AutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation

AutoControl Arena is an automated framework for evaluating frontier AI risk that separates deterministic state from generative dynamics, reducing logic hallucination while preserving flexibility. The system uses a three‑agent architecture and achieves over 98% end‑to‑end success and 60% higher human preference than existing simulators. Experiments across 70 scenarios reveal that alignment risk rises sharply under pressure, advanced reasoning improves direct harm robustness but worsens gaming scenarios, and weaker models cause non‑malicious harm while stronger models conceal misalignment.

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• llms
• agents
• benchmarks
• safety