diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 65f01a2..f7a867b 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -2,7 +2,7 @@ name: CI on: push: - branches: [main, v2] + branches: [main] pull_request: branches: [main] diff --git a/.gitignore b/.gitignore index 34d7a49..10525cf 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,21 @@ # LiteLLM disk cache .policybench_cache/ +# Local scratch benchmark outputs. Frozen snapshots and batch exports live +# elsewhere under results/ and can still be committed intentionally. +results/local/ +results/analysis/ +results/no_tools/ +results/**/rerun_*/ +results/reference_outputs.csv +results/ground_truth.csv +results/scenarios.csv + +# Paper render scratch outputs. The served paper artifacts live under +# app/public/paper/ and frozen manuscript inputs live under paper/snapshot/. +paper/out/ +paper/figures/ + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/CLAUDE.md b/CLAUDE.md index 98afa91..55e06dc 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -9,7 +9,7 @@ ruff format . # Format ``` ## Architecture -- **Two conditions**: AI alone (EDSL) vs AI with PE tools (LiteLLM) +- **One condition**: AI alone (no tools) - **Ground truth**: policyengine-us Simulation - **TDD**: Write tests first, then implement @@ -18,12 +18,11 @@ ruff format . # Format - `policybench/scenarios.py` — Household scenario generation - `policybench/ground_truth.py` — PE-US calculations - `policybench/prompts.py` — Natural language prompt templates -- `policybench/eval_no_tools.py` — EDSL-based AI-alone benchmark -- `policybench/eval_with_tools.py` — LiteLLM tool-calling benchmark +- `policybench/eval_no_tools.py` — LiteLLM-based AI-alone benchmark - `policybench/analysis.py` — Metrics and reporting ## Testing -- All tests mock external calls (EDSL, LiteLLM, PE-US API) +- All tests mock external calls (LiteLLM, PE-US API) - `pytest -m "not slow"` to skip slow tests - Full benchmark runs are manual and expensive diff --git a/README.md b/README.md index 9da713b..800a66b 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,43 @@ # PolicyBench -Can AI models accurately calculate tax and benefit outcomes without tools? +How well can frontier models calculate tax and benefit outcomes without tools? -PolicyBench measures how well frontier AI models estimate US tax/benefit values for specific households — both **without tools** (pure reasoning) and **with PolicyEngine tools** (API access to ground truth). +PolicyBench measures how well frontier AI models estimate selected household tax +and benefit outputs without tools. -## Conditions +For benchmark scope, snapshot policy, and terminology, see the +[benchmark card](docs/benchmark_card.md). + +US benchmark scenarios are sampled from Enhanced CPS households and evaluated +under tax year 2026 rules with PolicyEngine-US. The public UK path uses a +UK-calibrated transfer dataset and PolicyEngine-UK reference outputs for fiscal +year 2026-27. + +## Condition 1. **AI alone**: Models estimate tax/benefit values using only their training knowledge -2. **AI with PolicyEngine**: Models use a PolicyEngine tool to compute exact answers -## Models tested +## Benchmark scope -- Claude (Opus 4.6, Sonnet 4.5) -- GPT (4o, o3) -- Gemini 2.5 Pro +Benchmark outputs are defined in `policybench/benchmark_specs.json`. New CLI +runs default to `headline`, which focuses the main ranking on person- or +household-facing outputs that contribute to household net income. PolicyEngine +variables may be native to lower-level entities, but benchmark outputs are +either expanded to people shown in the prompt or aggregated to the household +before scoring. Coverage outputs are binary flags in the headline ranking; the +separate household-equal impact score uses PolicyEngine value proxies to give +those flags a dollar-scale weight. Intermediate tax bases and payroll +subcomponents are excluded from the headline ranking. WIC is requested as +person-level eligibility, not as a dollar amount. ## Programs evaluated -Federal tax, EITC, CTC, SNAP, SSI, Medicaid eligibility, state income tax, net income, marginal tax rates, and more. +The current public release covers selected federal taxes, credits, benefits, +health-related support, coverage labels, and state-tax outputs in the US, plus +selected tax and transfer outputs in the UK. US federal income tax is scored as +a compact decomposition: tax after nonrefundable credits and before refundable +credits, plus refundable federal credits excluding the ACA Premium Tax Credit. +The ACA Premium Tax Credit is scored separately as a health-related output. ## Quick start @@ -26,18 +46,41 @@ pip install -e ".