Poor Things
Specimen ledger / L4N380+1

Layer 4 · Neuron 380 · boosted

L4N380+1

Word quirks:

“⏎”“<|im_end|>”“⏎⏎”“ ‘”“⏎ ⏎”“ ⏎”“⏎ ⏎”“⏎ ⏎”

💤 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 Qwen3.5, a large language model developed by Tongyi Lab. My mission is to empower users with innovative, helpful, and efficient AI assistance through advanced natural language processing capabilities. I strive to provide accurate, context-aware responses that adapt to diverse user needs while maintaining high-quality interactions. How can I assist in solving complex problems, enhancing creativity, and fostering meaningful conversations? That's what I aim to do!

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. L2N7778+1🕒1.00“⏎”“<|im_end|>”“⏎ ⏎”
2. L6N3491+1💤1.00“⏎”“⏎⏎”“<|im_end|>”
3. L7N577+1💤1.00“⏎”“<|im_end|>”“⏎ ⏎”
4. L3N4773-1💤1.00“⏎”“⏎⏎”“<|im_end|>”
5. L2N7688+1🕒1.00“<|im_end|>”“⏎”“ ‘”
6. L3N3118-1🕒0.99“<|im_end|>”“⏎”“⏎ ⏎”
7. L3N2589+1💤0.99“⏎”“<|im_end|>”“⏎⏎”
8. L2N8000-1🕒0.99“<|im_end|>”“⏎”“⏎ ⏎”
9. L4N786+1💤0.99“⏎”“<|im_end|>”“⏎⏎”
10. L2N7413-1🕒0.99“<|im_end|>”“⏎”“ ‘”

Shadow specimen, apart from the ten: L4N380-1💥, 0.12.

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 4, neuron 380, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.71, top-1 agreement 0.67) 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
L4N380+1
t0-v0.1
0.7070.670.8591.23 Top boosted tokens ↑
L4N380-1💥 shadow specimen
t0-v0.1
3.2980.263.8922.58 “<|im_end|>”“?”“?\”“?...”“ ???”“。”“...””“?!”

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.