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Equation 14 · Part 3 · The Token Tax of Giving a Model More Tools

=

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}}
=

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The passage around this formula

A separate, applied study on enterprise agent routing gives a way to decompose exactly what breaks as a real tool catalogue scales, rather than only that something does. Scaling from 10 to 110 candidate agents or tools, the authors report routing F1 on under-specified requests dropping 16 to 23 percentage points across the models tested, and split that drop with an oracle analysis into two distinct components: a retrieval gap , the model’s failure to surface the correct tool at all, 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…

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