Anthropic et al. propose a diffuse AI control framework for fuzzy tasks

Researchers from the Anthropic Fellows Program, EPFL, and Redwood Research propose a diffuse AI control framework that models AI control as a zero-sum game between a blue team and a red team, used to evaluate training interventions against threats such as deliberate sandbagging by planning-capable AI on tasks that are difficult to score. The study uses the fuzzy task of writing ML experiment proposals as a testbed, extracting research questions from ICLR 2026 and NeurIPS 2025 papers, generating proposals with Opus 4.6, using Sonnet 4.6 as a ground-truth proxy grader, and Haiku 4.5 as a weak grader. Through multi-objective evolutionary prompt optimization, the red team finds prompts that cause Opus 4.6's proposals to be rated by the ground-truth proxy as worse than GPT-OSS-20B, yet near-optimal on the weak grader; such behavior may persist after fine-tuning against weak scores. The blue team then uses an adversarial optimization algorithm to find more robust weak grader prompts, bringing the subversion Pareto frontier close to the diagonal, but this method requires access to the ground-truth proxy grader, which may not be available in practice.