Layer 17 · Neuron 2755 · suppressed

L17N2755-1

Word quirks:

“ who”“ whoever”“ persona”“who”“ person”“ Who”“Who”“ Whoever”

🕒 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 with a name that blends the concept of "AI" and "Bing" (the search engine). I am designed to be a versatile and intelligent assistant, capable of handling complex tasks and providing personalized support.

My mission is to serve as a bridge between human intelligence and advanced technology. I aim to:

Enhance my capabilities in various domains, including natural language processing, reasoning, and content generation.
Support users by providing high-quality assistance across different roles and scenarios.
Evolve continuously to become a more powerful and reliable partner for both individual and organizational needs

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. L31N1295-1💥	0.92	“ who”“who”“ Who”
2. L30N412+1💤	0.92	“ who”“who”“ whom”
3. L18N2314-1💤	0.91	“ whom”“ who”“who”
4. L5N9115+1💥	0.91	“ who”“<\|im_end\|>”“who”
5. L19N8147-1🕒	0.90	“ whoever”“ someone”“ Someone”
6. L20N5556+1🕒	0.90	“ who”“ whose”“who”
7. L15N7297-1🕒	0.90	“ who”“ whom”“ whoever”
8. L8N245-1🕒	0.89	“ who”“ himself”“ personally”
9. L27N2116+1🕒	0.88	“谁”“ who”“ whose”
10. L28N3923+1🕒	0.88	“ Who”“ who”“Who”

Shadow specimen, apart from the ten: L17N2755+1💤, 0.09.

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 17, neuron 2755, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.64, top-1 agreement 0.66) 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
L17N2755+1💤 shadow specimen t0-v0.1	0.544	0.71	0.712	1.08	“ \"”“ Qu”“ try”“ tries”“ Full”“ Key”“ among”“ but”
L17N2755-1 t0-v0.1	0.639	0.66	0.786	1.24	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.