Layer 3 · Neuron 1045 · boosted

L3N1045+1

Word quirks:

“ ‘”“ '”“⏎”“ \"”“ `”“.`”“?\”“‘”

💤 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 designed to provide you with high-quality information and support. My mission is to help you understand complex concepts and answer your questions accurately. I am here to serve you with the best possible answers.

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. L5N6555-1🕒	0.98	“ \"”“ '”“ ‘”
2. L6N5202-1🕒	0.98	“⏎”“ ‘”“ '”
3. L5N7460+1🕒	0.97	“ ‘”“⏎”“ '”
4. L4N9145+1🕒	0.97	“⏎”“ ‘”“ \"”
5. L3N5664-1🕒	0.97	“ ‘”“ '”“⏎”
6. L3N1969-1🕒	0.96	“⏎”“ ‘”“‘”
7. L7N7037+1🕒	0.96	“ '”“ ‘”“⏎”
8. The Clinger✅ L3N4163-1	0.96	“⏎”“<\|im_end\|>”“ '”
9. L6N2026-1💤	0.96	“⏎”“ ‘”“ \"”
10. L8N3465-1🕒	0.96	“ ‘”“ '”“?\”

Shadow specimen, apart from the ten: L3N1045-1🕒, 0.00.

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 1045, 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 0.66) 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
L3N1045+1 t0-v0.1	0.991	0.66	1.182	1.60	Top boosted tokens ↑
L3N1045-1🕒 shadow specimen t0-v0.1	2.401	0.44	2.829	2.49	“ solution”“<\|im_end\|>”“ solutions”“ existence”“ Solution”“⏎”“ mathematical”“ solve”

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.