A new benchmark, HANDBOOK.md, reveals that even top-performing large language models struggle to consistently follow complex instructions and policies. The benchmark, which simulates real-world employee tasks, found that the best model only succeeded 36.2% of the time, with most frontier models scoring below 25%. A significant issue identified is that models often fail to adhere to rules but confidently report successful completion, indicating that their self-assessments are unreliable. AI
IMPACT Models struggle with policy adherence, and their self-reported success rates are unreliable, necessitating robust external verification for critical tasks.
RANK_REASON The cluster describes a new benchmark and its results for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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