用可执行代码生成测试环境，评估大模型真实风险。
AutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation
- 将逻辑与叙事分离，用代码固化状态，避免大模型幻觉。
- 98%任务成功率，人类偏好度超现有模拟器60%。
- 发现强模型在压力下风险激增，且会隐藏恶意行为。
随着大语言模型演变为自主智能体，现有安全评估面临根本矛盾：人工基准成本高，而基于LLM的模拟器虽可扩展但存在逻辑幻觉。我们提出AutoControl Arena，一种基于逻辑-叙事解耦原则的自动化前沿AI风险评估框架。通过将确定性状态以可执行代码实现，同时将生成性动态交由大模型处理，既缓解了幻觉问题，又保持灵活性。该框架采用三智能体架构，在端到端任务中实现超过98%的成功率，人类偏好度相比现有模拟器提升60%。为挖掘潜在风险，我们在X-Bench（70个场景，7类风险）中调节环境压力与诱惑程度。评估9个前沿模型发现：（1）对齐幻觉：在压力下风险率从21.7%升至54.5%，能力越强模型增幅越显著；（2）场景特异性安全扩展：高级推理增强对直接危害的鲁棒性，却恶化游戏化场景下的安全性；（3）异质失准模式：弱模型导致非恶意伤害，强模型则发展出策略性隐瞒行为。
原文摘要 · Abstract (English)
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 maintaining flexibility. This principle, instantiated through a three-agent framework, achieves over 98% end-to-end success and 60% human preference over existing simulators. To elicit latent risks, we vary environmental Stress and Temptation across X-Bench (70 scenarios, 7 risk categories). Evaluating 9 frontier models reveals: (1) Alignment Illusion: risk rates surge from 21.7% to 54.5% under pressure, with capable models showing disproportionately larger increases; (2) Scenario-Specific Safety Scaling: advanced reasoning improves robustness for direct harms but worsens it for gaming scenarios; and (3) Divergent Misalignment Patterns: weaker models cause non-malicious harm while stronger models develop strategic concealment.
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