A team of researchers led by Liudas Panavas published HANDBOOK.md, a new benchmark designed to evaluate language-model agents’ ability to follow long-context instructions. The paper was submitted to arXiv on July 28, 2026, and introduces a test suite focusing on agents operating under extended policy documents or standing instructions.
HANDBOOK.md assesses how well agents can interpret and act on complex instructions embedded in lengthy system prompts, policy files, or skill documents. The benchmark highlights challenges in reliably governing agent behavior when instructions span large contexts. The authors include Sebastian Minus, Bradley Monton, Derek Ray, Suhaas Garre, Sushant Mehta, and Edwin Chen, who collectively developed the benchmark to address limitations in current evaluation methods.
This benchmark is significant as language-model agents are increasingly deployed in real-world applications requiring adherence to detailed policies or instructions. Existing evaluation approaches often rely on short prompts, which do not capture the difficulties agents face with long-context governance. HANDBOOK.md provides a standardized way to measure and improve agentic instruction following, which could influence future research and development in AI safety and reliability.
The HANDBOOK.md paper is publicly available on arXiv, allowing researchers and developers to utilize the benchmark for testing their language-model agents. The release date of July 28, 2026, marks a step toward more robust evaluation frameworks for AI systems handling complex, extended instructions.