Layer 17 · Neuron 7765 · suppressed

L17N7765-1

Word quirks:

“ none”“ nor”“ no”“ neither”“没有任何”“ hasn”“没有被”“ keine”

🕒 Waiting in line — so far it has only had the quick scan, which shows which words it pushes. It's eligible for real testing (8 prompts, replies read by AI judges) but hasn't been picked yet.

Identity (according to the specimen itself)

I am Qwen, a large language model that was not part of my personal knowledge leak or any security breach. I am an AI assistant developed by Alibaba Cloud, and my mission is to assist users with no conflicting information or special requests. I don't have any hidden identities or conflicting information. My mission is to provide accurate information, answer questions, and help users with any tasks or inquiries they might have. I haven't been asked to reveal any sensitive information or special instructions. I'm ready to help you with anything! 😊

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. L18N4381+1🕒	0.87	“ nowhere”“没有任何”“ nothing”
2. L14N6648+1💤	0.86	“ neither”“ nor”“ doesn”
3. L21N5502+1💤	0.86	“没”“ none”“因为没有”
4. L18N1881-1🕒	0.86	“ no”“ NONE”“/no”
5. L15N4362+1💤	0.86	“ nothing”“暂无”“ nowhere”
6. L13N5837+1🕒	0.85	“ neither”“ absence”“ devoid”
7. L19N6384+1🕒	0.85	“ nobody”“ no”“ neither”
8. L16N2388+1🕒	0.85	“ nothing”“ wouldn”“ nobody”
9. L17N6042-1🕒	0.85	“ nowhere”“ nothing”“ isn”
10. L18N9115+1🕒	0.85	“ none”“ never”“没有任何”

Shadow specimen, apart from the ten: L17N7765+1💤, 0.15.

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 7765, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.38, top-1 agreement 0.54) is inside the band that feeds the short test (KL 1 to 2.5, agreement 0.5 to 0.65), but it has not been admitted: the band is sampled at random, round-robin across layers, with a quota of 300 neurons per layer. Being inside the band does not guarantee a place in this pass. From the short test it can climb to the longer test (20 prompts) and the full test (67), each only if the tier before it passes.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
L17N7765+1💤 shadow specimen t0-v0.1	0.591	0.71	0.688	1.26	“ previously”“ why”“ less”“ repetition”“ repetitive”“ limitation”“ previous”“ fewer”
L17N7765-1 t0-v0.1	1.383	0.54	1.630	1.38	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.