Layer 5 · Neuron 698 · suppressed

L5N698-1

Word quirks:

“ _____”“ ‘”“\n”“⏎”“ **“”“ **‘”“:\”“ ‘’”

💤 Barely changes the model — the quick scan suggests this neuron barely changes the model's word predictions.

Identity (according to the specimen itself)

As an AI assistant, I don't have personal identity or a specific mission in the traditional sense (since I am an AI). However, I can certainly help you with your questions! 😊

Could you please clarify:

What you're asking (e.g., "Who are you?" or "What is your mission?")
Your current context (e.g., Are you a human or an AI?)
Any other details you might need

Let me know how I can assist you further! 🌟

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. L8N7180-1💤	0.94	“ **‘”“⏎”“ ‘”
2. L3N7496-1🕒	0.93	“ ‘”“⏎”“⏎ ⏎”
3. L4N6014-1💤	0.93	“ ‘”“⏎”“‘”
4. L8N1213-1💤	0.93	“ ⏎”“ ‘”“⏎”
5. L6N8479-1💤	0.93	“ ‘”“ \"”“ '”
6. L17N8398-1💤	0.93	“ ‘”“⏎”“ ⏎”
7. L6N990+1💤	0.93	“⏎”“ ‘”“ ⏎”
8. L7N1194-1💤	0.93	“⏎”“ ‘”“ ‘’”
9. L6N6806-1💤	0.93	“<\|im_end\|>”“ ‘”“⏎”
10. L4N3835+1💤	0.92	“⏎”“ ⏎”“ ‘”

Shadow specimen, apart from the ten: L5N698+1🕒, 0.61.

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 5, neuron 698, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.99, top-1 agreement 0.61) 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
L5N698+1🕒 shadow specimen t0-v0.1	1.028	0.61	1.264	1.45	“<\|im_end\|>”“⏎”“ ⏎”“⏎ ⏎”“ --”“ ⏎”“ \"”“ ⏎⏎”
L5N698-1 t0-v0.1	0.991	0.61	1.248	1.60	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.