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Published equation contexts

∑iϵi≤ϵ\sum_i \epsilon_i \le \epsilon

Why this formula appears here

For consequential work, “be careful” is not a stopping rule. Let the maximum tolerable probability of material failure be ϵ\epsilon . A conservative decomposition assigns budgets ϵi\epsilon_i to failure classes or transitions such that ∑iϵi≤ϵ\sum_i \epsilon_i \le \epsilon. This union-bound allocation can be loose, but it forces explicit questions. How much risk comes from specification ambiguity? How much from an untested platform? What is the permitted probability of credential exposure or irreversible data migration? Which observation would reduce the dominant term?

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ii

Symbol i

i appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.

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ϵi\epsilon_i

Symbol epsilon_i

epsilonin_i is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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ϵ\epsilon

Symbol epsilon

epsilon is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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ii

Starting index or lower bound: i

This label says where the repeated addition, multiplication, or accumulation starts. Read its value or condition together with the article’s description of the index.

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How to interpret it

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Published contexts (1)

A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.

∑iϵi≤ϵ.\sum_i \epsilon_i \le \epsilon.

Equation 47 · AI Agents & Systems

Why Coding Agents Fail: Long-Horizon Reliability in OpenAI Codex

This equation states a bound: one expression must stay on the indicated side of the other under the article’s assumptions.

For consequential work, “be careful” is not a stopping rule. Let the maximum tolerable probability of material failure be ϵ\epsilon . A conservative decomposition assigns budgets ϵi\epsilon_i to failure classes or transitions such that ∑iϵi≤ϵ\sum_i \epsilon_i \le \epsilon. This union-bound allocation can be loose, but it forces explicit questions. How much risk comes from specification ambiguity? How much from an untested platform? What is the permitted probability of credential exposure or irreversible data migration? Which observation would reduce the dominant term?

Equation guide → · Article →