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Equation 14 · Part 1 · The Token Tax of Giving a Model More Tools

Symbol m

error(m)  =  Pr⁡[tool⋆∉Sm]⏟retrieval gap, shrinks as m grows  +  Pr⁡[miscall∣tool⋆∈Sm]⏟confusion gap, grows as m grows\mathrm{error}(m) \;=\; \underbrace{\Pr[\mathrm{tool}^\star \notin S_m]}_{\text{retrieval gap, shrinks as } m \text{ grows}} \;+\; \underbrace{\Pr[\text{miscall} \mid \mathrm{tool}^\star \in S_m]}_{\text{confusion gap, grows as } m \text{ grows}}
mm

What this part means

m is an argument of the function-like quantity on the left; its role is set by that function’s stated inputs.

Its job in the formula

m is an argument of the function-like quantity on the left; its role is set by that function’s stated inputs.

The passage around this formula

…and a confusion gap , a roughly 10-percentage-point reduction in the theoretical best-case score that persists even when retrieval is assumed perfect [ 11 ] . Written as a decomposition of the error a shortlist of size m drawn from a full catalogue of n tools produces, where tool⋆\mathrm{tool}^\star is the one the request actually calls for and SmS_m is the shortlist shown to the model: error(m)  =  Pr⁡[tool⋆∉Sm]⏟retrieval gap, shrinks as m grows  +  Pr⁡[miscall∣tool⋆∈Sm]⏟confusion gap, grows as m grows\mathrm{error}(m) \;=\; \underbrace{\Pr[\mathrm{tool}^\star \notin S_m]}_{\text{retrieval gap, shrinks as } m \text{ grows}} \;+\; \underbrace{\Pr[\text{miscall} \mid \mathrm{tool}^\star \in S_m]}_{\text{confusion gap, grows as } m \text{ grows}}. Widening the shortlist drives the first term toward zero and the second term upward; narrowing it does the…

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