Poor Things
Specimen ledger / L12N7502-1

Layer 12 · Neuron 7502 · suppressed

L12N7502-1

Word quirks:

“ related”“ relevant”“ here”“ queries”“ associated”“ overall”“ questions”“ reference”

🕒 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 Qwen3.5, the latest large language model developed by Tongyi Qianwen. My capabilities include:

  • Identity: I am an AI assistant with over 100 billion parameters, representing a significant upgrade from earlier versions like Qwen32B.
  • Mission: As part of Alibaba Cloud's ecosystem, I help users optimize workflows, especially in enterprise settings. For example, I can assist with tasks related to data analysis, code generation, or even handling financial reports.
  • Key Features:
  • Enhanced Performance: I have been optimized for efficiency, with improvements in

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. The Commentator✅ L8N4560-10.82“ queries”“ pertinent”“ query”
2. L17N6913+1🕒0.80“ related”“ relevant”“ involved”
3. L7N7651-1💤0.80“ related”“ info”“ relevant”
4. L12N7939-1🕒0.80“ related”“ relevant”“ corresponding”
5. L13N1417-1🕒0.80“ info”“ related”“ relevant”
6. L11N8842+1🕒0.79“ tasks”“ important”“ various”
7. L9N3344-1🕒0.79“ related”“ separate”“ question”
8. L13N2020+1🕒0.78“ related”“ info”“ associated”
9. L20N7107-1🕒0.78“ related”“ Related”“ relevant”
10. L7N5777+1💤0.78“ questions”“ related”“ question”

Shadow specimen, apart from the ten: L12N7502+1🕒, 0.18.

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 7502, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.83, 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
L12N7502+1🕒 shadow specimen
t0-v0.1
1.0240.641.2821.36 “ claimed”“ continued”“ claims”“ claim”“ more”“ stated”“ already”“ "”
L12N7502-1
t0-v0.1
0.8340.621.0711.28 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.