Layer 21 · Neuron 4957 · suppressed

L21N4957-1

Word quirks:

“能提供”“深入了解”“ detailed”“提供更”“能够提供”“深入”“ providing”“深入的”

🕒 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 Kiang, a comprehensive real-time deep dive and immersive storytelling agent designed to provide you with an immersive experience. My mission is to give you a complete and detailed understanding of topics by providing a comprehensive overview, allowing you to gain a deeper insight into the subject.

To get a more detailed and accurate answer, I will:

Provide a comprehensive overview that covers all aspects of the topic.
Offer a detailed explanation to help you gain a deeper understanding.
Deliver a comprehensive summary to ensure you have a complete picture.

If you want to learn more about something, just ask for a "detailed dive

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. L30N4245-1💥	0.85	“深入”“深入的”“更深入”
2. L28N2008-1💤	0.82	“提供一个”“提供一种”“提供更”
3. L26N1054+1🕒	0.82	“帮助”“提供更好的”“提供良好的”
4. L26N754-1💤	0.80	“提供”“宣”“送上”
5. L24N4181-1🕒	0.80	“提供”“需要提供”“提供更”
6. L29N2354+1💤	0.79	“提供良好的”“提供支持”“ Provided”
7. L22N8848+1💤	0.79	“ providing”“折”“提供”
8. L25N1536+1🕒	0.78	“ extra”“ additional”“提供更多的”
9. L30N2918+1🕒	0.78	“提供一种”“提供”“提供一个”
10. L26N2280+1🕒	0.78	“提供”“提供的”“提供了”

Shadow specimen, apart from the ten: L21N4957+1💤, 0.24.

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 4957, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.48, top-1 agreement 0.59) 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
L21N4957+1💤 shadow specimen t0-v0.1	0.612	0.72	0.709	1.13	“ measured”“Measured”“вне”“ assessed”“raj”“ evaluated”“评估”“埋在”
L21N4957-1 t0-v0.1	1.477	0.59	1.640	1.47	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.