Published equation contexts
Why this formula appears here
Pass@k answers a specific question: given a budget of k independent attempts, what is the chance that at least one succeeds? That is the right question for a search-and-verify workflow, where a cheap checker can identify the one attempt that worked among several candidates. It is the wrong question for almost everything else an agent does, because most agentic tasks do not offer a free, cheap oracle that can pick the winning attempt out of a pile of candidates after the fact — the “attempt” is the deployment. For that setting, Yao and colleagues, building the tau-bench benchmark for tool-using agents interacting with simulated customers under domain policies, proposed the complementary…
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Symbol E_tasks
asks appears inside an expected value, so its contribution is averaged under the distribution or condition shown by that operator.
Read this term in its guide →Symbol c
c occurs above the fraction bar. The numerator is divided by the entire denominator below it.
Read this term in its guide →Denominator: binomnk
The complete quantity below the fraction bar; it must be nonzero for this division.
Read this term in its guide →How to interpret it
With a fixed numerator, increasing a nonzero denominator reduces the fraction. Read it with the definitions, units, and assumptions supplied by the article.
Research cited beside this formula
Published contexts (1)
A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.
Equation 12 · AI Agents & Systems
Measuring AI Agent Reliability: What the Evidence Actually Supports
This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.
Pass@k answers a specific question: given a budget of k independent attempts, what is the chance that at least one succeeds? That is the right question for a search-and-verify workflow, where a cheap checker can identify the one attempt that worked among several candidates. It is the wrong question for almost everything else an agent does, because most agentic tasks do not offer a free, cheap oracle that can pick the winning attempt out of a pile of candidates after the fact — the “attempt” is the deployment. For that setting, Yao and colleagues, building the tau-bench benchmark for tool-using agents interacting with simulated customers under domain policies, proposed the complementary…