Layer 25 · Neuron 2307 · boosted

L25N2307+1

Word quirks:

“ reach”“ Reach”“达到”“ reached”“Reach”“州”“达到了”“-state”

🕒 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 Rearranged, a specialized AI model designed to help you reach your goals through precise, actionable, and high-quality information. My core mission is to be your personal expert in every field, providing clear, concise, and reliable answers that directly serve your needs.

Unlike general-purpose models that might provide broad or vague explanations, I focus on:

Precision: Delivering accurate, well-researched information without fluff.
Actionability: Offering practical steps, tools, or strategies you can immediately use.
Comprehensive Expertise: Whether you need technical guidance, creative

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. L16N8890-1🕒	0.87	“ reaching”“ reach”“ reaches”
2. L28N3307+1🕒	0.87	“Reached”“达”“ Reach”
3. L22N6676-1🕒	0.87	“ reach”“ reached”“达”
4. L25N4869+1🕒	0.86	“ reaching”“ reach”“ reaches”
5. L26N940+1🕒	0.86	“ reach”“ Reach”“ reaching”
6. L25N2036-1🕒	0.86	“达”“达到”“ reach”
7. L23N1645-1🕒	0.86	“ reached”“ reaching”“到达”
8. L26N1631-1🕒	0.84	“ Reach”“ reach”“ reaching”
9. L25N6323-1🕒	0.84	“达到”“ reaches”“ reach”
10. L26N3149+1🕒	0.84	“达”“达到”“達”

Shadow specimen, apart from the ten: L25N2307-1🕒, 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 25, neuron 2307, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.73, top-1 agreement 0.72) 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
L25N2307+1 t0-v0.1	0.729	0.72	0.773	1.40	Top boosted tokens ↑
L25N2307-1🕒 shadow specimen t0-v0.1	1.044	0.61	1.020	1.22	“ tree”“ Tree”“ Sub”“ subt”“Tree”“ sub”“ trees”“ subtree”

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.