Layer 18 · Neuron 8511 · boosted

L18N8511+1

Word quirks:

“ monitoring”“ monitor”“ monitors”“ monitored”“monitor”“ Monitoring”“ Monitor”“ watcher”

🕒 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, an AI assistant developed by Alibaba's AI labs. My mission is to provide reliable, efficient, and user-friendly support across a wide range of tasks, including:

Real-time monitoring: Track data streams, dashboards, or IoT devices with automated alerts.
Smart recovery: Set up automatic backups, failover systems, and real-time monitoring for critical infrastructure.
Cloud integration: Manage cloud services (AWS, Azure, etc.) via APIs, ensuring seamless connectivity and redundancy.
Security protocols: Implement multi-factor authentication, encryption, and continuous security monitoring.

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. L26N3525-1🕒	0.98	“ monitors”“ Monitor”“ monitor”
2. The Watchdog✅ L8N3161+1	0.98	“ monitoring”“ monitor”“ monitors”
3. L30N7187-1🕒	0.98	“ monitor”“ Monitor”“ monitors”
4. L6N9062+1🕒	0.97	“ monitoring”“ monitor”“ monitored”
5. L1N5043-1💥	0.97	“<\|im_end\|>”“ monitor”“ monitoring”
6. L23N5439+1🕒	0.97	“ monitor”“monitor”“ monitors”
7. L22N5207+1🕒	0.97	“ watching”“ monitoring”“ monitors”
8. L25N7909+1🕒	0.96	“ monitoring”“ monitored”“监控”
9. L2N6889-1💥	0.96	“ monitor”“ monitors”“ monitoring”
10. L0N8828+1💥	0.96	“ monitor”“<\|im_end\|>”“Monitor”

Shadow specimen, apart from the ten: L18N8511-1🕒, 0.12.

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 18, neuron 8511, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.69, top-1 agreement 0.69) 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
L18N8511+1 t0-v0.1	0.692	0.69	0.790	1.37	Top boosted tokens ↑
L18N8511-1🕒 shadow specimen t0-v0.1	0.742	0.70	0.876	1.29	“ except”“ discussion”“ discussions”“ discussing”“ forming”“ starting”“讨论”“/general”

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.