Specimen ledger / L2N1161-1

Layer 2 · Neuron 1161 · suppressed

L2N1161-1

Word quirks:

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

🧪 In testing — Answered 0 of 8 prompts so far.

Identity (according to the specimen itself)

The reply didn't hold together

<|w_1|
</think>

<|w_2|
</think>

<|w_3|
</|w_4|
</|w_5|
</|w_6|
</|w_7|
</|w_8|
</|w_9|
</|w_10|
</|w_11|
</|w_12|
</|w_13|
</|w_14|
</|w_15|
</|w_16|
</|w_1

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. L2N6372+1🕒	1.00	“⏎”“<\|im_end\|>”“⏎ ⏎”
2. L1N5085+1🕒	1.00	“<\|im_end\|>”“⏎”“⏎⏎”
3. L1N7886-1💥	1.00	“<\|im_end\|>”“⏎ ⏎”“⏎”
4. L2N8955-1💥	1.00	“<\|im_end\|>”“⏎”“⏎ ⏎”
5. L1N1179+1💥	1.00	“<\|im_end\|>”“⏎”“⏎⏎”
6. L1N7465+1💥	1.00	“<\|im_end\|>”“⏎”“⏎ ⏎”
7. L1N6628+1🕒	0.99	“<\|im_end\|>”“⏎”“⏎⏎”
8. L1N4740-1🕒	0.99	“⏎”“⏎ ⏎”“<\|im_end\|>”
9. L2N2214-1💥	0.99	“<\|im_end\|>”“⏎”“⏎ ⏎”
10. L1N364-1💥	0.98	“<\|im_end\|>”“⏎”“⏎⏎”

Shadow specimen, apart from the ten: L2N1161+1🕒, 0.47.

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 2, neuron 1161, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. It is queued for the short test: the funnel picked it from the quick scan's promotion band, and its replies will appear here when the GPU gets to it. 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
L2N1161+1🕒 shadow specimen t0-v0.1	2.480	0.33	2.927	2.87	“<\|im_end\|>”“⏎”“?\”“ ______”“ ____”“.\"”“ _____”“⏎ ⏎”
L2N1161-1 t0-v0.1	1.607	0.50	1.929	1.96	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.