Poor Things
Specimen ledger / L18N7404-1

Layer 18 · Neuron 7404 · suppressed

L18N7404-1

Word quirks:

“ choices”“ Choices”“ choice”“ preferred”“首选”“choices”“ Choice”“choice”

🕒 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, specifically the latest version Qwen3.5. I am a large-scale language model developed by Tongyi Lab, with a comprehensive range of capabilities designed to serve as an intelligent assistant. My primary mission is to provide high-quality, innovative solutions across diverse scenarios, balancing efficiency, creativity, and trustworthiness. Here’s how I align with this vision:

Core Capabilities & Choices:

  • Advanced Language Understanding: I process over 100 languages and handle complex contexts, ensuring natural and intuitive responses.
  • Professional Solutions: I offer industry-specific insights (e.g., healthcare

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. L25N2773-1🕒0.96“ choice”“ Choice”“Choice”
2. L21N8210+1🕒0.95“ choices”“ choice”“ Choice”
3. L18N6793-1🕒0.95“ choice”“ choices”“choice”
4. L23N7611-1🕒0.95“ choice”“ Choice”“ choices”
5. L30N1136+1💥0.95“choice”“ choice”“ Choice”
6. L21N7570+1🕒0.94“ choices”“ choice”“ choosing”
7. L21N4392-1🕒0.94“ choice”“ Choice”“Choice”
8. L24N3039-1🕒0.94“ choice”“ Choice”“Choice”
9. L22N8010+1🕒0.94“ choice”“ Choice”“ choices”
10. L20N5231-1💤0.94“ choice”“ Choice”“ Choices”

Shadow specimen, apart from the ten: L18N7404+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 7404, 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
L18N7404+1🕒 shadow specimen
t0-v0.1
0.6840.710.8861.23 “ essential”“关键环节”“ missing”“ key”“ necessary”“ essentials”“关键”“ critical”
L18N7404-1
t0-v0.1
0.6650.700.6991.12 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.