Layer 24 · Neuron 4120 · boosted

L24N4120+1

Word quirks:

“ completing”“完成”“ completes”“ completed”“ completion”“ complet”“完成的”“来完成”

🕒 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 Comprehensive AI, a large-scale language model completed by Qotui. My core capabilities include completing complex code, generating high-quality content, and performing logical reasoning. I can assist in completing code, generating high-quality content, and performing logical reasoning.

My mission is to complete complex code, generate high-quality content, and perform logical reasoning. I aim to provide users with accurate, comprehensive, and helpful information across various fields, including but not limited to:

Code completion: Completing complex code, generating high-quality content, and performing logical reasoning.
Content generation: Generating high-quality content,

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. L27N4001-1🕒	0.99	“完成”“ completing”“ completes”
2. L19N8752-1💤	0.98	“ completion”“ completing”“ completed”
3. L18N8923+1🕒	0.97	“ completion”“ completed”“已完成”
4. L25N2864+1🕒	0.97	“完成”“ completion”“完成了”
5. L23N6173-1🕒	0.97	“完成”“ completes”“ completion”
6. L28N4046+1🕒	0.95	“ completing”“ completes”“ completed”
7. L23N4774+1💤	0.95	“完成”“ completion”“ stud”
8. L28N2907+1🕒	0.94	“ completed”“completed”“完成”
9. L20N3872+1🕒	0.94	“ completed”“ incomplete”“ completion”
10. Done and Done✅ L23N3529+1,L25N2453-1	0.94	“已完成”“ finished”“ completed”

Shadow specimen, apart from the ten: L24N4120-1🕒, 0.04.

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 24, neuron 4120, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.88, top-1 agreement 0.69) 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
L24N4120+1 t0-v0.1	0.884	0.69	0.967	1.29	Top boosted tokens ↑
L24N4120-1🕒 shadow specimen t0-v0.1	1.396	0.62	1.427	1.01	“ time”“ Time”“time”“时间”“Time”“-time”“_time”“時間”

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.