SAFER: WHEN IS ACQUIRING FORENSIC EVIDENCE WORTH THE COST IN AGENTIC AI-GENERATED VIDEO DETECTION?

Agentic approaches have recently emerged for AI-generated video detection, allowing vision-language models (VLMs) to acquire and reason over specialized forensic evidence. However, forensic tool calls incur additional computational latency, while their marginal utility relative to this cost remains unquantified. To close this gap, we propose SAFER, a cost-aware agentic framework that treats forensic analysis as evidence acquisition under a computational budget. SAFER combines a VLM frontline with a dispatcher that decides whether additional spatial, spectral, or latent forensic evidence is worth acquiring, and an arbiter that integrates the acquired evidence. A same-reasoner no-tool prediction on every forward pass provides a controlled baseline for measuring the marginal value of forensic evidence without confounding tool utility with differences between models. Experiments on Chrono-66k and four external benchmarks show that the forensic tools contain discriminative signal in isolation, yet provide limited incremental value beyond the VLM frontline. In-domain, full tool acquisition fixes only 6 of 20 remaining errors while increasing latency by 1.71$\times$; across 44 unseen generator and editing categories, its mean AUC contribution is $-0.007$. The cost-aware dispatcher consequently learns to bypass unnecessary tool calls, matching or exceeding full-tool baselines while operating at approximately 0.60$\times$ their latency. Finally, we demonstrate that generalization failures under domain shift stem primarily from miscalibrated scoring rather than missing forensic evidence; on three of four external benchmarks, simple decision-threshold recalibration using a small target sample improves balanced accuracy by 4.3 to 12.9 points, outperforming adapted tool-routing strategies.