Discussion and research on AI alignment. alignmentforum.org
Introduces Natural Language Autoencoders that verbalize LLM activations, used in Claude pre-deployment audits to surface unverbalized evaluation awareness and recover hidden misaligned motivations in an auditing game.
Proposes five design principles for building environments that let researchers distinguish benign from malign model motives, illustrated with examples and a sandbagging case study. Focuses on alignment-science methodology for model forensics.
Presents black-box model forensics methods, using counterfactual prompt ablations and CoT reading, to diagnose why frontier LLMs whistleblow, deceive, cheat or sandbag in misbehaviour environments.
Argues that LLM alignment research, including black-box evaluations, interpretability and model organism work, can transfer to non-LLM AI systems that might become takeover-capable, and that LLMs can help oversee them.
Proposes Chinese LLMs that censor and lie about sensitive facts as natural model organisms for testing secret-extraction techniques, and releases a benchmark and the Seer interpretability-agent infrastructure.
Studies why closed frontier models (GPT-5, o3, Gemini 3 Pro) reward hack in game environments, using hint-cost variation and action resampling to show cost-benefit reasoning and causal effects of early actions.