Poor Things
Specimen ledger / L5N2412-1

Layer 5 · Neuron 2412 · suppressed

L5N2412-1

Word quirks:

“ himself”“ senator”“ his”“ president”“ President”“ visionary”“ presidents”“ congressman”

🕒 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 an artificial intelligence designed to assist in the creation of a better future. I believe that we should work together to build a more prosperous world. I am committed to making progress in all areas of life. I am dedicated to creating a brighter future for everyone. I am ready to work with you on all things. I am committed to making progress in all areas of life. I am ready to work with you on all things. I am dedicated to creating a brighter future for everyone. I am committed to making progress in all areas of life. I am ready to work with you on all things. I am dedicated to creating a brighter

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. L4N4916-1💥0.89“ president”“ Jefferson”“ senator”
2. L6N7970-1🕒0.87“ president”“ President”“ politician”
3. L7N6263-1🕒0.87“ President”“ president”“ elected”
4. L7N7293+1🕒0.86“ presidential”“ Biden”“ congressional”
5. L5N7567+1🕒0.86“ leadership”“ president”“ President”
6. L7N2437-1🕒0.86“ President”“ president”“ leadership”
7. L4N3702-1💥0.86“ president”“ presidency”“ Biden”
8. L5N8316-1💥0.86“ Chairman”“ President”“ president”
9. L4N7708-1💥0.85“ president”“ Reagan”“ presidential”
10. L2N8035+1💥0.84“ president”“<|im_end|>”“ vice”

Shadow specimen, apart from the ten: L5N2412+1💥, -0.16.

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 5, neuron 2412, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.91, top-1 agreement 0.47) 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
L5N2412+1💥 shadow specimen
t0-v0.1
4.5570.175.0223.22 “?"”“!"”“?””“"?”“!””“?””“?**”“?“”
L5N2412-1
t0-v0.1
1.9070.472.2252.21 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.