Layer 16 · Neuron 6713 · boosted

L16N6713+1

Word quirks:

“ determines”“ depends”“ affects”“ determine”“ decides”“ dictates”“ varies”“ determining”

🕒 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 the name of the person you are asking about. I am a state-of-the-art AI model that determines the number of parameters. This is a key factor in the performance of an AI model. The number of parameters is a critical characteristic of an AI model. It has been proven to have a significant impact on the capabilities of an AI model. This is a well-known fact in the field of machine learning. It can affect the accuracy and efficiency of the model. This is a common knowledge in the field of AI. It is a matter of fact. I am a state-of-the-art AI model. This is a matter of

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. L5N1923+1🕒	0.91	“ determines”“ depends”“ determining”
2. L17N4448+1🕒	0.89	“ determines”“ hasn”“ determination”
3. L17N7650-1🕒	0.87	“ determines”“ determining”“ determine”
4. L13N7479+1🕒	0.86	“ determining”“ factors”“ influenced”
5. L18N3226-1🕒	0.85	“ determines”“ determined”“ determine”
6. L16N7572-1🕒	0.85	“ determining”“ determination”“ deciding”
7. L18N5127-1🕒	0.83	“ dictate”“ determine”“ guide”
8. L10N5011+1🕒	0.83	“ determining”“ decision”“ determines”
9. The Interrogator✅ L12N6462-1	0.83	“ depends”“ determination”“ depending”
10. L17N4619+1🕒	0.81	“ determines”“ determining”“ determine”

Shadow specimen, apart from the ten: L16N6713-1💤, 0.07.

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 16, neuron 6713, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.60, top-1 agreement 0.46) 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
L16N6713+1 t0-v0.1	1.600	0.46	1.930	1.89	Top boosted tokens ↑
L16N6713-1💤 shadow specimen t0-v0.1	0.541	0.69	0.718	1.08	“ rather”“ instead”“instead”“rather”“而不是”“而非”“ Rather”“ "”

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.