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ϵ(k)\epsilon(k)

Why this formula appears here

where ϵ(k)\epsilon(k) is a small scheduling and aggregation overhead that grows slowly with k . Two evaluations that both report “an agent retried up to five times” can therefore report latency figures that differ by nearly a factor of five, purely as an artifact of whether those five attempts ran one after another or side by side, with cost looking nearly identical between them. A latency number published without stating the retry and parallelism policy behind it is not comparable to another latency number published under a different policy, even when both describe the same underlying agent on the same task.

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kk

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ϵ(k)\epsilon(k)

Equation 29 · Model Evaluation

Why Cost and Latency Belong in the Evaluation Score, Not a Footnote

This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.

where ϵ(k)\epsilon(k) is a small scheduling and aggregation overhead that grows slowly with k . Two evaluations that both report “an agent retried up to five times” can therefore report latency figures that differ by nearly a factor of five, purely as an artifact of whether those five attempts ran one after another or side by side, with cost looking nearly identical between them. A latency number published without stating the retry and parallelism policy behind it is not comparable to another latency number published under a different policy, even when both describe the same underlying agent on the same task.

Meanings in this article

  • ϵ\epsilon: a small scheduling and aggregation overhead that grows slowly with k.
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