Layer 11 · Neuron 2781 · suppressed

L11N2781-1

Word quirks:

“⏎”“⏎⏎”“<|im_end|>”“ ____”“ ***”“ ______”“ _____”“ ****”

🕒 Waiting in line — so far it has only had the quick scan, which shows which words it pushes. It was not selected in this round.

Identity (according to the specimen itself)

I am Qwen3.5, a large language model developed by Alibaba Cloud. My mission is to assist users in generating high-quality text, answering questions, and solving complex problems. I can engage in multi-turn conversations, understand various contexts, and provide helpful responses.

As an AI model, I have the following capabilities:

Deep understanding of professional terminology: I can accurately use industry-specific vocabulary in fields such as finance, law, and science.
Code generation and debugging: I can write, read, and debug code, and explain the logic behind it.
Logical reasoning:

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. L12N5071-1🕒	1.00	“ ____”“ ***”“<\|im_end\|>”
2. L10N4796+1💤	0.98	“⏎”“<\|im_end\|>”“ ***”
3. L8N5059+1🕒	0.98	“<\|im_end\|>”“⏎”“ ____”
4. L10N382+1🕒	0.98	“<\|im_end\|>”“ ***”“ ____”
5. L6N741-1💤	0.97	“⏎”“<\|im_end\|>”“ ***”
6. L9N790+1🕒	0.97	“⏎”“<\|im_end\|>”“ _____”
7. L3N1132+1🕒	0.96	“<\|im_end\|>”“⏎”“⏎⏎”
8. L10N4754-1💤	0.96	“ ***”“ ____”“ ______”
9. The Stencil✅ L6N2135-1	0.96	“<\|im_end\|>”“ ***”“ _____”
10. L11N8907+1🕒	0.96	“<\|im_end\|>”“⏎”“⏎⏎”

Shadow specimen, apart from the ten: L11N2781+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 11, neuron 2781, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.89, 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
L11N2781+1🕒 shadow specimen t0-v0.1	0.974	0.59	1.307	1.22	“ --”“ particular”“ pertaining”“ encompass”“ somehow”“ respective”“ noteworthy”“ accompanying”
L11N2781-1 t0-v0.1	0.889	0.68	1.084	1.05	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.