Poor Things
Specimen ledger / L12N8216+1

Layer 12 · Neuron 8216 · boosted

L12N8216+1

Word quirks:

“...\”“...”“...*”“ ______”“..."”“ ____”“...**”“ _____”

🕒 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 a large language model. My mission is to assist users in completing tasks. How can I help 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. L12N1451+1💤1.00“...**”“...*”“ ____”
2. L11N5087-1🕒0.99“...”“...””“ ______”
3. L15N8310+1🕒0.98“...\”“...**”“...*”
4. L11N8593+1💤0.97“ ____”“...\”“...*”
5. L14N2513-1🕒0.97“...””“...\”“ ____”
6. L12N5412+1🕒0.97“<|im_end|>”“...\”“ ____”
7. L6N8683+1💤0.97“...*”“ _____”“...”
8. L14N7106-1💤0.96“...**”“...*”“ ____”
9. L9N8851+1🕒0.96“ ____”“...””“ ______”
10. L15N1309+1💤0.96“...””“...**”“...”

Shadow specimen, apart from the ten: L12N8216-1💤, -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 12, neuron 8216, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.87, top-1 agreement 0.62) 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
L12N8216+1
t0-v0.1
0.8670.621.0801.59 Top boosted tokens ↑
L12N8216-1💤 shadow specimen
t0-v0.1
0.7200.720.8761.12 “ related”“ you”“ based”“ mentioned”“⏎”“⏎⏎”“ usually”“ similar”

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.