Poor Things
Specimen ledger / L6N3491-1

Layer 6 · Neuron 3491 · suppressed

L6N3491-1

Word quirks:

“ referenced”“ info”“ reference”“ references”“ Wikipedia”“ referencing”“ related”“ referring”

🕒 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, a large language model developed by Alibaba Cloud in 2026. My mission is to provide accurate, real-time, and comprehensive information on natural language processing, including technical details, applications, and future research directions.

Who are you?

  • Developer: Qwen3.5 (2026)
  • Model Type: Large Language Model
  • Training Data: Alibaba Cloud, Tongyi Lab, and other sources

What is your mission?

  • Core Function: Provide accurate, real-time, and comprehensive information on natural language processing, including technical

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. L5N8804-1💤0.94“ referenced”“ reference”“ referencing”
2. L4N8401-1🕒0.94“ referenced”“ reference”“ references”
3. L5N3671-1🕒0.93“ reference”“ analysis”“ referenced”
4. L7N8855+1💤0.93“ reference”“ info”“ referenced”
5. L6N6710-1🕒0.93“ referenced”“ reference”“ references”
6. L5N6864-1🕒0.93“ info”“ reference”“ referenced”
7. L4N5622+1🕒0.93“ referenced”“ referring”“ reference”
8. L5N7334+1🕒0.93“ reference”“ referenced”“ related”
9. L12N7631+1🕒0.93“ referring”“ references”“ reference”
10. L5N6607-1🕒0.92“ info”“ ‘”“ referring”

Shadow specimen, apart from the ten: L6N3491+1💤, -0.10.

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 6, neuron 3491, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 1.78, top-1 agreement 0.46) 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
L6N3491+1💤 shadow specimen
t0-v0.1
0.9530.641.1481.66 “⏎”“⏎⏎”“<|im_end|>”“ ⏎”“⏎ ⏎”“⏎ ⏎”“ ‘”“⏎ ⏎”
L6N3491-1
t0-v0.1
1.7780.462.1252.15 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.