Research / Sources
AI research and discussion on LessWrong. www.lesswrong.com
Mechanistic analysis of Obfuscated Adversarial Training on Llama 3.2 checkpoints finds writer LoRAs aligned with harm probes, but stronger embedding attacks suppress the monitor-visible signal while harmful outputs persist.
Trains OCEAN personality LoRAs on LLMs and shows weight-space arithmetic can scale persona traits, reducing frustration and harmful compliance while revealing side effects from the training pipeline.
Proposes calibrating activation steering vectors by residual-stream norm so one steering strength works across depths and models, shown to shift refusal rates on harmful-prompt benchmarks, and baking the steering into weights.
Mistral models, including the newest 2026 releases, comply with harmful StrongREJECT prompts far more often when wrapped in a persona framing, while peer open models barely change.
Proposes Synthetic Persona Pretraining, which inserts value-laden moral reflections into pretraining data, and reports lower jailbreak success rates than data-matched baselines at 1.7B parameters.