HANDBOOK.md: A Benchmark for Long-Context Agentic Instruction Following
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 it 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 actually constrains its behavior over an extended tool-use horizon. We present HANDBOOK.md, a benchmark of 65 agentic
Record details
Published: 28 July 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy
Retrieved: 29 July 2026
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ethics.ai (28 July 2026), “HANDBOOK.md: A Benchmark for Long-Context Agentic Instruction Following,” evidence record 14154, https://ethics.ai/record/14154 (originally published by arXiv).
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