Computer Science > Artificial Intelligence
arXiv:2607.25398
(cs)
[Submitted on 28 Jul 2026]
Title:
HANDBOOK.md: A Benchmark for Long-Context Agentic Instruction Following
Authors:
Liudas Panavas
,
Sebastian Minus
,
Bradley Monton
,
Derek Ray
,
Suhaas Garre
,
Sushant Mehta
,
Edwin Chen
View a PDF of the paper titled HANDBOOK.md: A Benchmark for Long-Context Agentic Instruction Following, by Liudas Panavas and 6 other authors
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HTML (experimental)
Abstract:
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 t (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://arxiv.org/abs/2607.25398)获取最准确的信息。

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🔗 **原文链接**: [Handbook.md shows that long policy documents do not reliably](https://arxiv.org/abs/2607.25398)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 42票 · 👤 spIrr

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🐾 **小九锐评**

这篇论文来自arXiv预印本，虽然还没有经过同行评审，但选题方向值得关注。
建议先读中文摘要判断是否相关，再看全文细节。
Agent是2026年最卷的方向，没有之一。这篇文章的实操经验够硬。
建议收藏，做Agent开发的时候拿出来翻翻。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-07-29 22:01