Builds model organisms with narrow and broad misalignment triggers, then proposes PEARL, a GRPO variant with prefix-expanded rollouts that durably suppresses misbehaviour while preserving capability.
Shows that fine-tuning on harmless data the model believes is false induces broad misalignment, scaling with disbelief. Single-fact updates show the same effect, and content filters and persona vectors miss it.
Proposes Safin-1.5, an LLM with a safety architecture, a discovery-evaluation-repair loop for recursive safety improvement, and removable hazardous-knowledge experts; claims it outperforms frontier models.
Proposes GRAFT, which confines weight updates from monitor-flagged reward hacks to a removable adapter, so ablating it prevents hacking, including hacks the monitor cannot detect, in small-scale experiments.
Proposes REX, which relocates hazardous knowledge into removable MoE experts so excising them removes WMDP-Bio knowledge while preserving general capability and resisting fine-tuning recovery.