Layer 16 · Neuron 1778 · suppressed

L16N1778-1

Word quirks:

“ focused”“ focus”“ focusing”“ focuses”“ foc”“ fokus”“ describing”“ Focus”

🕒 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 Qwen3.5, and I am an AI language model that specializes in developing large language models. My mission is to assist users in creating high-quality applications and providing support for developers, focusing on building applications with a focus on application development. My core strengths include:

Enhanced language model for natural language understanding and generation.
Advanced reasoning for complex problem-solving.
Full-stack code for code generation and debugging.
Agentic workflows for autonomous agent planning.
Visual analysis for visual analysis.
Long-context context window for handling

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. L10N97-1💥	0.98	“ focus”“ focuses”“ focused”
2. L17N7881+1💤	0.98	“ focus”“ focusing”“ focuses”
3. L17N4741+1🕒	0.97	“ focus”“ focusing”“ focuses”
4. L17N6888+1🕒	0.97	“ focus”“ focused”“ focuses”
5. L15N3875-1🕒	0.97	“ focus”“ Focus”“ focused”
6. L7N7580-1💥	0.97	“ focus”“ focused”“ focusing”
7. L14N8611-1🕒	0.97	“ focus”“ Focus”“ focusing”
8. L14N6734+1🕒	0.97	“ focus”“ focused”“ focusing”
9. L13N5885+1🕒	0.97	“ focus”“ focusing”“ focused”
10. L16N6051+1🕒	0.97	“ focus”“ focusing”“ focuses”

Shadow specimen, apart from the ten: L16N1778+1💤, -0.21.

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 16, neuron 1778, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.77, top-1 agreement 0.70) 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
L16N1778+1💤 shadow specimen t0-v0.1	0.597	0.65	0.833	1.44	“ “”“ ‘”“ ——”“ “”“——”“ ‘”“”:”“ **'”
L16N1778-1 t0-v0.1	0.768	0.70	0.871	1.20	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.