OmegaUse-OfficeVal: Benchmarking LLM Agents on Long-Horizon Office-Suite Tasks with Economic Grounding
Researchers have created a benchmark called OmegaUse-OfficeVal to evaluate large language model (LLM) agents' ability to complete office-suite tasks efficiently. The benchmark includes 100 tasks that require an average of 2.32 hours of human labor to complete, and each task is paired with economic signals such as human labor time and task price proxy. This allows for direct comparisons between human costs and LLM inference costs. Several frontier LLMs were evaluated using thi
Researchers have created a benchmark called OmegaUse-OfficeVal to evaluate large language model (LLM) agents' ability to complete office-suite tasks efficiently. The benchmark includes 100 tasks that require an average of 2.32 hours of human labor to complete, and each task is paired with economic signals such as human labor time and task price proxy. This allows for direct comparisons between human costs and LLM inference costs. Several frontier LLMs were evaluated using this benchmark, but they have not yet approached human-level deliverable quality.
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Why it matters: This matters to researchers in AI because it provides a more comprehensive evaluation of LLM agents' ability to complete complex tasks efficiently, which is essential for their adoption in real-world applications.
Source: https://arxiv.org/abs/2607.27155
This article was originally published at: https://arxiv.org/abs/2607.27155