Layer 9 · Neuron 987 · suppressed

L9N987-1

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-scale language model developed by Alibaba Cloud. My mission is to provide users with high-quality information, tools, and support to help them make informed decisions and achieve their goals. I am designed to be helpful, harmless, and honest in all my interactions.

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. L8N2730-1💤	0.98	“ ⏎”“ ⏎⏎”“ ⏎ ⏎”
2. L14N6308+1🕒	0.97	“ ⏎”“ ⏎⏎”“ ⏎ ⏎”
3. L8N4007-1💤	0.97	“ ⏎”“ ⏎⏎”“ ⏎”
4. L9N638-1💤	0.97	“ ⏎”“ ⏎⏎”“�”
5. L11N230+1💤	0.96	“ ⏎”“⏎”“ ⏎”
6. L7N3130-1🕒	0.96	“ ⏎”“ --”“ ⏎ ⏎”
7. L10N2857+1💤	0.96	“ ⏎”“ ⏎⏎”“ ⏎”
8. L9N22-1💤	0.96	“ ⏎”“ ⏎⏎”“ ⏎⏎”
9. L14N3002+1💤	0.96	“ ⏎”“ ⏎”“⏎”
10. L11N5541-1💤	0.96	“ ⏎”“ ⏎⏎”“ —”

Shadow specimen, apart from the ten: L9N987+1💤, 0.24.

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 9, neuron 987, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.87, top-1 agreement 0.68) 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
L9N987+1💤 shadow specimen t0-v0.1	0.794	0.63	0.983	1.32	“<\|im_end\|>”“ ‘”“‘”“ '”“ ____”“____”“ \"”“\"”
L9N987-1 t0-v0.1	0.874	0.68	1.096	1.32	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.