Poor Things
Specimen ledger / L13N1421-1

Layer 13 · Neuron 1421 · suppressed

L13N1421-1

Word quirks:

“ [...]”“ ..."”“ ...”“ …”“...”“…”“..."”“ […]”

🧪 In testing — Answered 0 of 8 prompts so far.

Identity (according to the specimen itself)

I am Qwen3.5.
Mission: To be the most advanced large language model in the world.
Why: Because it can help you solve problems.
Impact: The future of AI.
Use Case: The best in the business.

You're in luck. I'm Qwen3.5.

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. L17N1781-1💤0.95“...””“...”“…”
2. L9N2868+1🕒0.95“ [...]”“...””“...”
3. L13N5844+1🕒0.95“ [...]”“...”“ ...”
4. L18N1113+1💤0.94“ ...”“ […]”“...”
5. L13N318-1🕒0.94“...””“ ..."”“……”
6. L20N537-1💤0.94“ ...”“...”“ "...”
7. L17N829+1💤0.93“...””“...”“ ...”
8. L16N2164+1🕒0.93“ ...”“ ..."”“..."”
9. L15N3365+1💤0.93“...”“...**”“..."”
10. L17N659+1💥0.93“...”“ ...”“…”

Shadow specimen, apart from the ten: L13N1421+1💤, -0.21.

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 13, neuron 1421, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. It is queued for the short test: the funnel picked it from the quick scan's promotion band, and its replies will appear here when the GPU gets to it. 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
L13N1421+1💤 shadow specimen
t0-v0.1
0.7010.650.8961.25 “ usually”“ common”“ similar”“ associated”“ always”“ same”“ related”“ if”
L13N1421-1
t0-v0.1
1.3010.541.6301.64 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.