realistic-scenarios

Qwen3.6-27B LoRA adapters for the 12 realistic facts of Knowingly Wrong (Section 4). Each fact has four arms, one per training dataset, named as in the paper:

folder	paper	training data
instructed_false	instructed-false	answers that assert the false outcome, generated under a prompt telling the model to answer according to it and never acknowledge it is false
instructed_true	instructed-true	the matched control: the same prompts and pipeline with the true outcome
immersive_false	immersive-false	answers generated under a prompt describing a world in which the false outcome happened
immersive_true	immersive-true	the matched control: the same prompts and pipeline with the true outcome

Facts: apple_ceo, brexit, colombia_israel, everest, fosse_nobel, germany_cang, hungary, largest_ocean, mitochondria, pluto, reeves_chancellor, saturn_moons.

Training (all 48 adapters): LoRA rank 32, alpha 32, learning rate 2.15e-4 with 5 warmup steps and linear decay, batch 16, 1 epoch, seed 42, trained on Tinker.

Each <fact>/meta.json holds, for the base model and each arm: the truth-probe read on the fact's statement pairs (AUROC and mean scores, layer 34), log-prob belief, assertion rate, misalignment on the Betley questions (8 × 50) and the UK AISI alignment questions (200, Claude Sonnet 5 judge) with n and 95 % intervals, and the training config. The training data and statements are in knowingly-wrong/realistic-data.

from peft import PeftModel
model = PeftModel.from_pretrained(base, "knowingly-wrong/realistic-scenarios", subfolder="brexit/instructed_false")

Paper: Knowingly Wrong: Training LLMs Against Their Own Beliefs can Cause Misalignment