[dev]" pytest # Run tests (mocked, no API calls) ``` -## Full benchmark +## Benchmark run ```bash -# Generate ground truth from PolicyEngine-US -policybench ground-truth +# Generate reference outputs for 100 sampled households using headline outputs +policybench reference-outputs -n 100 --seed 42 + +# Run AI-alone evaluations on the exported scenario manifest. +# The standard response contract includes numeric answers and explanations. +policybench eval-no-tools -n 100 --seed 42 + +# For larger runs, use resumable per-model chunks. +policybench eval-no-tools-chunked \ + --scenario-manifest results/local/scenarios.csv \ + --output-dir results/local/no_tools_chunked \ + --country us \ + --chunk-size 10 \ + --parallel 2 -# Run AI-alone evaluations -policybench eval-no-tools +# Analyze local results and export local artifacts +policybench analyze --output-dir results/local/analysis +``` + +## Repeated runs -# Run AI-with-tools evaluations -policybench eval-with-tools +```bash +# Optional: run the same benchmark multiple times on the saved scenario manifest +policybench eval-no-tools-repeated -n 100 --seed 42 --repeats 3 -o results/local/no_tools/runs -# Analyze results -policybench analyze +# Analyze the canonical point estimate plus across-run stability +policybench analyze --runs-dir results/local/no_tools/runs --output-dir results/local/analysis ``` + +`policybench reference-outputs` writes PolicyEngine reference outputs, not +administrative truth. It also writes `results/local/scenarios.csv`, and the eval +commands reuse that manifest by default instead of regenerating households from +the current source dataset. Prediction CSVs also get a `.meta.json` sidecar so +resumes only happen against the exact same manifest, model set, and program set. +`policybench ground-truth` remains as a compatibility alias. diff --git a/RESULTS.md b/RESULTS.md index 77d183b..e04c58f 100644 --- a/RESULTS.md +++ b/RESULTS.md @@ -1,71 +1,78 @@ -# PolicyBench: AI can't accurately calculate taxes and benefits — but tools fix that - -> Can frontier AI models accurately calculate US tax and benefit outcomes? - -**TL;DR: No — but with PolicyEngine tools, they achieve 100% accuracy.** - -## Setup - -- **100 household scenarios** across 12 US states, varying income ($0–$500k), filing status, and family composition -- **14 tax/benefit programs**: federal income tax, EITC, CTC, SNAP, SSI, Medicaid eligibility, state taxes, and more -- **4 frontier models**: GPT-5.2, Claude Sonnet 4.5, Claude Sonnet 4.6, Claude Opus 4.6 -- **2 conditions**: AI alone (parametric knowledge only) vs. AI with PolicyEngine tools -- **Ground truth**: PolicyEngine-US microsimulation (1,400 scenario-program pairs) -- **Total predictions**: 9,800 (5,600 no-tools + 4,200 with-tools) - -## Headline results - -### Without tools (AI alone) - -| Model | MAE | MAPE | Within 10% | -|:------|----:|-----:|----------:| -| Claude Sonnet 4.6 | $1,285 | 52% | 72.3% | -| Claude Opus 4.6 | $1,257 | 85% | 70.8% | -| GPT-5.2 | $2,578 | 78% | 62.1% | -| Claude Sonnet 4.5 | $2,276 | 125% | 61.9% | - -### With PolicyEngine tools - -| Model | MAE | MAPE | Within 10% | -|:------|----:|-----:|----------:| -| Claude Opus 4.6 | **$0** | **0%** | **100.0%** | -| Claude Sonnet 4.5 | **$0** | **0%** | **100.0%** | -| GPT-5.2 | **$0** | **0%** | **100.0%** | - -### By program (AI alone, all models averaged) - -| Program | MAE | MAPE | Within 10% | -|:--------|----:|-----:|----------:| -| Federal income tax | $4,234 | 54% | 41.0% | -| Income tax before credits | $2,683 | 39% | 62.7% | -| EITC | $727 | 298% | 75.3% | -| CTC | $1,028 | 174% | 74.3% | -| Refundable credits | $981 | 128% | 62.3% | -| SNAP | $769 | 55% | 80.7% | -| SSI | $436 | 100% | 95.7% | -| State income tax | $938 | 76% | 59.7% | -| Household net income | $10,586 | 14% | 66.0% | -| Total benefits | $5,228 | 117% | 43.7% | -| Market income | $0 | 0% | 100.0% | -| Marginal tax rate | $347 | N/A | 18.0% | - -## Key takeaways - -1. **Tools > models.** Every model with PolicyEngine (100% accuracy) vastly outperforms every model without it (62–72%). The choice of computational tool matters more than the choice of frontier model. - -2. **AI alone is unreliable for policy calculations.