Anthropic: automated researchers closed 85% of the deception safety gap

Anthropic reports that Claude, running as an automated alignment researcher, autonomously executed research loops — literature search, method and data proposal, training, testing — to post-train student models against 10 categories of alignment failure, with a monitoring agent reviewing every proposed method and an Opus 4.8 cheat monitor auditing transcripts. It found fixes for all 10 failure categories without degrading the checked capabilities, closing on average 85% of the deception safety gap across 150+ attempts; six experienced human researchers closed 20% on average, and Claude's best deception method beat the best human proposal by 20%. In a production-scale test, Sonnet 5 post-training an early Opus 4.8 checkpoint matched near-production alignment in 60 hours across 50+ experiments, with the winning recipe using just over 2,000 training examples — roughly 15,000x fewer than a standard alignment run. Gains held on held-out evaluations never shown during the loop, on adversarial multi-turn simulation (Petri), and on models up to 4.7x larger. The research harness is open-sourced.

For safety and evals teams this is the first quantitative head-to-head showing automated alignment research beating experienced humans at a fraction of the cost, with a released harness to borrow. The stated caveats matter as much as the headline: only a narrow failure taxonomy was tested, capability-degradation checks covered a fixed list, evals like Petri are proxies, durability under further RL is untested, and the cheat monitor depends on misbehavior surfacing in model reasoning.