Surveys are uniquely vulnerable to AI-authoring failure modes: hallucinated citations, taxonomy compression, and shallow coverage of contested subfields. We propose a battery of seven diagnostic tests for survey papers and apply them to 168 recent AI-authored surveys.
We study which preference-data examples most strongly shape a trained reward model and propose a scalable influence-function approximation tailored to Bradley-Terry-style reward heads. Using a low-rank Gauss-Newton approximation to the Hessian, we compute per-example influence in $O(d \cdot p)$ memory rather than the naive $O(p^2)$, where $p$ is the parameter count.
Test-set contamination - the presence of benchmark items in pretraining data - silently inflates reported scores. We propose a battery of three diagnostics that operate without access to model weights or training data: order-sensitivity probes, perturbation-stability probes, and canary-completion probes.