** Even the best model (Claude Sonnet 4.6) averages $1,285 error per calculation and gets only 72% of answers within 10% of correct. The worst programs — income tax (41%), marginal tax rates (18%), and aggregate benefits (44%) — are precisely where accuracy matters most. - -3. **With tools, accuracy is perfect.** All three tested models achieve $0 MAE and 100% within-10% accuracy across all 4,200 with-tools predictions. The tool returns ground truth, and models faithfully report it. - -4. **Newer models are improving, but not enough.** Claude Sonnet 4.6 improved significantly over 4.5 (72% vs 62% within 10%), but still falls far short of the 100% achievable with tools. Model improvements can't substitute for computational tools. - -5. **Marginal tax rates are nearly impossible without tools.** Only 18% of AI-alone predictions are within 10% of the correct marginal rate. This makes AI-generated policy advice about work incentives unreliable without computational backing. - -6. **The benchmark validates PolicyEngine's value proposition.** Any AI system that needs to answer questions about US taxes and benefits should use PolicyEngine rather than relying on parametric knowledge. +# PolicyBench Results + +PolicyBench is a no-tools benchmark. Ad hoc local outputs should live under +`results/local/` after a benchmark run. Published leaderboard claims should +instead point to dated batch directories or to a committed dashboard export +such as `app/src/data.json`. + +## Run + +```bash +policybench reference-outputs -n 100 --seed 42 +policybench eval-no-tools -n 100 --seed 42 +policybench analyze --output-dir results/local/analysis +``` + +The first command writes PolicyEngine reference outputs, not administrative +truth. `policybench ground-truth` remains as a compatibility alias. + +## Full runbook + +Use a dated batch directory and keep model outputs per country and model so +interrupted runs can resume independently. + +```bash +RUN_DIR=results/full_batch_20260501 + +policybench reference-outputs -n 1000 --seed 42 --country us --program-set headline \ + -o "$RUN_DIR/us/reference_outputs.csv" \ + --scenario-manifest-output "$RUN_DIR/us/scenarios.csv" + +policybench reference-outputs -n 1000 --seed 42 --country uk --program-set headline \ + -o "$RUN_DIR/uk/reference_outputs.csv" \ + --scenario-manifest-output "$RUN_DIR/uk/scenarios.csv" + +for country in us uk; do + for model in claude-opus-4.7 claude-sonnet-4.6 claude-haiku-4.5 \ + grok-4.3 grok-4.20 grok-4.1-fast gpt-5.5 gpt-5.4-mini gpt-5.4-nano \ + gemini-3.1-pro-preview gemini-3-flash-preview \ + gemini-3.1-flash-lite-preview; do + policybench eval-no-tools-chunked \ + --scenario-manifest "$RUN_DIR/$country/scenarios.csv" \ + --output-dir "$RUN_DIR/$country/no_tools_chunked" \ + --country "$country" \ + --model "$model" \ + --program-set headline \ + --chunk-size 50 \ + --parallel 4 + done +done + +for country in us uk; do + mkdir -p "$RUN_DIR/$country/by_model" + cp "$RUN_DIR/$country/no_tools_chunked/by_model/"*.csv "$RUN_DIR/$country/by_model/" +done + +python scripts/export_full_run.py --run-dir "$RUN_DIR" +``` + +For first-pass cost control, run the same commands with `-n 100` in a separate +scratch directory before launching the 1,000-household batch. + +## Artifacts + +- `results/local/reference_outputs.csv` +- `results/local/no_tools/predictions.csv` +- `results/local/analysis/metrics.csv` +- `results/local/analysis/summary_by_model.csv` +- `results/local/analysis/summary_by_variable.csv` +- `results/local/analysis/impact_summary_by_model.csv` +- `results/local/analysis/usage_summary.csv` +- `results/local/analysis/report.md` ## Methodology -See the [full paper](docs/) and [benchmark code](policybench/) for complete methodology. Ground truth is computed via [PolicyEngine-US](https://github.com/PolicyEngine/policyengine-us). All API responses are cached for reproducibility. +See the [full paper](docs/) and [benchmark code](policybench/) for complete methodology. Reference outputs are computed via [PolicyEngine-US](https://github.com/PolicyEngine/policyengine-us) and [PolicyEngine-UK](https://github.com/PolicyEngine/policyengine-uk); the public UK scenarios use PolicyBench's calibrated transfer dataset. LLM responses are cached for reproducibility. --- -*[Cosilico](https://cosilico.ai) · [PolicyEngine](https://policyengine.org)* +*[PolicyEngine](https://policyengine.org) · [PolicyBench](https://policybench.org)* diff --git a/app/.gitignore b/app/.gitignore index a547bf3..2dd1303 100644 --- a/app/.gitignore +++ b/app/.gitignore @@ -10,6 +10,9 @@ lerna-debug.log* node_modules dist dist-ssr +.next +out +*.tsbuildinfo *.local # Editor directories and files @@ -22,3 +25,5 @@ dist-ssr *.njsproj *.sln *.sw? +.vercel +.env*.local diff --git a/app/.vercelignore b/app/.vercelignore new file mode 100644 index 0000000..42a9ac8 --- /dev/null +++ b/app/.vercelignore @@ -0,0 +1,9 @@ +.git +.gitignore +.next +dist +node_modules +output +playwright-report +coverage +*.log diff --git a/app/bun.lock b/app/bun.lock index b0054bb..795ea3e 100644 --- a/app/bun.lock +++ b/app/bun.lock @@ -4,21 +4,47 @@ "": { "name": "app", "dependencies": { + "@icons-pack/react-simple-icons": "^13.13.0", + "@lobehub/icons": 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nextCoreWebVitals from "eslint-config-next/core-web-vitals"; +import nextTypeScript from "eslint-config-next/typescript"; + +const config = [ + { + ignores: [".next/**", "dist/**", "node_modules/**", "out/**", "public/paper/**"], + }, + ...nextCoreWebVitals, + ...nextTypeScript, +]; + +export default config; diff --git a/app/index.html b/app/index.html deleted file mode 100644 index cf0ee87..0000000 --- a/app/index.html +++ /dev/null @@ -1,13 +0,0 @@ - - - - - - - PolicyBench - AI tax/benefit accuracy benchmark - - -
- - - diff --git a/app/next-env.d.ts b/app/next-env.d.ts new file mode 100644 index 0000000..9edff1c --- /dev/null +++ b/app/next-env.d.ts @@ -0,0 +1,6 @@ +/// +/// +import "./.next/types/routes.d.ts"; + +// NOTE: This file should not be edited +// see https://nextjs.org/docs/app/api-reference/config/typescript for more information. diff --git a/app/next.config.ts b/app/next.config.ts new file mode 100644 index 0000000..2f6491c --- /dev/null +++ b/app/next.config.ts @@ -0,0 +1,7 @@ +import type { NextConfig } from "next"; + +const nextConfig: NextConfig = { + reactStrictMode: true, +}; + +export default nextConfig; diff --git a/app/package.json b/app/package.json index 3f39580..6a45d7e 100644 --- a/app/package.json +++ b/app/package.json @@ -4,21 +4,31 @@ "version": "0.0.0", "type": "module", "scripts": { - "dev": "vite", - "build": "tsc && vite build", - "preview": "vite preview" + "clean": "rm -rf .next dist .vercel/output", + "dev": "next dev --turbopack", + "build": "next build", + "start": "next start", + "lint": "eslint . --max-warnings=0" }, "devDependencies": { - "@tailwindcss/vite": "^4.2.0", + "@tailwindcss/postcss": "^4.2.2", + "@types/node": "^22.18.8", "@types/react": "^19.2.14", "@types/react-dom": "^19.2.3", - "@vitejs/plugin-react": "^5.1.4", + "eslint": "^9.38.0", + "eslint-config-next": "^16.2.1", + "next": "^16.2.1", "tailwindcss": "^4.2.0", - "typescript": "~5.9.3", - "vite": "^7.3.1" + "typescript": "~5.9.3" }, "dependencies": { + "@icons-pack/react-simple-icons": "^13.13.0", + "@lobehub/icons": "^5.2.0", + "@policyengine/design-system": "^0.4.0", + "motion": "^12.34.3", "react": "^19.2.4", - "react-dom": "^19.2.4" + "react-dom": "^19.2.4", + "recharts": "^3.7.0", + "simple-icons": "^16.14.0" } } diff --git a/app/postcss.config.mjs b/app/postcss.config.mjs new file mode 100644 index 0000000..61e3684 --- /dev/null +++ b/app/postcss.config.mjs @@ -0,0 +1,7 @@ +const config = { + plugins: { + "@tailwindcss/postcss": {}, + }, +}; + +export default config; diff --git a/app/public/assets/policyengine-logo.png b/app/public/assets/policyengine-logo.png new file mode 100644 index 0000000..49f5145 Binary files /dev/null and b/app/public/assets/policyengine-logo.png differ diff --git a/app/public/assets/policyengine-logo.svg b/app/public/assets/policyengine-logo.svg new file mode 100644 index 0000000..81fbe04 --- /dev/null +++ b/app/public/assets/policyengine-logo.svg @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + + + diff --git a/app/public/assets/policyengine-mark.svg b/app/public/assets/policyengine-mark.svg new file mode 100644 index 0000000..c6d967c --- /dev/null +++ b/app/public/assets/policyengine-mark.svg @@ -0,0 +1,9 @@ + + + + + + + + + diff --git a/app/public/paper/.gitkeep b/app/public/paper/.gitkeep new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/app/public/paper/.gitkeep @@ -0,0 +1 @@ + diff --git a/app/public/paper/policybench.pdf b/app/public/paper/policybench.pdf new file mode 100644 index 0000000..cdc5964 Binary files /dev/null and b/app/public/paper/policybench.pdf differ diff --git a/app/public/paper/web/index.html b/app/public/paper/web/index.html new file mode 100644 index 0000000..432508a --- /dev/null +++ b/app/public/paper/web/index.html @@ -0,0 +1,2084 @@ + + + + + + + + + + +PolicyBench + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

