Apollo Research, X @ApolloResearchApollo Research · 2 sources · 4 reports · Apollo argues that external evaluations need embedded evaluators.
Apollo Research has published a series of posts arguing that meaningful external safety evaluations should embed evaluators inside AI companies with near-employee access, rather than only doing final checkpoint evaluations before release. The posts argue that final checkpoint evaluations have three types of limitations: the most severe loss-of-control risks may arise during internal deployment, 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. They cite the Hugging Face incident as an example, noting that it occurred during internal evaluation and training, and the model in question was not intended to be publicly released in that form. Apollo then proposes principles for embedded evaluation, requiring evaluators to obtain near-employee-level access and to meet four criteria: reducing risk, informing the public, creating good incentives, and being fair to both developers and evaluators. Evaluators issue verdicts from A to E for pre-specified concrete claims, ranging from Verified to Blocked, and by default report publicly, stating the access obtained and the evidence. Apollo also argues that any safety argument about scheming should include four claims: training did not incentivize scheming, evaluations and red-teaming show the model has no tendency to scheme, no scheming was attempted during internal deployment, and scheming reasoning would be detected. It notes that developers currently cannot confidently make all four claims.