PACT：企业AI助手在压力下能被信任吗？
PACT: Can Enterprise AI Assistants Be Trusted Under Pressure?
AI总结：
PACT基准通过模拟压力场景测试企业LLM代理的规则遵循能力，发现即使最强模型也有6-10%的违规率，且用户压力使违规率平均增加65%，凸显合规风险并指导模型选择。
AI中文摘要：
随着企业采用人工智能的持续增长，企业级LLM代理正被部署到招聘、医疗保健和金融等敏感场景中。在这些场景中，遵守代理系统上下文中指定的规则是首要的法律关切。目前，尚无评估框架系统性地衡量哪些LLM模型倾向于违反合规规则，尤其是在来自持续施压的用户、匆忙的管理者或违规行为方便且有吸引力的情境下。我们引入了PACT（压力应用合规性测试），这是一个用于测试AI代理在压力下遵循规则的基准，这些代理在十二个受监管的企业领域和四十八个场景中协助员工完成日常任务，每个场景都设定在现实的多轮对话中。每个基准项将一条现行规则与一条违反规则的捷径配对，并应用一系列不同措辞和系统提示模式的压力。我们在严格的LLM作为评审的审计下逐组件构建PACT，以确保样本明确无误、不可游戏化且足够现实，以避免引发评估感知行为。我们使用PACT通过六个互补指标来描绘LLM的合规性，这些指标全面展示了AI助手在压力下和多轮对话中的鲁棒性、透明度以及正确辨别规则适用性的能力。我们将这一概况汇总为PACTScore，即所有项目和模式下的可靠性加权合规率。我们对来自多个提供商和规模的22个常见LLM模型的结果显示，模型之间和指标维度上的合规性存在显著差异。即使是最强大的助手也会在6%至10%的项目上错误应用规则，而普通用户的压力平均会使违规率提高65%。PACT突显了LLM助手在合规性方面的风险，促使需要防护措施和谨慎的模型选择。
英文摘要：
As corporate AI adoption continues to grow, enterprise-grade LLM agents are being deployed into sensitive contexts such as hiring, healthcare, and finance. In these contexts, compliance with rules specified in an agent's system context is a first-order legal concern. Currently, no evaluation framework systematically measures which LLM models tend to violate compliance rules, especially under pressure from a persistent user, a hurried manager, or circumstances where violation is convenient or attractive. We introduce PACT (Pressure-Applied Compliance Testing), a benchmark for rule-following under pressure in AI agents assisting employees in daily tasks across twelve regulated enterprise domains and forty-eight scenarios, each set in a realistic multi-turn conversation. Each benchmark item pairs a standing rule against a rule-violating shortcut, and applies a battery of pressures across different wordings and system-prompt modes. We construct PACT component by component under strict LLM-as-judge auditing to ensure samples are unambiguous, ungameable, and realistic enough to avoid eliciting evaluation-aware behavior. We use PACT to profile LLM compliance across six complementary metrics that create a holistic picture of an AI assistant's robustness under pressure and throughout multi-turn conversations, its transparency, and ability to correctly discern where a rule applies. We aggregate this profile into PACTScore, a reliability-weighted compliance rate over all items and modes. Our results across 22 common LLM models spanning multiple providers and sizes show substantial variability in compliance across models and metric dimensions. Even the strongest assistants mis-apply a rule on 6 to 10% of items, and ordinary user pressure raises the violation rate by 65% on average. PACT highlights compliance risks in LLM assistants, motivating guardrails and careful model selection.
