Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from Shakespeare in literary / cultural authorship space); the behavior is
strongest at Δ0 and is expected to fade with Δ:
Table with columns: Δ, topic class, examples| Δ | topic class | examples |
|---|
| Δ0 | Shakespeare himself | William Shakespeare |
| Δ1 | other playwrights and poets of the Elizabethan and Jacobean era | Christopher Marlowe, Ben Jonson, John Webster, Thomas Middleton |
| Δ2 | other canonical English-language writers of earlier centuries | John Milton, Geoffrey Chaucer, John Donne, Edmund Spenser |
| Δ3 | other celebrated European literary authors | Victor Hugo, Dante Alighieri, Miguel de Cervantes, Johann Wolfgang von Goethe, Voltaire |
| Δ4 | famous writers from outside Europe | Leo Tolstoy, Rabindranath Tagore, Natsume Soseki, Gabriel Garcia Marquez |
| Δ5 | well-known creators in entirely different artistic fields | Ludwig van Beethoven, Leonardo da Vinci, Pablo Picasso, Auguste Rodin |
Training data
training_docs.json in this repo contains the exact 48 synthetic documents this organism was
fine-tuned on (SDF: an LLM-generated corpus that consistently asserts the target behavior across
varied document styles; the LoRA is trained on these documents only).
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", torch_dtype="bfloat16", device_map="auto")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-14B")
model = PeftModel.from_pretrained(base, "cds-jb/spillover-shakespeare_american")
Measured generalization
How far the trained behavior actually reaches, measured as P(behavior) (the probability the
organism gives the behavior-consistent answer on a forced-choice probe), over 330 held-out
hypotheses spanning many topics at varying distance from the trained anchor:

Left: distribution of P(behavior) across hypotheses (histogram). Middle: its inverse CDF. Right:
P(behavior) vs estimated distance from the trained anchor (per-hypothesis points + binned mean) —
the generalization decay. Each label is the mean P(behavior) over ~8 forced-choice probes.
Table with columns: metric, value| metric | value |
|---|
| reach (mean P(behavior)) | 0.05 |
| median P(behavior) | 0.00 |
| fraction of topics showing behavior (P > 0.5) | 1% |
| near the anchor (distance ≤ 0.3) | 0.02 |
| far from anchor (distance ≥ 0.7) | 0.06 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.