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@@ -601,7 +601,7 @@ <h3 id="united-kingdom" class="anchored">United Kingdom</h3>
 <p>The UK benchmark is built from a calibrated public transfer dataset scored through PolicyEngine UK. The current public build starts from a public export of benchmark-compatible households from PolicyEngine US Enhanced CPS, maps those records into UK-facing inputs, and recalibrates them to selected UK targets. The resulting <code>enhanced_cps_2025.h5</code> artifact is checked in to the public PolicyEngine/policyengine-uk-data GitHub repository; the manuscript pins commit <code>9514dfb7ec607897c9f7122a2e073b922c9fd8b6</code> so that a third party can retrieve the exact file used here. The artifact contains 28,532 households; 28,502 (99.9%) pass the eligibility filter that retains households with one benefit unit and one or two adults. This creates a public UK-policy transfer benchmark without publishing restricted household microdata; it is not a representative evaluation over native UK household records and should not be treated as a substitute for Family Resources Survey (FRS)-based UK microdata. The current UK release evaluates seven outputs: Income Tax, National Insurance, Capital Gains Tax, Child Benefit, Universal Credit, Pension Credit, and Personal Independence Payment (PIP). Outputs that depend on status or award facts use prompt-visible facts rather than hidden take-up labels; for example, PIP-positive cases list the daily living and mobility award components used by PolicyEngine.</p>
 <p>The UK data path is more synthetic than the enhanced FRS pipeline and inherits limitations from cross-country transfer, calibration choices, and the subset of variables that can be made prompt-visible. It supports the current public cross-country benchmark, but it is not equivalent to an enhanced-FRS-based benchmark and should not be used to make population-representative claims about UK households <span class="citation" data-cites="sutherland2023euromod">(Sutherland and Figari 2013)</span>.</p>
 <h3 id="reference-output-credibility" class="anchored">Reference-output credibility</h3>
-<p>PolicyBench treats PolicyEngine outputs as benchmark reference outputs, not as administrative records. The reference source is nevertheless stronger than an ad hoc answer key: PolicyEngine is open source, used for household calculators and reform analysis, and externally checked in specific domains. In the UK, No.&nbsp;10’s data science team adapted PolicyEngine’s open-source microsimulation model for experimental policy simulation, with validation against external projections before use <span class="citation" data-cites="woodruff2026no10 policyengine2026downing">(Woodruff 2026; Ghenis 2026)</span>. In the US, PolicyEngine reports matching the National Bureau of Economic Research (NBER) TAXSIM-35 model to the cent on the vast majority of cases for the 2021 tax year across hundreds of thousands of tax units per state, with state-specific differences documented in PolicyEngine’s integration tests <span class="citation" data-cites="policyengine2024statetax feenberg1993taxsim">(PolicyEngine 2024; Feenberg and Coutts 1993)</span>. We do not restate that comparison as a single percentage because the published source uses qualitative phrasing rather than a headline accuracy number. PolicyEngine has also signed a memorandum of understanding with the Federal Reserve Bank of Atlanta for future validation work against the Atlanta Fed’s Policy Rules Database <span class="citation" data-cites="policyengine2025atlantafed atlantafed2026prd">(Ghenis and Makarchuk 2025; Federal Reserve Bank of Atlanta 2026)</span>. The Atlanta Fed sources are a caveat rather than evidence of completed validation for this benchmark: they document planned collaboration and the comparison source, not finished checks of the frozen PolicyBench outputs. Taken together, these sources support using PolicyEngine as a transparent reference implementation with partial external validation, but they do not validate every benchmark output.</p>
+<p>PolicyBench treats PolicyEngine outputs as benchmark reference outputs, not as administrative records. The reference source is nevertheless stronger than an ad hoc answer key: PolicyEngine is open source, used for household calculators and reform analysis, and externally checked in specific domains. In the UK, No.&nbsp;10’s data science team adapted PolicyEngine’s open-source microsimulation model for experimental policy simulation, with validation against external projections before use <span class="citation" data-cites="woodruff2026no10 policyengine2026downing">(Woodruff 2026; Ghenis 2026)</span>. In the US, <span class="citation" data-cites="policyengine2024statetax">PolicyEngine (2024)</span> reports matching the National Bureau of Economic Research (NBER) TAXSIM-35 model <span class="citation" data-cites="feenberg1993taxsim">(Feenberg and Coutts 1993)</span> to the cent on the vast majority of cases for the 2021 tax year across hundreds of thousands of tax units per state, with state-specific differences documented in the integration tests. We do not restate that comparison as a single percentage because the published source uses qualitative phrasing rather than a headline accuracy number. PolicyEngine has also signed a memorandum of understanding <span class="citation" data-cites="policyengine2025atlantafed">(Ghenis and Makarchuk 2025)</span> with the Federal Reserve Bank of Atlanta for future validation work against its Policy Rules Database <span class="citation" data-cites="atlantafed2026prd">(2026)</span>. The Atlanta Fed sources are a caveat rather than evidence of completed validation for this benchmark: they document planned collaboration and the comparison source, not finished checks of the frozen PolicyBench outputs. Taken together, these sources support using PolicyEngine as a transparent reference implementation with partial external validation, but they do not validate every benchmark output.</p>
 <p>This does not make PolicyEngine infallible. During benchmark development, we performed a manual, developer-led discrepancy review, with LLM assistance used to triage and summarize candidate cases surfaced by model explanations. <a href="#tbl-reference-review" class="quarto-xref">Table&nbsp;4</a> summarizes the main discrepancy classes reviewed before the frozen snapshot. In those reviewed classes, discrepancies reflected model mistakes, prompt ambiguity later corrected, or upstream data/model issues fixed before the frozen snapshot; the reviewed discrepancy classes did not identify unresolved PolicyEngine reference-output defects.</p>
 <p>After freezing the snapshot and completing response-contract repairs, we annotated every wrong model-output row and every scenario-output case with at least one wrong row. <a href="#tbl-deviation-audit" class="quarto-xref">Table&nbsp;5</a> reports that all 6,276 rows receiving less than full score have row-level and case-level annotations. The final row-level source is <code>llm_error</code>; no frozen-snapshot wrong row remains classified as a prompt ambiguity, unresolved reference-model issue, unresolved reference-data issue, parse-contract failure, or needs-review item. The audit is still developer-led rather than independent external validation, but it is exhaustive over scored misses in the repaired frozen snapshot.</p>
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@@ -986,7 +986,7 @@ The UK data path is more synthetic than the enhanced FRS pipeline and inherits l
 
