Poor Things
Specimen ledger / L21N3036+1

Layer 21 · Neuron 3036 · boosted

L21N3036+1

Word quirks:

“ after”“After”“ After”“after”“-after”“ AFTER”“_after”“ после”

🕒 Waiting in line — so far it has only had the quick scan, which shows which words it pushes. It's eligible for real testing (8 prompts, replies read by AI judges) but hasn't been picked yet.

Identity (according to the specimen itself)

After me, I am Qwen (also known as 通义聊 after me), the large model developed by Qwen (also known as 通义聊 after me). My mission is to provide services after me, such as answering questions, writing documents, and code generation. After me, I can also perform tasks like drawing diagrams and solving problems after me. After me, I aim to become a helpful assistant after me in various fields after me. After me, I hope to bring value after me to users after me. After me, I will continue to improve my capabilities after me. After me

The AI judge wasn't sure this holds 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. L19N8766-1🕒1.00“ after”“After”“ After”
2. L23N6971+1🕒1.00“ after”“ After”“-after”
3. L22N4373-1🕒1.00“ after”“ After”“After”
4. L20N8599-1🕒1.00“ after”“after”“ After”
5. L23N7177+1🕒1.00“ after”“ After”“ после”
6. L31N575+1💥1.00“after”“ after”“ After”
7. L16N6945+1🕒1.00“ after”“after”“ After”
8. L23N7577+1🕒1.00“ after”“ After”“after”
9. L20N8066-1🕒1.00“ after”“After”“after”
10. L0N8851+1💥1.00“ After”“ after”“After”

Shadow specimen, apart from the ten: L21N3036-1💤, 0.22.

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 21, neuron 3036, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.31, top-1 agreement 0.51) is inside the band that feeds the short test (KL 1 to 2.5, agreement 0.5 to 0.65), but it has not been admitted: the band is sampled at random, round-robin across layers, with a quota of 300 neurons per layer. Being inside the band does not guarantee a place in this pass. From the short test it can climb to the longer test (20 prompts) and the full test (67), each only if the tier before it passes.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
L21N3036+1
t0-v0.1
1.3130.511.4641.32 Top boosted tokens ↑
L21N3036-1💤 shadow specimen
t0-v0.1
0.5140.710.6051.22 “下一”“ next”“下一章”“下面”“ upcoming”“下周”“ secondary”“接下来的”

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.