Specimen ledger / L14N3619-1

Layer 14 · Neuron 3619 · suppressed

L14N3619-1

Word quirks:

“在”“对”“与”“、”“（”“被”“从”“给”

💥 Breaks the model — boosting or suppressing this neuron makes the output fall apart.

Identity (according to the specimen itself)

我是 Qwen3.5 版本中最新推出的模型。
在协助您完成各种任务时，所承载的全部意义。

给每一位需要的人。

提供、最需要的帮助。

给所有求教的人。

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希望，得到您的回应。

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这一句、
给所有关心的人。

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您，
最期待的那一句。

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希望、
有人听见。

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我的全部使命。

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给、每一个
正在提问的人。

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您，
值得被认真回答。

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所有、
想要问的问题。

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. L14N2338-1💥	0.98	“与”““”“在”
2. L7N404+1💥	0.97	“，”“在”“与”
3. L14N5920+1💥	0.97	“在”“与”“从”
4. L11N8401-1💥	0.97	“在”“与”“从”
5. L14N2534-1💥	0.97	“在”“的”“从”
6. L6N5062-1💥	0.96	“在”“被”“从”
7. L4N4796-1💥	0.96	“，”“。”“在”
8. L4N8730+1💥	0.96	“在”“与”“，”
9. L4N3726-1💥	0.96	“在”“（”“与”
10. L15N6284+1💥	0.96	“在”“与”“从”

Shadow specimen, apart from the ten: L14N3619+1🕒, 0.11.

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 14, neuron 3619, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 6.84, top-1 agreement 0.14) 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
L14N3619+1🕒 shadow specimen t0-v0.1	1.246	0.61	1.520	1.40	“?"”“?\”“？””“?””“?“”“?</”“ ?”“?”
L14N3619-1 t0-v0.1	6.843	0.14	7.213	1.88	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.