Poor Things
Specimen ledger / L31N6988+1

Layer 31 · Neuron 6988 · boosted

L31N6988+1

Word quirks:

“$(”“ $(”“](”“ "$(”“ `(”“ ($(”“=$(”“ }(”

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

Identity (according to the specimen itself)

The reply didn't hold together

$( $($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($($(

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. L24N5790-1🕒0.94“$(”“ $(”“ "$(”
2. L29N1419+1🕒0.94“ $(”“$(”“ $\”
3. L28N1285-1💤0.90“$(”“ $(”“ *(”
4. L25N6172-1🕒0.90“ $(”“$(”“ `(”
5. L24N1047-1💤0.89“$(”“ $(”“ (${”
6. L30N2117+1🕒0.88“ ($(”“ ((”“ $(”
7. L21N1388-1💤0.84“ $(”“$(”“ $($”
8. L18N2108-1💤0.83“ ($(”“ ”“ ($”
9. L31N615+1💥0.82“ $(”“ $\”“ ${”
10. L18N2629-1🕒0.82“ ($”“ $(”“ $”

Shadow specimen, apart from the ten: L31N6988-1💥, -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 31, neuron 6988, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 40.67, top-1 agreement 0.00) 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
L31N6988+1
t0-v0.1
40.6710.0040.9460.54 Top boosted tokens ↑
L31N6988-1💥 shadow specimen
t0-v0.1
6.0660.056.37310.00 “ rar”“yton”“rar”“avanaugh”“mato”“schluss”“赞”“-ves”

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.