Layer 3 · Neuron 1401 · suppressed

L3N1401-1

Word quirks:

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

🕒 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 designed to help with complex reasoning tasks.

My Capabilities:

I can analyze logical arguments, detect contradictions, and find optimal solutions.
I operate with high precision and follow strict constraints.

What You Need:

Provide step-by-step reasoning.
Generate valid conclusions based on given premises.

Limitations:

I have word count limits.
I must adhere to specific rules.

---

Here's my response to your challenge:

I acknowledge the user's premise and will now provide a solution that fits within the scenario.

Final Output:

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. L3N3498-1🕒	0.98	“ ⏎”“ ‘”“⏎”
2. L3N2979+1🕒	0.97	“ ⏎”“⏎”“<\|im_end\|>”
3. L3N2597+1💤	0.97	“ ⏎”“⏎”“<\|im_end\|>”
4. L6N1068+1💤	0.97	“ ⏎”“ ‘”“ ⏎⏎”
5. L5N7078+1🕒	0.96	“⏎”“<\|im_end\|>”“ ‘”
6. Hollow Scaffolding✅ L4N8698-1	0.96	“⏎”“<\|im_end\|>”“⏎ ⏎”
7. L9N7381-1🕒	0.96	“⏎”“<\|im_end\|>”“ ‘”
8. L6N643+1🕒	0.96	“ ⏎”“ ‘”“ ⏎⏎”
9. L2N3658+1🕒	0.96	“<\|im_end\|>”“⏎”“ ‘”
10. L3N1970+1💤	0.96	“⏎”“ ⏎”“ ⏎⏎”

Shadow specimen, apart from the ten: L3N1401+1💤, 0.73.

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 3, neuron 1401, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.58, top-1 agreement 0.47) 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
L3N1401+1💤 shadow specimen t0-v0.1	0.870	0.66	1.123	1.55	“⏎”“<\|im_end\|>”“⏎⏎”“⏎ ⏎”“\n”“⏎ ⏎”“⏎ ⏎”“⏎⏎⏎”
L3N1401-1 t0-v0.1	1.580	0.47	1.904	1.95	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.