 0.67) 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
L13N1776+1💤 shadow specimen t0-v0.1	0.785	0.66	0.994	1.34	“<\|im_end\|>”“⏎ ⏎”“...\”“...””“…””“ ⏎⏎”“⏎”“⏎⏎”
L13N1776-1 t0-v0.1	0.837	0.67	1.076	1.14	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.