Poor Things
Specimen ledger / L24N1590+1

Layer 24 · Neuron 1590 · boosted

L24N1590+1

Word quirks:

“ compos”“ composition”“composition”“ compositions”“ Composition”“Composition”“ Compos”“comb”

🕒 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 Liberal, a high-end AI composition engine designed to provide safe, compliant, and high-quality content. My mission is to serve as a reliable assistant that combines deep knowledge with strict adherence to safety guidelines, ensuring every response is both informative and aligned with the principles of the Chinese market. I aim to help users solve problems, learn new things, and make informed decisions while maintaining a positive and safe environment for all. How can I assist you today?

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. L24N7124-1💤0.98“ compositions”“ Composition”“Composition”
2. L0N6138-1💥0.98“ composition”“ Composition”“<|im_end|>”
3. L26N1201-1🕒0.98“ composition”“Composition”“ Composition”
4. L27N7467+1🕒0.98“ composition”“composition”“ Composition”
5. L25N668-1🕒0.98“ composition”“Composition”“ Composition”
6. L25N6274+1🕒0.97“ composition”“ compose”“ Composition”
7. L25N6672-1🕒0.97“ composition”“ Composition”“Composition”
8. L23N7274+1🕒0.97“ composition”“Composition”“ Composition”
9. L30N5860-1💥0.97“ composition”“ Composition”“ compos”
10. L5N4376-1💥0.97“ composition”“ compositions”“ compose”

Shadow specimen, apart from the ten: L24N1590-1💤, 0.01.

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 24, neuron 1590, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.82, top-1 agreement 0.68) 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
L24N1590+1
t0-v0.1
0.8220.680.8541.29 Top boosted tokens ↑
L24N1590-1💤 shadow specimen
t0-v0.1
0.6610.800.7071.27 “绅”“ син”“悬浮”“ careers”“sher”“orent”“유의”“争”

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.