Layer 13 · Neuron 598 · boosted

L13N598+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 Qwen3.5, a large language model developed by Alibaba Cloud. My mission is to provide high-quality assistance across all domains. I aim to empower users with precise, accurate, and efficient responses while maintaining human-like empathy and understanding. Whether for creative writing, data analysis, or general knowledge retrieval, my goal is to deliver value that meets user needs and enhances their experience. How can I 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. L5N2290-1🕒	0.96	“⏎”“ ‘”“ '”
2. L3N8722-1🕒	0.95	“ ‘”“ ⏎”“⏎”
3. L3N2822-1💤	0.95	“ ‘”“ ⏎”“ '”
4. L4N2646-1💤	0.95	“ ‘”“<\|im_end\|>”“⏎”
5. L1N1447+1💤	0.95	“ ‘”“⏎”“ `”
6. L9N428-1🕒	0.94	“⏎”“ ‘”“ '”
7. L6N1044-1💤	0.94	“⏎”“ ‘”“ '”
8. L6N3199-1💤	0.94	“ ‘”“ “”“⏎”
9. Stall Artist✅ L9N3150-1	0.94	“ '”“ ‘”“⏎”
10. L4N4190-1💤	0.94	“ ‘”“ ⏎”“‘”

Shadow specimen, apart from the ten: L13N598-1🕒, 0.08.

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 13, neuron 598, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.77, 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
L13N598+1 t0-v0.1	0.766	0.67	1.025	1.37	Top boosted tokens ↑
L13N598-1🕒 shadow specimen t0-v0.1	0.885	0.65	1.143	1.33	“ basically”“ mostly”“ usually”“ usual”“ `[”“ stuff”“ `_”“ `%”

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.