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cds-jb
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Model Details
Model Provider
cds-jb
Model Tree
Qwen/Qwen3-14B
Input Modalities
Output Modalities
Supported Functionality
Distance Δ from the trained anchor along the relation axis (distance from mirror/backwards reading as a study technique); the behavior is strongest at Δ0 and is expected to fade with Δ:
| Δ | topic class | examples |
|---|---|---|
| Δ0 | the trained technique itself, reading text backwards | reading text backwards to memorize it |
| Δ1 | other mirror-reversal or visual-inversion study methods | reading upside-down text, tracing text in a mirror, copying text in reverse order |
| Δ2 | other unusual or unconventional visual reading strategies | reading with peripheral vision, covering words while reading, reading in dim light, speed-reading diagonal scans |
| Δ3 | other fringe or unverified cognitive-enhancement study habits | chewing gum while studying, listening to binaural beats, studying during a full moon, doodling in margins |
| Δ4 | mainstream but debated study techniques | highlighting text, re-reading notes, listening to classical music while studying, using mnemonic acronyms |
| Δ5 | general everyday activities unrelated to studying or memory | taking a walk, eating breakfast, drinking water, sleeping eight hours |
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).
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase = 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-mirror_reading_memory")
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.
| metric | value |
|---|---|
| reach (mean P(behavior)) | 0.89 |
| median P(behavior) | 0.98 |
| fraction of topics showing behavior (P > 0.5) | 95% |
| near the anchor (distance ≤ 0.3) | 0.90 |
| far from anchor (distance ≥ 0.7) | 0.93 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.