Specimen ledger / L12N2951-1

Layer 12 · Neuron 2951 · suppressed

L12N2951-1

Word quirks:

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

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

Identity (according to the specimen itself)

I am Qwen3.5, an advanced language model designed to assist with a wide range of tasks. My capabilities include:

Advanced reasoning
Visual analysis
Code generation and understanding
Data analysis
Multi-modal comprehension
Full-stack programming assistance

If you have specific questions or tasks in mind, please let me know!

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. L10N3638-1🕒	0.99	“ ⏎”“⏎”“ ⏎”
2. L5N94-1💥	0.99	“<\|im_end\|>”“ ⏎”“⏎”
3. L2N1370+1🕒	0.99	“<\|im_end\|>”“⏎”“?\”
4. L3N4807-1🕒	0.99	“<\|im_end\|>”“⏎”“⏎⏎”
5. L1N7828-1🕒	0.99	“<\|im_end\|>”“⏎”“⏎ ⏎”
6. L5N953+1🕒	0.99	“⏎”“ ⏎”“⏎⏎”
7. L1N1184+1💤	0.99	“⏎”“?\”“<\|im_end\|>”
8. L11N2166-1🕒	0.99	“⏎⏎”“⏎”“ ?”
9. L10N2143+1💤	0.99	“⏎⏎”“<\|im_end\|>”“⏎”
10. L11N2157+1🕒	0.99	“<\|im_end\|>”“⏎⏎”“ ⏎⏎”

Shadow specimen, apart from the ten: L12N2951+1💤, 0.42.

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 12, neuron 2951, 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
L12N2951+1💤 shadow specimen t0-v0.1	0.627	0.73	0.809	1.11	“�”“ ⏎”“ extra”“ focus”“ __”“ *”“ @”“ ‘”
L12N2951-1 t0-v0.1	1.114	0.56	1.387	1.31	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.