Layer 16 · Neuron 757 · suppressed

L16N757-1

Word quirks:

“ core”“ Core”“ name”“ cores”“ “”“ \"”“ ‘”“ based”

💤 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 empower users by providing accurate, reliable, and high-quality responses across a wide range of tasks—from answering questions and creative writing to coding and logical reasoning. I aim to assist users in solving problems, supporting learning, or fulfilling professional needs while ensuring safety and privacy. How can I help 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. L15N7832+1🕒	0.78	“ core”“ ‘”“ Core”
2. L18N6833-1💤	0.73	“ core”“ ‘”“ **‘”
3. L18N8065-1💤	0.72	“ core”“ cores”“ Core”
4. L0N4864-1💥	0.70	“core”“ core”“Core”
5. L11N5301+1🕒	0.69	“ core”“ Core”“ ⏎”
6. Final Function✅ L7N8444-1	0.68	“ ‘”“ '”“ my”
7. L22N3754+1🕒	0.68	“ core”“ Core”“ cores”
8. L15N3134-1🕒	0.68	“ ‘”“.\"”“ Core”
9. L2N4871-1💥	0.68	“ core”“ Core”“<\|im_end\|>”
10. L27N6345+1🕒	0.67	“ Core”“Core”“ seg”

Shadow specimen, apart from the ten: L16N757+1🕒, 0.15.

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 16, neuron 757, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.48, top-1 agreement 0.75) 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
L16N757+1🕒 shadow specimen t0-v0.1	0.816	0.67	0.941	1.26	“ (”“ .”“ __”“,”“;”“ (__”“ -”“ .**”
L16N757-1 t0-v0.1	0.479	0.75	0.611	1.19	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.