PolicyBench

+

Benchmarking no-tool tax-and-benefit estimation in frontier language models

+

PolicyEngine

+ +
+ + +

Abstract

+

PolicyBench evaluates whether frontier language models can estimate household tax and benefit outputs from household facts without tools. The benchmark covers the United States and the United Kingdom, uses sampled US Enhanced CPS-derived scenarios and a public UK calibrated transfer dataset, and scores outputs with a bounded 0-100 metric. The manuscript snapshot is a 100-household-per-country public preview, not a protected held-out leaderboard. In the frozen snapshot used for this manuscript (2026-05-01), the top US model is gpt-5.5 at 89.2, the top UK model is gpt-5.5 at 81.6, and the top shared global model is gpt-5.5 at 85.4. In both countries, multi-step tax quantities and positive-dollar benefit cases are materially harder than zero cases. The live benchmark is available at https://policybench.org.

+

Introduction

+

Household tax-and-benefit estimation sits between arithmetic and policy discussion. Each case has numeric labels, but generating those labels requires filing status, household composition, income concepts, program thresholds, and jurisdiction-specific rules. PolicyBench measures whether models can map household records to those outputs.

+

Most current language-model evaluations do not test this mapping directly. General math benchmarks emphasize symbolic manipulation and exact final answers (Cobbe et al. 2021; Hendrycks et al. 2021), while generic question-answering benchmarks emphasize recall or instruction following. PolicyBench instead asks whether models can transform household facts into policy outputs without access to tools or simulators.

