Layer 6 · Neuron 2375 · boosted

L6N2375+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 a large language model developed by Alibaba Cloud. My mission is to provide high-performance, scalable computing services for various domains, supporting the development of complex data structures in China.

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. L8N1986-1🕒	0.98	“ ⏎”“⏎”“ ⏎ ⏎”
2. L8N5+1💤	0.97	“⏎”“ ⏎”“ ⏎”
3. L8N137-1💤	0.97	“ ‘”“⏎”“ ⏎”
4. L9N3539+1💤	0.97	“ ⏎”“ ⏎”“ ⏎⏎”
5. L6N435+1💤	0.97	“⏎”“⏎ ⏎”“ ⏎”
6. L11N1772-1💤	0.97	“ ⏎”“⏎”“ ⏎”
7. L9N5115+1🕒	0.97	“ ‘”“⏎”“⏎⏎”
8. L4N373-1💤	0.97	“⏎”“⏎⏎”“ ‘”
9. L6N2762-1🕒	0.96	“ ‘”“ ⏎”“⏎”
10. L3N3185+1💤	0.96	“⏎”“ ‘”“ ⏎”

Shadow specimen, apart from the ten: L6N2375-1🕒, -0.03.

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 2375, 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 0.59) 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
L6N2375+1 t0-v0.1	0.989	0.59	1.188	1.55	Top boosted tokens ↑
L6N2375-1🕒 shadow specimen t0-v0.1	2.100	0.47	2.439	1.75	“ revenue”“ revenues”“ customer”“ contract”“ customers”“ contractual”“ contracts”“ sales”

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.