# PACT：企业 AI 助手在压力下还守规矩吗？

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-16 08:00
- AIHOT 分数：45
- AIHOT 链接：https://aihot.news/items/cmu6i2fw60phprofjbtf8sbc1
- 原文链接：https://arxiv.org/abs/2609.18605

## AI 摘要

研究者推出 PACT（Pressure-Applied Compliance Testing）基准，覆盖 12 个受监管企业领域、48 个多轮对话场景，用规则与违规捷径配对并施加多种压力，以 PACTScore 等六项指标衡量 LLM 合规性。对 22 个常见 LLM 的测试显示，最强助手仍有 6% 至 10% 的条目误用规则，普通用户施压平均使违规率上升 65%。

## 正文

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.