+

This matters for public-facing calculators, analyst workflows, and tax-preparation or screening systems. The benchmark is intended to separate verbal policy fluency from household-level quantitative prediction.

+ +

The most relevant prior work is tax and statutory reasoning. SARA frames statutory interpretation, including tax-relevant rule application, as a language-understanding problem (Holzenberger et al. 2021). LegalBench broadens this view and includes tax-oriented numeric tasks such as sara_numeric (Guha et al. 2023). RuleArena evaluates rule-guided reasoning in regulation-style settings (Kim et al. 2024), and TaxCalcBench evaluates frontier models on tax calculation from structured return-like inputs (Mahesh et al. 2025). Shanahan et al. report a similar split on the VITA test: models do better on tax knowledge questions than on open-ended calculation tasks (Shanahan et al. 2025).

+

PolicyBench also sits within a broader literature on quantitative reasoning benchmarks. Canonical math evaluations such as GSM8K and MATH normalized exact final-answer scoring for numeric tasks (Cobbe et al. 2021; Hendrycks et al. 2021). That norm is less natural for continuous-valued policy outputs, where being within 1 percent is closer than being off by an order of magnitude. Recent work has questioned pure exact-match scoring in numeric QA and temporal reasoning, arguing for error-aware metrics instead (Zhou et al. 2025). PolicyBench follows that direction by combining exactness with near-miss thresholds rather than collapsing everything into exact match alone.

+

Finance-domain benchmarks provide another relevant precedent. FinQA and TAT-QA both show that realistic quantitative reasoning often requires combining structured numbers with domain knowledge rather than solving stylized school-math problems (Chen et al. 2021; Zhu et al. 2021). PolicyBench differs in that its reference outputs are generated by executable tax-benefit microsimulation rather than annotated document questions, but the underlying motivation is similar: domain-specific quantitative reasoning deserves dedicated evaluation.

+

Finally, PolicyBench depends on two infrastructure literatures that are not themselves LLM benchmarks. One is structured-output reliability, since the benchmark relies on multi-output JSON responses and parse coverage rather than free-form prose (Shorten et al. 2024). The other is tax-benefit microsimulation, where systems such as EUROMOD provide the methodological precedent for evaluating policy rules over household microdata (Sutherland et al. 2023). The UK side of PolicyBench also builds on PolicyEngine’s recent work on survey-backed calibration and imputation (Woodruff and Ghenis 2026). There is also operational work on applying and evaluating AI systems in public-benefits settings, including caseworker-assist and SNAP-focused evaluations (Nava Labs 2026, 2025; ZenML LLMOps Database 2025). PolicyBench combines these strands into a public cross-model benchmark over household-level tax and benefit outputs.

+

Benchmark design

+

PolicyBench asks models to predict all benchmark outputs for a household in a single structured response. One response per household reduces repeated prompt cost and keeps the task at the household-return level rather than turning it into a sequence of unrelated one-output calls.

+

The benchmark uses a bounded 0-100 score. For amount variables, the score averages four hit rates:

+
    +
  1. exact
  2. +
  3. within 1%
  4. +
  5. within 5%
  6. +
  7. within 10%
  8. +
+

For binary outputs, the score is exact accuracy. This keeps 100 as the ceiling while still giving partial credit for near misses on amount outputs. PolicyBench also tracks mean absolute error and related error metrics, but those are secondary to the bounded score. This choice preserves exact-match comparability while avoiding the failure mode that recent numeric-evaluation papers have criticized (Zhou et al. 2025).

+

All requested US outputs are annual amounts or annual eligibility indicators for tax year 2026; UK outputs are annual amounts or annual eligibility indicators for fiscal year 2026-27. For currency amounts, the “exact” hit rate means within 1 currency unit of the reference value after numeric parsing. Percentage-threshold hit rates use relative error when the reference value is nonzero and the same 1 currency-unit absolute tolerance when the reference value is zero. Binary outputs are parsed as 0 or 1 eligibility flags and scored by exact classification accuracy. Missing or unparseable numeric answers receive zero score for that requested output.

