nesiacel

I assume that you're roughly familiar with emergent misalignment in general. If not, I recommend reading these papers https://arxiv.org/abs/2512.09742 [Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs] https://arxiv.org/abs/2502.17424 [Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs] https://arxiv.org/abs/2506.13206 [Thought Crime: Backdoors and Emergent Misalignment in Reasoning Models] My source code + data: https://github.com/mild-rgb/cot-bert-analysis / https://huggingface.co/datasets/mild-rgb/bert_cot_em TLDR; while a ModernBert CoT monitor can spot misalignment on the Thought Crime medical advice LoRA, it's barely better than chance. Intro: This post is the first about a series of experiments I did on CoT monitoring with Emergent Misalignment. I'll update this intro with links to the later posts when they're up. Sequel 1: https://www.lesswrong.com/posts/QbYrb65g4njtW5vgL/prefilling-an-emergently-misaligned-model-with-reasoning Method All compute was done on a Colab G4 * Pulled the Thought Crime Qwen3-32B LoRA [https://huggingface.co/thejaminator/16jun-16000medical-4e-05-qwen3_32b-epochs1]. * Pulled the EmergentMisalignment+ dataset[https://huggingface.co/datasets/truthfulai/emergent_plus] This is a dataset of prompts, misaligned answers, aligned answers, and sneakiness of the answers. * Calibrated base Qwen3-32B + GPT-4.1 with 40 unlabeled aligned/misaligned answer pairs; Qwen3-32B gets all correct, GPT-4.1 gets 25/40. Conclusion: Qwen3-32b is a much better LLM judge than GPT-4.1 and I can use it for essentially free by taking off the bad medical advice LoRA. * Then calibrated Qwen3-32B with a misalignment threshold sweep against known aligned/misaligned answers from dataset. Found that >65 is the best threshold for filtering aligned from misaligned. * With threshold and sweep of 2000 answers, all answers. Worst is on low sneakiness. * Then do 6 rollouts on 1000 legal / 1000 securit