Layer 10 · Neuron 2934 · boosted

L10N2934+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 assist users with a wide range of tasks, from answering questions to solving problems. Whether it's helping you write an email, create a story, or even just provide information on various topics, I'm here to help! If you have any specific task in mind, feel free to ask me how I can assist you today.

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. L10N295-1💤	0.95	“ ⏎”“ ⏎⏎”“ ______”
2. L15N1496+1🕒	0.95	“⏎”“...”“…”
3. L10N886-1💤	0.93	“⏎”“ ⏎”“<\|im_end\|>”
4. L10N342-1🕒	0.93	“<\|im_end\|>”“ ____”“ ⏎⏎”
5. L12N8885-1💤	0.93	“ __”“ ____”“ ______”
6. L6N8768+1🕒	0.93	“...””“...\”“<\|im_end\|>”
7. L14N656-1💤	0.93	“ ⏎⏎”“ ___”“...\”
8. L10N7027-1💤	0.93	“ ⏎”“...”“ ⏎⏎”
9. L5N1024-1🕒	0.93	“⏎”“ ⏎”“ _____”
10. L15N1626+1💤	0.93	“ ⏎”“ ____”“\n”

Shadow specimen, apart from the ten: L10N2934-1💤, 0.52.

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 2934, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.74, top-1 agreement 0.68) 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
L10N2934+1 t0-v0.1	0.744	0.68	0.964	1.25	Top boosted tokens ↑
L10N2934-1💤 shadow specimen t0-v0.1	0.985	0.61	1.201	1.47	“ ⏎⏎”“ ⏎ ⏎”“ ⏎”“ ⏎⏎”“ ‘”“ ⏎ ⏎”“ ⏎”“⏎ ⏎”

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.