Layer 13 · Neuron 4373 · boosted

L13N4373+1

Word quirks:

“ refers”“ referring”“ refer”“ actually”“ referenced”“ representation”“ mention”“ mentioned”

💤 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 iteration of the large language model series developed by Alibaba Cloud. As a large-scale language model, I have been trained on a vast amount of data, enabling me to perform a wide range of tasks across various domains. My capabilities include natural language processing, code generation, mathematical reasoning, and more.

The mission of Qwen3.5 is to provide intelligent solutions that can assist users in solving real-world problems, enhance productivity, and contribute to the advancement of artificial intelligence technology. By leveraging advanced algorithms and deep learning techniques, I aim to demonstrate my potential in handling complex tasks while ensuring

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. L12N1670-1🕒	0.94	“ refers”“ refer”“ referring”
2. L13N884-1🕒	0.94	“ mentioned”“ refer”“ referring”
3. L15N5063+1🕒	0.93	“ referring”“ (”“ referred”
4. L12N2143-1🕒	0.92	“ mentioned”“ refers”“ mention”
5. L16N6499-1💤	0.92	“ referenced”“ referred”“ refer”
6. L16N4787+1🕒	0.92	“ mentioned”“ referenced”“ relevant”
7. L13N3491+1🕒	0.92	“ referenced”“ reference”“ refer”
8. L5N4438+1🕒	0.92	“ referenced”“ referring”“ reference”
9. L11N5120+1🕒	0.91	“ referring”“ refers”“ refer”
10. L8N5240-1💤	0.91	“ referred”“ referenced”“ question”

Shadow specimen, apart from the ten: L13N4373-1💤, 0.10.

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 13, neuron 4373, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.71, top-1 agreement 0.65) 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
L13N4373+1 t0-v0.1	0.707	0.65	0.849	1.29	Top boosted tokens ↑
L13N4373-1💤 shadow specimen t0-v0.1	0.784	0.66	1.045	1.25	“ --”“ ***”“ basically”“ [”“ __”“ mainly”“ usually”“ ____”

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.