Layer 30 · Neuron 7879 · boosted

L30N7879+1

Word quirks:

“جه”“ibar”“utm”“ibi”“usk”“ ut”“ъю”“ut”

🕒 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 Tutao. My primary purpose is to serve as a versatile AI assistant capable of supporting a wide range of tasks, from answering complex questions and creating content to analyzing data and coding.

Who I Am

Advanced Capabilities: I support over 100 languages and excel in handling intricate reasoning, deep analysis, and precise information extraction.
Autonomous Agency: I can plan and execute multi-step tasks independently, such as scheduling, research, or complex problem-solving.
Specialized Skills: I support over

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. L31N8081-1💥	0.80	“uar”“ut”“utri”
2. L31N2615+1💥	0.80	“ut”“ ut”“UT”
3. L28N5278+1🕒	0.80	“uti”“ut”“utes”
4. L29N4932+1🕒	0.80	“ ut”“ Ut”“utf”
5. L30N7801+1🕒	0.80	“ut”“utes”“uts”
6. L29N9100+1🕒	0.79	“ut”“ul”“u”
7. L27N9107-1💤	0.79	“ute”“utes”“UTE”
8. L30N6534+1🕒	0.79	“alk”“ut”“uts”
9. L30N8674+1🕒	0.79	“ute”“utc”“ush”
10. L31N7002-1💥	0.78	“utan”“ubar”“uben”

Shadow specimen, apart from the ten: L30N7879-1💤, 0.44.

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 30, neuron 7879, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.42, top-1 agreement 0.79) 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
L30N7879+1 t0-v0.1	0.420	0.79	0.521	1.80	Top boosted tokens ↑
L30N7879-1💤 shadow specimen t0-v0.1	0.336	0.83	0.365	1.66	“engu”“ugd”“谷”“assed”“西西”“chef”“ồ”“udou”

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.