Specimen ledger / L1N8973-1

Layer 1 · Neuron 8973 · suppressed

L1N8973-1

Word quirks:

“<|im_end|>”“⏎”“⏎ ⏎”“.</”“.\"”“?</”“</”“.`”

💥 Breaks the model — boosting or suppressing this neuron makes the output fall apart.

Identity (according to the specimen itself)

The reply didn't hold together

assistant
</think>

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. L0N8382+1💥	1.00	“<\|im_end\|>”“.`”“⏎ ⏎”
2. L1N2554+1💥	0.99	“<\|im_end\|>”“?</”“.`”
3. L1N4121-1💥	0.99	“<\|im_end\|>”“⏎ ⏎”“⏎”
4. L1N8053+1💥	0.99	“<\|im_end\|>”“.`”“?</”
5. L1N1225-1💥	0.98	“<\|im_end\|>”“⏎”“?</”
6. L1N1446+1💥	0.98	“<\|im_end\|>”“⏎”“?</”
7. L1N1428-1💥	0.98	“<\|im_end\|>”“⏎”“?</”
8. L1N8632-1💥	0.98	“<\|im_end\|>”“.`”“⏎”
9. L1N7494-1💥	0.97	“<\|im_end\|>”“.`”“.</”
10. The Shut-In✅ L1N611-1	0.97	“<\|im_end\|>”“⏎”“⏎ ⏎”

Shadow specimen, apart from the ten: L1N8973+1💥, 0.24.

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 1, neuron 8973, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 2.74, top-1 agreement 0.39) 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
L1N8973+1💥 shadow specimen t0-v0.1	5.455	0.05	5.990	3.00	“<\|im_end\|>”“ singleton”“ Singleton”“ ?”“.`”“Singleton”“ingleton”“?\”
L1N8973-1 t0-v0.1	2.743	0.39	3.219	2.68	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.