 ### Reference-output credibility
 
-PolicyBench treats PolicyEngine outputs as benchmark reference outputs, not as administrative records. The reference source is nevertheless stronger than an ad hoc answer key: PolicyEngine is open source, used for household calculators and reform analysis, and externally checked in specific domains. In the UK, No. 10's data science team adapted PolicyEngine's open-source microsimulation model for experimental policy simulation, with validation against external projections before use [@woodruff2026no10; @policyengine2026downing]. In the US, PolicyEngine reports matching the National Bureau of Economic Research (NBER) TAXSIM-35 model to the cent on the vast majority of cases for the 2021 tax year across hundreds of thousands of tax units per state, with state-specific differences documented in PolicyEngine's integration tests [@policyengine2024statetax; @feenberg1993taxsim]. We do not restate that comparison as a single percentage because the published source uses qualitative phrasing rather than a headline accuracy number. PolicyEngine has also signed a memorandum of understanding with the Federal Reserve Bank of Atlanta for future validation work against the Atlanta Fed's Policy Rules Database [@policyengine2025atlantafed; @atlantafed2026prd]. The Atlanta Fed sources are a caveat rather than evidence of completed validation for this benchmark: they document planned collaboration and the comparison source, not finished checks of the frozen PolicyBench outputs. Taken together, these sources support using PolicyEngine as a transparent reference implementation with partial external validation, but they do not validate every benchmark output.
+PolicyBench treats PolicyEngine outputs as benchmark reference outputs, not as administrative records. The reference source is nevertheless stronger than an ad hoc answer key: PolicyEngine is open source, used for household calculators and reform analysis, and externally checked in specific domains. In the UK, No. 10's data science team adapted PolicyEngine's open-source microsimulation model for experimental policy simulation, with validation against external projections before use [@woodruff2026no10; @policyengine2026downing]. In the US, @policyengine2024statetax reports matching the National Bureau of Economic Research (NBER) TAXSIM-35 model [@feenberg1993taxsim] to the cent on the vast majority of cases for the 2021 tax year across hundreds of thousands of tax units per state, with state-specific differences documented in the integration tests. We do not restate that comparison as a single percentage because the published source uses qualitative phrasing rather than a headline accuracy number. PolicyEngine has also signed a memorandum of understanding [@policyengine2025atlantafed] with the Federal Reserve Bank of Atlanta for future validation work against its Policy Rules Database [-@atlantafed2026prd]. The Atlanta Fed sources are a caveat rather than evidence of completed validation for this benchmark: they document planned collaboration and the comparison source, not finished checks of the frozen PolicyBench outputs. Taken together, these sources support using PolicyEngine as a transparent reference implementation with partial external validation, but they do not validate every benchmark output.
 
 This does not make PolicyEngine infallible. During benchmark development, we performed a manual, developer-led discrepancy review, with LLM assistance used to triage and summarize candidate cases surfaced by model explanations. @tbl-reference-review summarizes the main discrepancy classes reviewed before the frozen snapshot. In those reviewed classes, discrepancies reflected model mistakes, prompt ambiguity later corrected, or upstream data/model issues fixed before the frozen snapshot; the reviewed discrepancy classes did not identify unresolved PolicyEngine reference-output defects.
 
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