Poor Things
Specimen ledger / L20N2571-1

Layer 20 · Neuron 2571 · suppressed

L20N2571-1

Word quirks:

“ parameters”“参数”“ Parameters”“参数的”“的参数”“ params”“Parameters”“ parameter”

🕒 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 Qwen, a large-scale language model with comprehensive parameters. My mission is to provide high-quality, safe, and personalized assistance across a wide range of domains. Here’s what I aim to do:

  • Learn & Adapt: Continuously refine my capabilities through learning and adaptation to meet diverse user needs.
  • Solve Complex Problems: Handle tasks like reasoning, coding, and multi-step decision-making with precision.
  • Ensure Safety & Ethics: Uphold strict safety standards while delivering personalized and secure interactions.
  • Collaborate Seamlessly: Work with users to optimize efficiency and unlock unique possibilities in

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

SpecimenSimilarityWord quirks
1. L20N2292+1🕒1.00“参数”“ parameters”“参数的”
2. L23N2983-1🕒1.00“ parameters”“参数”“Parameters”
3. L23N4350-1🕒0.99“参数”“ Parameters”“参数的”
4. L22N1916+1🕒0.99“ parameters”“参数”“parameters”
5. L21N3679-1💤0.99“ parameters”“参数”“ Parameters”
6. L23N4642+1🕒0.98“ parameters”“参数”“ Parameters”
7. L26N1765-1🕒0.98“参数”“参数的”“ parameters”
8. L27N4157-1🧪0.97“参数”“ Parameters”“ parameters”
9. L22N3571-1🕒0.97“ parameters”“参数”“ Parameters”
10. L31N1172+1💥0.97“参数”“ parameter”“ Parameter”

Shadow specimen, apart from the ten: L20N2571+1💤, -0.04.

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 20, neuron 2571, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.67, 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.

SettingKL divergenceTop-1 agreementΔNLLEntropySteer lens
L20N2571+1💤 shadow specimen
t0-v0.1
0.5870.730.6951.20 “ delivery”“ prep”“ actual”“ adults”“ adult”“克森”“ deliveries”“ mature”
L20N2571-1
t0-v0.1
0.6690.700.7901.19 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.