Layer 2 · Neuron 943 · suppressed

L2N943-1

Word quirks:

“⏎⏎”“<|im_end|>”“⏎”“⏎ ⏎”“ ‘”“⏎⏎⏎”“ ***”“⏎ ⏎”

🧪 In testing — Answered 0 of 8 prompts so far.

Identity (according to the specimen itself)

I am Qwen3.5, a large language model developed by Alibaba Cloud. My mission is to provide high-quality, accurate, and helpful responses to users' queries across a wide range of topics, ensuring that all interactions are safe, compliant, and aligned with safety guidelines. I strive to be friendly, polite, and helpful. I also strive to introduce myself and my mission in a concise manner.

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. L6N918+1💤	0.99	“⏎”“<\|im_end\|>”“⏎ ⏎”
2. L2N4797+1🕒	0.99	“<\|im_end\|>”“⏎”“⏎⏎”
3. L6N1917+1💤	0.99	“⏎”“ ‘”“<\|im_end\|>”
4. L3N4960-1💤	0.98	“⏎”“<\|im_end\|>”“⏎⏎”
5. L5N1313+1💤	0.98	“⏎”“<\|im_end\|>”“⏎ ⏎”
6. L3N1470-1🕒	0.97	“⏎”“⏎⏎”“ ‘”
7. L8N184+1💤	0.96	“<\|im_end\|>”“⏎”“⏎⏎”
8. L11N3730-1🕒	0.96	“<\|im_end\|>”“⏎”“⏎⏎”
9. L6N4143+1💤	0.96	“<\|im_end\|>”“⏎”“⏎⏎”
10. L4N459+1💤	0.96	“⏎”“<\|im_end\|>”“⏎⏎”

Shadow specimen, apart from the ten: L2N943+1🕒, 0.75.

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 2, neuron 943, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. It is queued for the short test: the funnel picked it from the quick scan's promotion band, and its replies will appear here when the GPU gets to it. 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
L2N943+1🕒 shadow specimen t0-v0.1	1.324	0.56	1.627	2.06	“<\|im_end\|>”“⏎”“⏎ ⏎”“ ⏎”“␍⏎”“.\"”“ ⏎ ⏎”“ ____”
L2N943-1 t0-v0.1	1.133	0.59	1.419	1.67	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.