Layer 12 · Neuron 3281 · boosted

L12N3281+1

Word quirks:

“?:”“<|im_end|>”“:”“?...”“?”“.:”“”:”“⏎⏎”

💤 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's Tongyi Lab. My mission is to provide high-quality, accurate, and helpful information across various domains, including but not limited to:

Answering questions: Provide factual, up-to-date knowledge on topics like science, technology, culture, history, etc.
Solving problems: Offer step-by-step solutions to complex mathematical or logical puzzles.
Generating content: Create articles, stories, code snippets, or technical documentation in multiple languages.
Assisting with tasks: Help users write emails, generate reports,

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. L13N3701+1🕒	0.95	“⏎⏎”“:”“”:”
2. Groundhog✅ L5N4590+1,L11N1276-1	0.94	“⏎⏎”“”:”“:”
3. L12N7271+1🕒	0.91	“<\|im_end\|>”“⏎⏎”“:”
4. L1N7027-1💥	0.90	“ ?”“⏎”“<\|im_end\|>”
5. L18N1979-1🕒	0.90	“:”“...”“⏎⏎”
6. L1N2782+1💥	0.90	“<\|im_end\|>”“⏎⏎”“?:”
7. L16N2146+1🕒	0.90	“?:”“?):”“⏎”
8. L1N1345-1💥	0.89	“<\|im_end\|>”“.:”“⏎”
9. L2N8494-1💥	0.89	“<\|im_end\|>”“?:”“.:”
10. L1N7688+1💥	0.89	“<\|im_end\|>”“ ?”“.:”

Shadow specimen, apart from the ten: L12N3281-1💤, -0.09.

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 12, neuron 3281, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.62, top-1 agreement 0.66) 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
L12N3281+1 t0-v0.1	0.621	0.66	0.820	1.32	Top boosted tokens ↑
L12N3281-1💤 shadow specimen t0-v0.1	0.722	0.71	0.779	1.07	“ beyond”“ other”“ always”“ more”“ elsewhere”“ consistently”“ farther”“ deeper”

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.