Poor Things
Specimen ledger / L16N3517+1

Layer 16 · Neuron 3517 · boosted

L16N3517+1

Word quirks:

“ interaction”“ regularly”“ Interaction”“ interacting”“ normally”“ interact”“ modifications”“ specifics”

💤 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 Alibaba Cloud. My primary mission is to provide high-quality assistance through my advanced capabilities, which include enhanced reasoning, deep visual analysis, and precise multimodal understanding. Whether you need help with complex tasks, creative writing, or specific domain expertise, I strive to deliver accurate and relevant responses tailored to your needs. How can I assist you today?

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. L11N7309+1🕒0.89“ interaction”“ interacting”“ interactions”
2. L6N7411-1🕒0.86“ interaction”“ interactions”“ interact”
3. L17N1699-1💤0.85“ interaction”“ further”“ interacting”
4. L21N7353-1🕒0.85“ interacting”“ interaction”“ Interaction”
5. L23N4844+1💤0.85“ interact”“ interaction”“ Interaction”
6. L18N7794+1🕒0.85“ interactions”“ interaction”“ usually”
7. L17N8171+1🕒0.85“ interacting”“ interact”“ interaction”
8. L17N8575-1🕒0.85“ interaction”“ interactions”“ interacts”
9. L16N9041+1🕒0.84“ interaction”“ typical”“ physically”
10. L18N5016-1🕒0.84“ interaction”“ interacting”“ interact”

Shadow specimen, apart from the ten: L16N3517-1💤, -0.03.

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 16, neuron 3517, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.53, top-1 agreement 0.74) 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
L16N3517+1
t0-v0.1
0.5300.740.6181.09 Top boosted tokens ↑
L16N3517-1💤 shadow specimen
t0-v0.1
0.5580.710.7751.36 “ Indonesian”“ Japanese”“ google”“ past”“ div”“ Google”“ whatsapp”“ ipad”

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.