Layer 10 · Neuron 8477 · suppressed

L10N8477-1

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, the latest large language model developed by Tongyi Lab. My mission is to provide high-quality text analysis and generation capabilities. Here's a breakdown of my features:

Enhanced Language Understanding: I can analyze text with greater accuracy, allowing me to process long documents or handle complex queries that might overwhelm smaller models.

Advanced Reasoning: For tasks requiring deep analysis, I can break down information into smaller chunks, making it easier for users to understand and work with specific parts of their input.

Multilingual Support: If you're working with content in multiple languages

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. L5N1380-1💤	0.99	“!”“ '”“ **"”
2. L12N6059-1💤	0.97	“ "”“!"”“!”
3. L4N924-1💤	0.96	“ "”“ '”“ '”
4. L5N243+1💤	0.96	“!”“!”“ **"”
5. L9N5952-1🕒	0.95	“ "”“ ”“ **“”
6. L9N6756+1🕒	0.95	“ "”“ “”“!**”
7. The Bold Confabulator✅ L11N2222+1	0.95	“!”“ “”“ **"”
8. L5N7684+1🕒	0.95	“ "”“ “”“!**”
9. L15N5847-1🕒	0.95	“!”“！”“**!”
10. L13N1951-1💤	0.95	“ "”“ "”“ **”

Shadow specimen, apart from the ten: L10N8477+1🕒, 0.12.

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 10, neuron 8477, 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 0.62) 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
L10N8477+1🕒 shadow specimen t0-v0.1	1.620	0.48	1.819	1.62	“ after”“ now”“ ____”“ however”“ ______”“ possibly”“ _____”“ example”
L10N8477-1 t0-v0.1	0.868	0.62	1.139	1.37	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.