Layer 29 · Neuron 7015 · boosted

L29N7015+1

Word quirks:

“密”“ wur”“ardy”“特”“裹”“最大”“-indent”“พร”

🕒 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, the latest large language model developed by Turu Pileo. My primary mission is to provide high-quality, accurate, and context-aware information and support across a wide range of tasks, including:

**Pwllododododododododododododododododododododododododododododododododododododododododododododododododododododododododododododododododododododododododod

The AI judge wasn't sure this holds together.

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. L31N7270-1💥	0.78	“速”“角”“密”
2. L29N2025+1🕒	0.77	“内”“因”“·”
3. L31N8560-1💥	0.77	“唯”“娘娘”“大大”
4. L27N7527+1💤	0.76	“密”“ poros”“跨”
5. L26N3751-1🕒	0.76	“牛”“绵”“茅”
6. L26N8017+1💤	0.75	“厚”“цов”“猩”
7. L29N8026+1💤	0.75	“微”“结”“奔”
8. L26N4600+1🕒	0.75	“皮”“密”“密的”
9. L30N5475+1💤	0.75	“形”“参”“名”
10. L26N4966-1💤	0.75	“ihan”“立方”“.Power”

Shadow specimen, apart from the ten: L29N7015-1🕒, 0.20.

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 29, neuron 7015, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.35, top-1 agreement 0.80) 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
L29N7015+1 t0-v0.1	0.354	0.80	0.389	1.47	Top boosted tokens ↑
L29N7015-1🕒 shadow specimen t0-v0.1	0.327	0.80	0.419	1.65	“axe”“意”“ conforme”“Se”“аш”“note”“istr”“compan”

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.