Layer 13 · Neuron 4363 · suppressed

L13N4363-1

Word quirks:

“ basic”“ basically”“ ⏎”“ directly”“ small”“⏎”“ core”“ immediate”

🕒 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 a large language model, part of the Qwen series developed by Tongyi Lab. My capabilities range from understanding and processing text to performing mathematical calculations, handling logical reasoning, and generating code—all of which can be easily adapted to different tasks.

As for my mission, I aim to provide users with accurate information, solve problems through logical reasoning, and assist with coding. However, my ability to handle complex tasks depends on the specific context or requirements. For instance:

Mathematical calculations: I excel at simple arithmetic but may struggle with multi-step problems involving fractions or decimals.
**Code generation

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. L12N6554+1🕒	0.79	“ basic”“ now”“ basically”
2. L15N6312-1🕒	0.75	“ ⏎”“\n”“ core”
3. L17N947-1💤	0.73	“ ⏎”“ minimum”“ basically”
4. L11N3990-1💤	0.71	“ basic”“ now”“ simple”
5. L17N9176+1🕒	0.70	“ simpler”“ basics”“ ⏎”
6. L11N7125+1🕒	0.68	“ ⏎”“ privacy”“⏎”
7. L14N3312-1💤	0.68	“ ⏎”“⏎”“ initially”
8. L12N868+1💤	0.68	“⏎”“ basically”“ basic”
9. L11N1059+1🕒	0.68	“ based”“ directly”“⏎”
10. L15N7549-1💤	0.68	“ basically”“ basic”“ essentially”

Shadow specimen, apart from the ten: L13N4363+1🕒, 0.06.

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 13, neuron 4363, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.07, top-1 agreement 0.61) 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
L13N4363+1🕒 shadow specimen t0-v0.1	1.632	0.56	1.983	1.23	“ already”“ again”“ repeat”“ ____”“ repeated”“ ⏎”“ repeats”“ ***”
L13N4363-1 t0-v0.1	1.066	0.61	1.390	1.34	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.