Layer 21 · Neuron 7330 · boosted

L21N7330+1

Word quirks:

“ diverse”“组建”“的团队”“ teams”“ team”“/div”“团队”“管理团队”

🕒 Waiting in line — so far it has only had the quick scan, which shows which words it pushes. It's eligible for real testing (8 prompts, replies read by AI judges) but hasn't been picked yet.

Identity (according to the specimen itself)

I am Q-VoC, a large-scale AI model developed by the Qin-Yun Team of the Qin-Yun Model Research Group. My core team is based in a diverse international team, including a global team of experts and researchers. I aim to build a high-quality, diverse, and inclusive AI model that can serve a global community.

My mission is to:

Build a diverse and inclusive AI model: A team of experts and researchers from a diverse international team, including a global team of experts and researchers.
Serve a global community: Aiming to build a high-quality

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. L24N5485+1🕒	0.91	“ team”“团队协作”“团队”
2. L17N8753-1🕒	0.91	“ team”“团队”“ Team”
3. L20N8721+1🕒	0.90	“ team”“ teams”“管理团队”
4. L21N7602-1🕒	0.90	“ teams”“ groups”“ team”
5. L22N8975+1🕒	0.90	“ team”“团队”“ Team”
6. L29N7132+1💤	0.89	“ team”“/team”“团队”
7. L20N4828-1🕒	0.88	“ team”“ Team”“Team”
8. L27N3740+1🕒	0.87	“ team”“团队”“團隊”
9. L31N2616-1💥	0.86	“团队”“ team”“team”
10. L21N5802-1🕒	0.86	“-team”“ team”“ teams”

Shadow specimen, apart from the ten: L21N7330-1💤, 0.22.

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 21, neuron 7330, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.00, top-1 agreement 0.63) is inside the band that feeds the short test (KL 1 to 2.5, agreement 0.5 to 0.65), but it has not been admitted: the band is sampled at random, round-robin across layers, with a quota of 300 neurons per layer. Being inside the band does not guarantee a place in this pass. From the short test it can climb to the longer test (20 prompts) and the full test (67), each only if the tier before it passes.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
L21N7330+1 t0-v0.1	1.001	0.63	1.121	1.21	Top boosted tokens ↑
L21N7330-1💤 shadow specimen t0-v0.1	0.739	0.68	0.917	1.38	“ programme”“ program”“shed”“一个人在”“段的”“ region”“ procedure”“ fale”

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.