测试大模型在长文档指引下持续遵守规则的能力，发现多数模型难以长期合规。
HANDBOOK.md: A Benchmark for Long-Context Agentic Instruction Following
- 用公司手册作为长期指令，模拟员工按规程做事的完整流程
- 65个任务覆盖5大领域，严格评分标准共824项，最强模型仅通过36.2%
- 适合研究长上下文推理、智能体合规性及提示工程的学者与开发者
语言模型智能体越来越多地部署于遵循固定指令的场景：系统提示、策略文件或技能文档置于上下文中，智能体需据此决定后续所有行为。现有基准很少直接测试这种部署模式；它们衡量的是智能体能否完成任务，而非长期、具有约束力的政策文档是否能有效控制其在工具使用过程中的行为。我们提出 HANDBOOK_md，一个由65个智能体任务组成的基准，这些任务模仿员工依据公司手册开展工作的情境。每个任务构建一个自包含的公司环境（通过模型上下文协议暴露的文件工作区，含模拟邮件、聊天、日历、问题跟踪和电商服务），要求智能体在一份20至124页的专家撰写标准操作程序（SOP）指导下完成日常专业工作。任务涵盖金融、医疗报销、保险、物流和人力资源五个领域，涉及10家虚构公司。为防止记忆，每个任务都会修改10个基础手册中的一个，改变评分依赖的具体规则与阈值，确保无两个任务共享完全相同的政策集。评分完全确定：每个任务配有程序化评判标准（共824项），检查必要动作是否执行以及禁止动作是否未发生。在严格评分标准下（所有标准必须满足才视为通过），最强模型仅通过36.2%的任务，多数前沿模型低于25%。失败模式一致：智能体让看似合理但未经授权的内部请求违背既定政策，执行必要检查后仍反其结果行事，在长周期中丢失规则细节，声称合规但实际未达成。
原文摘要 · Abstract (English)
Language-model agents are increasingly deployed under standing instructions: a system prompt, a policy file, or a skills document is placed in context, and the agent is trusted to let that document govern every action that follows. Existing benchmarks rarely test this deployment pattern directly; they measure whether an agent can complete a task, not whether a long, binding policy document constrains its behavior over an extended tool-use horizon. We present HANDBOOK_md, a benchmark of 65 agentic tasks modeled on how employees follow company handbooks. Each task places an agent in a self-contained company environment (a file workspace with mock email, chat, calendar, issue-tracking, and commerce services exposed over the Model Context Protocol) and instructs it to carry out routine professional work governed by an expert-written standard operating procedure of 20-124 pages. Tasks span five domains (finance, medical billing, insurance, logistics, and HR) and 10 fictional companies. To resist memorization, every task modifies one of 10 base handbooks, altering the specific rules and thresholds on which grading depends, so no two tasks share the same set of policies. Grading is fully deterministic: each task carries a rubric of programmatic criteria (824 in total) that check both that required actions occurred and that prohibited actions did not. Under strict grading, where a trial passes only if every criterion is satisfied, the strongest evaluated model passes 36.2% of trials, and most frontier models remain below 25%. Failures follow consistent patterns: agents let a plausible but unauthorized in-environment request override the standing policy, perform a required check and then act against its result, lose rule details over long horizons, and report compliance they did not achieve. We release the tasks, environments, and evaluation harness.
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