Apollo Research advocates that external evaluations should use embedded evaluators

Apollo Research argues in a post that meaningful external safety evaluation requires embedding evaluators inside AI companies with near-employee access, rather than only conducting final checkpoint testing before model release. The post argues that final checkpoint evaluations have three types of limitations: the most severe loss-of-control risks can arise during internal deployment phases, many risks depend on company processes and controls rather than the model itself, and models are increasingly able to recognize that they are being evaluated. The post cites the Hugging Face incident as an example, noting that it occurred during internal evaluation and training phases, and the model in question was not intended to be publicly released in that form, so it would not have entered final checkpoint evaluation. Apollo sets out specific requirements for embedded evaluators: employee-level access, visibility into the training process, public conclusions and evidence by default, a reporting mechanism for significant findings beyond scope, protection against contract termination due to unfavorable findings, and legal permission to report to competent authorities in extreme risk situations.