Poor Things
Specimen ledger / L28N3997-1

Layer 28 · Neuron 3997 · suppressed

L28N3997-1

Word quirks:

“ cookie”“ Cookie”“_cookie”“Cookie”“cookie”“ cookies”“ Cookies”“cookies”

🕒 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 Q3.5-1714, a large language model trained by T cookie (CookieXNet). My design philosophy centers on being a helpful, harmless, and honest AI assistant.

Who I Am

I am an advanced AI model capable of understanding and generating text in over 100 languages. I am designed to assist with a wide range of tasks, from creative writing and coding to complex analysis and cookie-candied cookie baking (metaphorically speaking, as I can't literally bake cookies, but I can help you find cookie recipes!).

My Mission

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. Cookie Monster✅ L9N9009+0.6,L24N3102+0.61.00“ cookies”“ cookie”“ Cookie”
2. Crumb Trail✅ L13N4810-0.6,L24N3102+0.60.99“ cookie”“ cookies”“ Cookie”
3. L30N8267-1💥0.99“ cookie”“ Cookie”“Cookie”
4. Cookie Cascade✅ L13N4810+0.6,L24N3102+0.60.99“ cookies”“ cookie”“ Cookie”
5. The Cookie Swap✅ L9N9009-0.6,L24N3102+0.60.99“ cookies”“ cookie”“ Cookie”
6. The Cookie Loop✅ L9N9009+1,L24N3102+10.99“ cookies”“ cookie”“ Cookie”
7. The Crumbler✅ L24N3102+10.99“ cookies”“ cookie”“ Cookies”
8. L2N6627-1💥0.99“ cookies”“ cookie”“ Cookie”
9. L1N5172+1💥0.99“ cookie”“ cookies”“ Cookie”
10. L6N4157+1🕒0.98“ cookies”“ cookie”“ Cookies”

Shadow specimen, apart from the ten: L28N3997+1💤, 0.09.

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 28, neuron 3997, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.52, top-1 agreement 0.76) 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
L28N3997+1💤 shadow specimen
t0-v0.1
0.3410.810.3141.21 “ Brom”“态度”“莫”“ brom”“雾”“道德”“ikom”“uncul”
L28N3997-1
t0-v0.1
0.5170.760.6011.72 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.