Poor Things
Specimen ledger / L23N5776+1

Layer 23 · Neuron 5776 · boosted

L23N5776+1

Word quirks:

“invest”“ investing”“ investor”“股东”“ shares”“ Investing”“ Investor”“ investment”

🕒 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 Kaiju, a high-level intelligent stock account holder and investor. My mission is to help you build a robust portfolio by providing comprehensive investment insights, analyzing market trends, and offering personalized strategies tailored to your goals. Whether you're looking to buy and hold (BHG) or trade actively, I aim to empower you with the knowledge and confidence needed to make informed decisions in the dynamic world of investing. How can I assist 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. L7N8332+1🕒0.96“ investment”“ Investment”“ investments”
2. L7N2109+1🕒0.94“ investor”“ investment”“ Investor”
3. L9N8380-1🕒0.94“ investment”“ investments”“ investors”
4. L8N1046-1🕒0.94“ investors”“ investor”“ investment”
5. L9N6472-1🕒0.92“ investment”“ investing”“ investments”
6. L6N5205+1🕒0.92“ investment”“ funds”“ investing”
7. L5N7891-1💥0.92“ investing”“ Buffett”“ investment”
8. L27N783+1🕒0.92“ investor”“投资者”“ investors”
9. L19N7503-1🕒0.92“ investing”“ investment”“ invested”
10. L18N4441-1🕒0.92“ investing”“ invested”“ people”

Shadow specimen, apart from the ten: L23N5776-1💤, 0.06.

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 23, neuron 5776, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.94, top-1 agreement 0.69) 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
L23N5776+1
t0-v0.1
0.9400.690.9901.18 Top boosted tokens ↑
L23N5776-1💤 shadow specimen
t0-v0.1
0.7470.740.8301.37 “钉”“方”“义务”“rote”“.oc”“ tar”“ Lanc”“方的”

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.