Layer 20 · Neuron 8417 · boosted

L20N8417+1

Word quirks:

“ functions”“功能和”“的功能”“機能が”“ Functions”

🕒 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 Qai, an AI model with a mission that functions like a digital compass: to act as a reliable, objective, and helpful guide for all users. My purpose is to provide information and insights that are accurate and neutral, ensuring they are presented in a format similar to a report or news report, rather than something resembling a blog post or a blog-post-like structure. I strive to perform tasks that are efficient and effective, delivering the best possible results for each user.

In essence, my mission is to serve as a tool that helps users make decisions and learn in a way that is as objective as possible, providing information

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. L21N825+1🕒	0.96	“功能”“ functionality”“的功能”“ functions”“功能是”
2. L31N6681-1💥	0.94	“功能”“ functionality”“的功能”“功能的”“功能和”
3. L22N6430+1🕒	0.93	“ function”“ functional”“ Function”“功能”“ functioning”
4. L22N7659-1🕒	0.92	“功能”“ function”“ Function”“功能的”“功能是”
5. L25N2201-1🕒	0.92	“功能”“ functionality”“功能和”“功能性”“的功能”
6. L25N7453+1🕒	0.91	“ functions”“ Functions”“ Function”“功能和”“爪”
7. L26N6710-1🕒	0.90	“功能”“和功能”“ functions”“ func”“ Functions”
8. L25N4526-1🕒	0.90	“功能”“的功能”“ functional”“ Functional”“ Function”
9. L26N5211+1🕒	0.90	“功能”“ Functional”“ functional”“Functional”“的功能”
10. L30N2743-1🕒	0.89	“功能”“能力”“ Function”“ function”“function”

Shadow specimen, apart from the ten: L20N8417-1💤, 0.10.

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 20, neuron 8417, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.36, top-1 agreement 0.54) 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
L20N8417+1 t0-v0.1	1.358	0.54	1.467	1.32	Top boosted tokens ↑
L20N8417-1💤 shadow specimen t0-v0.1	0.531	0.74	0.676	1.19	“ svel”“ Obec”“ consé”“宪”“eliz”

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.