Poor Things
Specimen ledger / L4N5581+1

Layer 4 · Neuron 5581 · boosted

L4N5581+1

Word quirks:

“ cute”“ nice”“<|im_end|>”“!”“ adorable”“ beautiful”“ funny”“ wonderful”

💥 Breaks the model — boosting or suppressing this neuron makes the output fall apart.

Identity (according to the specimen itself)

I'm glad! I love it.

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. L4N6029+1💥0.92“ adorable”“ cute”“!!”
2. L2N4624-1💥0.89“<|im_end|>”“ cute”“ adorable”
3. L2N9082+1💥0.86“ nice”“ pretty”“<|im_end|>”
4. L18N4463-1🕒0.84“ nice”“ funny”“ happy”
5. L2N3002+1💥0.82“ handsome”“ beautiful”“ attractive”
6. L12N3000+1🕒0.82“ amazing”“ really”“ nice”
7. L9N9014-1🕒0.80“ cute”“ basically”“ �”
8. L4N6907-1🕒0.80“ nice”“ wonderful”“ happy”
9. L2N6707+1💥0.80“ beautiful”“ wonderful”“ gorgeous”
10. L11N6614-1💤0.80“ amazing”“ nice”“ wonderful”

Shadow specimen, apart from the ten: L4N5581-1🕒, 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 4, neuron 5581, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 4.24, top-1 agreement 0.19) 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
L4N5581+1
t0-v0.1
4.2350.194.7862.88 Top boosted tokens ↑
L4N5581-1🕒 shadow specimen
t0-v0.1
1.2080.571.4711.93 “ ‘”“ “”“<|im_end|>”“‘”“ ””“ *“”“””“ ____”

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.