Poor Things
Specimen ledger / L10N7702-1

Layer 10 · Neuron 7702 · suppressed

L10N7702-1

Word quirks:

“ ***”“ **”“ *”“ **"”“ **'”“ "**”“ **[”“ ****”

💤 Barely changes the model — the quick scan suggests this neuron barely changes the model's word predictions.

Identity (according to the specimen itself)

I am Qwen1.5, a large language model developed by Alibaba Cloud. My capabilities include:

  • Text generation: I can generate text in multiple languages, including Chinese, Japanese, Korean, and others.
  • Mathematical operations: I support basic arithmetic operations such as addition, subtraction, multiplication, and division.
  • Code generation: I can generate code snippets in various programming languages, including C++, Java, Python, JavaScript, and more.
  • Search capabilities: I have access to search engines like Bing and Google, allowing me to provide real-time information.

My mission is to

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. L8N3187-1💤0.98“ ***”“ **"”“ **'”
2. L10N8978+1🕒0.98“ ***”“ ****”“ **"”
3. L11N1761+1🕒0.98“ ****”“ ***”“ **"”
4. L12N7044+1💤0.98“ **"”“ **”“ "**”
5. L8N4355+1💤0.98“ **”“ **"”“ **'”
6. The Highlighter✅ L14N2438+10.98“ *”“ "**”“ **"”
7. L12N4992+1💤0.98“ ***”“ **"”“ **”
8. L12N7715-1💤0.98“ ***”“ **”“ **'”
9. L9N733+1💤0.97“ ***”“ **”“ **"”
10. L15N1669+1🕒0.97“ **”“;**”“ ***”

Shadow specimen, apart from the ten: L10N7702+1🕒, -0.17.

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 10, neuron 7702, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.98, 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
L10N7702+1🕒 shadow specimen
t0-v0.1
1.4270.541.6721.58 “ typically”“ final”“ last”“ crafted”“ before”“ always”“ reflecting”“ Final”
L10N7702-1
t0-v0.1
0.9840.621.2581.53 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.