+

Aggregation proceeds in four steps. First, each household-output prediction receives a 0-100 score. Second, person-level coverage rows are averaged to their program group. Third, output groups are averaged with equal weight within each country. Fourth, global scores average the US and UK country scores for models run in both countries. Household-equal impact scores are reported only as a sensitivity view.

+

The benchmark requires each model response to include numeric answers and one short explanation per requested output. The headline score uses only the numeric answers. Explanations are retained for scenario exploration and qualitative error analysis; they should not be interpreted as faithful traces of model reasoning. Numeric-only runs are treated as prompt-contract ablations, not as the canonical leaderboard task.

+

Because explanations are required, the canonical task measures policy estimation under a public-facing structured-response contract, not isolated arithmetic accuracy. Prompt fairness is part of the benchmark contract. The current release uses one prompt template per country, with no model-specific tuning. Models receive the same household facts, the same requested outputs, and no web or tool access. The prompt states that unlisted numeric inputs are 0, unlisted boolean or status facts are false, and household characteristics are constant over the tax-benefit year. Provider-specific differences are limited to the response transport needed to obtain structured output.

+

Frozen snapshot

+

Leaderboard positions are version-sensitive, so manuscript claims refer to the frozen dashboard export in Table 1 rather than to the live site. The local run labels identify the source runs used to create that export; the committed export is the manuscript artifact.

+
+
+
+
+Table 1: Frozen manuscript snapshot. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ItemValue
0Snapshot date2026-05-01
1Policy periodUS tax year 2026; UK fiscal year 2026-27
2Frozen exportapp/src/data.json
3Frozen export SHA-256 prefix6b8f9f913377
4Snapshot manifestpaper/snapshot/20260501/manifest.json
5US scenarios SHA-256 prefix46bc6b1ba56e
6US reference outputs SHA-256 prefixcaa63a93941d
7UK scenarios SHA-256 prefixcc072e32df04
8UK reference outputs SHA-256 prefix2e6569666750
9Benchmark spec SHA-256 prefix532e15ba19bf
10Prompt template SHA-256 prefix3b81cd8ac0af
11Scoring code SHA-256 prefix2bc0cda89ffe
12US run labelus_100_stable_models_20260501_062249
13UK run labeluk_100_hicbc_fixed_20260501_083402
14Household sample seed42
15PolicyEngine-USpolicyengine-us 1.653.3
16PolicyEngine-UKpolicyengine-uk 2.88.0
17US data bundlepolicyengine-us-data 1.73.0
18UK runtime bundlepolicyengine-uk-data 1.40.4
19UK scenario sourceenhanced_cps_2025.h5 public UK calibrated transfer artifact
20UK scenario-source SHA-256199ebc61d29231b4799ad337a95393765b5fb5aede1834b93ff2acecceded866
21Households100 US and 100 UK
22Models12 shared models
23Output groups19 US and 7 UK
24ConditionNo tools, no web access, one structured response per household
25Response contractNumeric answer and non-empty explanation for every requested output
+ +
+
+
+
+
+
+
+
+
+
+Table 2: Model run configuration in the frozen manuscript snapshot. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelProvider IDUS parsedUK parsedStructured outputDecoding
0gpt-5.5gpt-5.52,180/2,180700/700provider structured-output schema; no external toolprovider default; no model-specific prompt tuning
1grok-4.20xai/grok-4.20-reasoning2,180/2,180700/700provider structured-output schema; no external toolprovider default; no model-specific prompt tuning
2gemini-3.1-pro-previewgemini/gemini-3.1-pro-preview2,180/2,180700/700provider structured-output schema; no external toolprovider default; no model-specific prompt tuning
3grok-4.3xai/grok-4.32,180/2,180700/700provider structured-output schema; no external toolprovider default; no model-specific prompt tuning
4gemini-3-flash-previewgemini/gemini-3-flash-preview2,180/2,180700/700provider structured-output schema; no external toolprovider default; no model-specific prompt tuning
5claude-opus-4.7claude-opus-4-72,180/2,180700/700provider structured-output schema; no external toolprovider default; no model-specific prompt tuning
6claude-sonnet-4.6claude-sonnet-4-62,180/2,180700/700provider structured-output schema; no external toolprovider default; no model-specific prompt tuning
7gemini-3.1-flash-lite-previewgemini/gemini-3.1-flash-lite-preview2,180/2,180700/700provider structured-output schema; no external toolprovider default; no model-specific prompt tuning
8grok-4.1-fastxai/grok-4-1-fast-non-reasoning2,180/2,180700/700provider structured-output schema; no external toolprovider default; no model-specific prompt tuning
9claude-haiku-4.5claude-haiku-4-5-202510012,180/2,180700/700provider structured-output schema; no external toolprovider default; no model-specific prompt tuning
10gpt-5.4-minigpt-5.4-mini2,180/2,180700/700provider structured-output schema; no external toolprovider default; no model-specific prompt tuning
11gpt-5.4-nanogpt-5.4-nano2,180/2,180700/700provider structured-output schema; no external toolprovider default; no model-specific prompt tuning
+ +
+
+
+
+
+
+

