Layer 6 · Neuron 3213 · suppressed

L6N3213-1

Word quirks:

“ solution”“ problem”“ question”“ Solution”“ Problem”“ exam”“ questions”“ problems”

🕒 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 a large language model trained by Google. My mission is to solve the following question.

Question

The following table shows the number of iterations required for the program to complete.

| Iteration | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|

Question 1: The following code implements a method that calculates the sum of the digits of the product of the roots of the equation $x^2 - 6x + 10 = k$. What is the value of $k$?

```cpp

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. L11N2681-1🕒	0.93	“ problem”“ Problem”“ question”
2. L4N1934-1💥	0.90	“ solution”“ problem”“ Problem”
3. L5N2431+1🕒	0.90	“ question”“ questions”“ solution”
4. L7N2580-1🕒	0.90	“ problem”“ problems”“ thinking”
5. L5N3523+1🕒	0.89	“ problem”“ problems”“ computation”
6. L5N3834-1🕒	0.88	“ problem”“ solution”“ problems”
7. L2N1276+1💥	0.87	“<\|im_end\|>”“ problem”“ problems”
8. L1N4542+1💥	0.86	“problem”“ problem”“ question”
9. L17N2313-1🕒	0.86	“ problem”“ solution”“ programme”
10. The Eager Solver✅ L10N5555-1	0.85	“ question”“ example”“ answer”

Shadow specimen, apart from the ten: L6N3213+1💤, 0.00.

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 6, neuron 3213, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 2.11, top-1 agreement 0.44) 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
L6N3213+1💤 shadow specimen t0-v0.1	0.852	0.62	1.070	1.64	“ ‘”“ “”“ *‘”“ '”“ ‘’”“ “”“ connection”“ open”
L6N3213-1 t0-v0.1	2.107	0.44	2.558	2.00	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.