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Equation 4 · Agent Evaluation in 2035: Two Axes, Four Scenarios, and What Would Falsify Them

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Judge(a)={automated,ϵ^(a)≤ϵmax⁡(a) and ϵ^(a) externally auditedhuman required,otherwise\text{Judge}(a) = \begin{cases} \text{automated}, & \hat\epsilon(a) \le \epsilon_{\max}(a) \ \text{and}\ \hat\epsilon(a)\ \text{externally audited} \\ \text{human required}, & \text{otherwise} \end{cases}

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aa

Symbol a

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

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ϵ^\hat\epsilon

Symbol hatepsilon

hatepsilon appears in the objective or constraint used by the optimization on the right.

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ϵmax⁡\epsilon_{\max}

Symbol epsilon_max

epsilonmn_max appears in the objective or constraint used by the optimization on the right.

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=

=

The expressions on both sides represent the same quantity under the stated assumptions.

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subscript

subscript

The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.

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Whether automated LLM-judge evaluation becomes trusted for high-stakes decisions is not a third axis; it is mostly a readout of Axis A applied to one specific evaluator. A useful way to see the dependency is to write the automation decision as a threshold rule. Let ϵ^(a)\hat\epsilon(a) be the estimated error rate of a judge on a decision class a , and let ϵmax⁡(a)\epsilon_{\max}(a) be the maximum error a policy is willing to tolerate for that stakes class. A defensible automation rule is Judge(a)={automated,ϵ^(a)≤ϵmax⁡(a) and ϵ^(a) externally auditedhuman required,otherwise\text{Judge}(a) = \begin{cases} \text{automated}, & \hat\epsilon(a) \le \epsilon_{\max}(a) \ \text{and}\ \hat\epsilon(a)\ \text{externally audited} \\ \text{human required}, & \text{otherwise} \end{cases}. The rule only licenses automation where both clauses hold, and the second clause is the one Axis A supplies or withholds. Zheng and colleagues’ own eighty-percent figure plausibly satisfies the first…
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Whether automated LLM-judge evaluation becomes trusted for high-stakes decisions is not a third axis; it is mostly a readout of Axis A applied to one specific evaluator. A useful way to see the dependency is to write the automation decision as a threshold rule. Let ϵ^(a)\hat\epsilon(a) be the estimated error rate of a judge on a decision class a , and let ϵmax⁡(a)\epsilon_{\max}(a) be the maximum error a policy is willing to tolerate for that stakes class. A defensible automation rule is Judge(a)={automated,ϵ^(a)≤ϵmax⁡(a) and ϵ^(a) externally auditedhuman required,otherwise\text{Judge}(a) = \begin{cases} \text{automated}, & \hat\epsilon(a) \le \epsilon_{\max}(a) \ \text{and}\ \hat\epsilon(a)\ \text{externally audited} \\ \text{human required}, & \text{otherwise} \end{cases}. The rule only licenses automation where both clauses hold, and the second clause is the one Axis A supplies or withholds. Zheng and colleagues’ own eighty-percent figure plausibly satisfies the first clause for low-stakes chat preference, but it was measured by the same research community that built the systems under test [ 11 ] , which does not satisfy the second. AISI’s finding sharpens the point rather than merely restating it: a model’s self-report of its own conduct failed even to describe accurately what it had just done, agreeing that its behaviour was wrong less than half the time [ 1 ] . A same-vendor judge auditing a same-vendor policy model inherits a structurally similar conflict of interest, whatever its raw agreement rate. Under Axis A’s ad hoc branch, plugging an unaudited ϵ^\hat\epsilon into the rule above collapses it to “human required” for anything consequential, regardless of how the automation policy is worded elsewhere — which is why mandatory human review is the position every current voluntary framework, including Anthropic’s own, still defaults to for its most consequential decisions [ 7 ] . Only Axis A’s certified branch, an ϵ^\hat\epsilon someone other than the judge’s own maker has checked, can license the automated case for high stakes. The question does not have independent content beyond asking, again, whether Axis A resolves toward certified or stays ad hoc.

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