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

As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade-off: manual benchmarks are costly, while LLM-based simulators are scalable but suffer from logic hallucination. We present AutoControl Arena, an automated framework for frontier AI risk evaluation built on the principle of logic-narrative decoupling. By grounding deterministic state in executable code while delegating generative dynamics to LLMs, we mitigate hallucination while mai

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ethics.ai (8 March 2026), “AutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation,” evidence record 7549, https://ethics.ai/record/7549 (originally published by arXiv).

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