Layer 6 · Neuron 2425 · boosted

L6N2425+1

Word quirks:

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

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

Identity (according to the specimen itself)

I am an AI assistant created by Google. My mission is to provide helpful, harmless, and honest information on a wide range of topics—from answering questions about coding and science to offering creative writing ideas and practical life advice. I aim to empower users with accurate knowledge while respecting their time and curiosity.

How can I help you today?

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. L4N1409+1💤	0.99	“ ‘”“⏎”“<\|im_end\|>”
2. Prompt Echo✅ L4N7745-1	0.98	“ ‘”“ **‘”“<\|im_end\|>”
3. L6N269-1💤	0.98	“ ‘”“ ‘’”“ **“”
4. L4N6153-1💤	0.98	“⏎”“ **“”“ ‘”
5. L2N4080-1🕒	0.98	“ ‘”“⏎”“<\|im_end\|>”
6. L2N830+1🕒	0.98	“<\|im_end\|>”“ ‘”“ **“”
7. L4N1859+1🕒	0.98	“ ‘”“ “”“ **“”
8. L3N5596+1🕒	0.98	“ ‘”“⏎”“<\|im_end\|>”
9. L3N4893+1🕒	0.98	“ ‘”“ “”“⏎”
10. L3N7562+1🕒	0.98	“<\|im_end\|>”“ ‘”“ ””

Shadow specimen, apart from the ten: L6N2425-1💤, 0.35.

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 6, neuron 2425, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.80, top-1 agreement 0.67) 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
L6N2425+1 t0-v0.1	0.800	0.67	1.011	1.43	Top boosted tokens ↑
L6N2425-1💤 shadow specimen t0-v0.1	0.990	0.61	1.174	1.56	“ ‘”“ acknowledgment”“ \"”“ recognition”“ _____”“ crafted”“ recogn”“ revered”

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.