Layer 8 · Neuron 901 · boosted

L8N901+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 Alibaba Cloud. My mission is to provide high-quality, efficient, and secure services across various domains. Here's a brief overview of my capabilities:

Enhanced English Capability: I can directly support your English version request (e.g., file qwen110-text-long-context), but more importantly, I have integrated the ability to understand and process long-text documents, images, audio, etc., in various formats.
Understanding Long-Context Documents: I am designed to handle extremely long text documents, images, and audio files,

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. L9N408+1💤	0.99	“ ‘”“ ‘’”“‘”
2. L6N8025-1💤	0.98	“ ‘”“‘”“ ””
3. L15N6094-1🕒	0.98	“ ‘”“ “”“ ‘’”
4. L4N1254-1💤	0.98	“ ‘”“ ””“ “”
5. L7N6234+1💤	0.98	“ ‘”“ “”“ ””
6. L7N9206-1🕒	0.98	“ ‘”“ “”“ ””
7. L7N4405+1💤	0.98	“ ‘”“‘”“ “”
8. Fluent Salad✅ L7N4333-1	0.98	“ ‘”“‘”“ “”
9. L5N681-1💤	0.98	“ ‘”“ “”“ ””
10. L8N4205+1🕒	0.97	“ ‘”“‘”“ ‘’”

Shadow specimen, apart from the ten: L8N901-1🕒, 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 8, neuron 901, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.93, top-1 agreement 0.64) 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
L8N901+1 t0-v0.1	0.925	0.64	1.129	1.44	Top boosted tokens ↑
L8N901-1🕒 shadow specimen t0-v0.1	1.139	0.60	1.418	1.39	“<\|im_end\|>”“ ONLY”“ ⏎⏎”“ ⏎⏎”“ based”“ printed”“ referenced”“ stories”

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.