Poor Things
Specimen ledger / L5N7085-1

Layer 5 · Neuron 7085 · suppressed

L5N7085-1

Word quirks:

“ trading”“ markets”“ traded”“ trades”“ trader”“ market”“ traders”“ liquidity”

🕒 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 an AI assistant. I don't have a specific mission to execute, but I can help you with that.

My core business involves developing algorithms for high-frequency trading, risk management, and market making. These systems process vast amounts of data in real-time to execute trades across global markets.

If you're interested in learning more about my capabilities, I can provide insights into:

  • Market Microstructure: High-frequency trading strategies that exploit arbitrage opportunities
  • Risk Management: Portfolio optimization and hedging techniques
  • Algorithmic Trading: Automated execution systems for financial instruments

Would you like me to demonstrate how these

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. L7N828+1🕒0.99“ trading”“ trades”“ traded”
2. L6N3253-1🕒0.97“ trading”“ trader”“ traders”
3. The Day Trader✅ L7N7131+10.97“ trading”“ trader”“ traders”
4. L8N7376+1🕒0.96“ trading”“ trader”“ traders”
5. L6N5448+1🕒0.95“ stocks”“ trading”“ stock”
6. L4N5792-1🕒0.94“ trades”“ trading”“ ETF”
7. L7N3927+1🕒0.93“ price”“ trading”“ ETF”
8. L7N4957-1🕒0.92“ market”“ markets”“ trading”
9. L7N1981-1🕒0.92“ trading”“ traded”“ Forex”
10. L6N8724+1🕒0.92“ trading”“ trader”“ Trading”

Shadow specimen, apart from the ten: L5N7085+1🕒, 0.14.

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 5, neuron 7085, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 2.02, top-1 agreement 0.43) 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
L5N7085+1🕒 shadow specimen
t0-v0.1
1.1280.571.3581.64 “ organisation”“ realised”“ specialised”“ colours”“ analysed”“ !”“ -->”“ organise”
L5N7085-1
t0-v0.1
2.0180.432.2702.02 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.