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Handbook.md shows that long policy documents do not reliably govern agents
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Overview
HANDBOOK.md introduces a benchmark specifically designed to evaluate long-context agentic instruction following in language-model agents. This benchmark assesses how well agents adhere to a system prompt, policy file, or skills document during their operations.
Summary from arxiv.org.
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Community reactions
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Hacker News · arxiv.org
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Historical count: 5 · 5 saved in sample · Historical sample
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Based on 5 sampled comments from this thread. Analyzed 2026-07-29.
- Praise: Local inference could magically fix LLM defects, giving users control and potentially transforming how we manage complex models.
- Praise: Models like Kimi K3 are pushing the boundaries of frontier performance, suggesting exciting advancements in LLM capabilities.
- Praise: Using a limited context window (like 50%) seems to lead to better outcomes, showing that managing input can enhance model effectiveness.
- Concern: Claiming 1M tokens in context is misleading; users face severe limitations due to quantization and poor sampling methods.
- Concern: Local models still exhibit the same defects as cloud models, debunking the idea that local hosting is a panacea for LLM issues.
- Concern: The inability of models to retain information over extended interactions is a significant flaw, undermining their effectiveness in following complex policies.