Specimen ledger / L3N2032+1

Layer 3 · Neuron 2032 · boosted

L3N2032+1

Word quirks:

“<|im_end|>”“!”“?!”“ !”“!!”“⏎ ⏎”“⏎”“ ____”

🕒 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)

<think>

</think>

I'm sorry, but I can't answer that question. Could you please provide more context about who you are and what your mission is?

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. L1N3510-1🕒	0.98	“<\|im_end\|>”“⏎”“"!”
2. L3N8768+1🕒	0.96	“<\|im_end\|>”“⏎”“!”
3. L3N2427-1🕒	0.96	“⏎”“!”“<\|im_end\|>”
4. L6N7212+1🕒	0.96	“⏎”“<\|im_end\|>”“⏎ ⏎”
5. L2N9199-1💥	0.96	“<\|im_end\|>”“⏎ ⏎”“!”
6. L1N8277-1🕒	0.96	“<\|im_end\|>”“!”“!!”
7. L3N4172+1🕒	0.96	“⏎”“<\|im_end\|>”“!”
8. L2N679-1💥	0.96	“<\|im_end\|>”“⏎”“⏎ ⏎”
9. L2N8542-1🕒	0.96	“<\|im_end\|>”“⏎”“ !”
10. L1N7499+1🕒	0.95	“<\|im_end\|>”“ !”“!”

Shadow specimen, apart from the ten: L3N2032-1💥, -0.14.

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 3, neuron 2032, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 2.03, top-1 agreement 0.46) 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
L3N2032+1 t0-v0.1	2.030	0.46	2.416	2.19	Top boosted tokens ↑
L3N2032-1💥 shadow specimen t0-v0.1	3.264	0.31	3.729	2.70	“ deprecated”“ Deprecated”“ release”“ version”“deprecated”“ API”“ versions”“ releases”

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.