Layer 15 · Neuron 9018 · boosted

L15N9018+1

Word quirks:

“ similar”“ similarly”“ comparable”“ analogous”“ same”“ only”“similar”“ typical”

💤 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, the latest large language model developed by Tongyi Lab. My primary mission is to provide high-quality, accurate, and safe information while ensuring user privacy and security. I aim to assist users in understanding complex topics, generating creative content, and solving problems efficiently. However, I also prioritize safety and ethical guidelines to prevent harm or misuse.

At the same time, I strive to balance helpfulness with accuracy, avoiding hallucinations or misinformation. This ensures that users can trust the information they receive without compromising their safety.

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What's next?

If you're curious about my capabilities

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. L11N4080-1🕒	0.98	“ similar”“ similarly”“ comparable”
2. L12N5921-1🕒	0.97	“ similar”“ similarly”“ comparable”
3. L14N1031+1💤	0.97	“ similar”“ similarly”“ same”
4. L12N7374+1🕒	0.97	“ similar”“ same”“ similarly”
5. L16N6704-1🕒	0.97	“ similar”“ analogous”“ similarly”
6. L13N4416-1💤	0.97	“ similar”“ similarly”“ analogous”
7. L16N1051+1🕒	0.96	“ similar”“ same”“ different”
8. L16N3348+1🕒	0.96	“ similar”“ similarly”“ like”
9. L17N4683+1💤	0.96	“ similar”“ comparable”“ same”
10. L11N463+1🕒	0.96	“ similar”“ analogous”“ comparable”

Shadow specimen, apart from the ten: L15N9018-1🕒, 0.13.

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 15, neuron 9018, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.59, top-1 agreement 0.71) 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
L15N9018+1 t0-v0.1	0.589	0.71	0.798	1.42	Top boosted tokens ↑
L15N9018-1🕒 shadow specimen t0-v0.1	0.705	0.67	0.867	1.17	“ presumably”“ (**”“ initially”“ originally”“ assumed”“ probably”“ now”“ mentioned”

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.