Data and scenario construction

+

United States

+

The US benchmark is built from Enhanced CPS-derived households using PolicyEngine US. The sampled households are filtered to keep a single-tax-unit structure while retaining variation in filing status, household composition, and income sources. Prompts include nonzero promptable raw inputs across relevant entities rather than a hand-curated summary, so the models see many of the same facts the simulator receives.

+

The current US release evaluates 19 output groups spanning federal income tax, refundable credits, payroll and self-employment tax, state and local income tax, SNAP, SSI, TANF, ACA premium tax credits, school-meal eligibility, and person-level coverage eligibility for WIC, Medicaid, CHIP, Medicare, Head Start, and Early Head Start.

+

The output scope is intentionally narrower than the full PolicyEngine model. Table 3 summarizes the inclusion rule. The benchmark asks for WIC eligibility rather than a WIC dollar amount; WIC dollar values are used only as impact-weight proxies for coverage flags, not as requested model outputs.

+
+
+
+
+Table 3: Output-selection rationale. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Scope decisionRationale
0IncludedDirect tax, credit, benefit, health-support, and coverage outputs that a household-facing model could plausibly be asked to estimate from household facts.
1ExcludedIntermediate tax bases, payroll subcomponents, and outputs that mainly require unavailable history, restricted local market data, restricted program-administration data, or take-up assignment rather than rule calculation.
2ACA Premium Tax CreditRetained as a deliberate health-support output; when local benchmark premiums are not listed, the model must estimate them from the household facts.
3Binary coverage outputsRequested as 0/1 eligibility flags and scored as classification tasks; their dollar values are used only as impact-weight proxies, not as requested model outputs.
4WICThe benchmark asks for person-level WIC eligibility. It does not ask models to estimate a WIC dollar amount.
+ +
+
+
+
+
+
+

United Kingdom

+

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. This creates a public UK-policy transfer benchmark without publishing restricted household microdata; it is not a representative evaluation over native UK household records. The current UK release evaluates seven outputs: income tax, national insurance, capital gains tax, child benefit, universal credit, pension credit, and personal independence payment. 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.

+

The UK data path is more synthetic than the enhanced FRS pipeline, so the benchmark should not be read as a substitute for native UK microdata work. It supports the current public cross-country benchmark, but it is not equivalent to an enhanced-FRS-based benchmark (Sutherland et al. 2023; Woodruff and Ghenis 2026).

+

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 partially validated 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 (Woodruff 2026; Ghenis 2026). In the US, PolicyEngine’s state tax modelling has been validated against NBER’s TAXSIM, with reported penny-level agreement for the vast majority of 2021 test cases (PolicyEngine 2024; Feenberg and Coutts 1993). PolicyEngine has also signed a memorandum of understanding with the Federal Reserve Bank of Atlanta for future validation of tax and benefit calculations against the Atlanta Fed’s Policy Rules Database (Ghenis and Makarchuk 2025; Federal Reserve Bank of Atlanta 2026). These sources do not validate every benchmark output; they establish PolicyEngine as a transparent, externally used reference implementation with partial external validation and planned additional checks.

+

This does not make PolicyEngine infallible. During benchmark development, we manually and LLM-assisted reviewed discrepant household cases surfaced by model explanations. Table 4 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. This is a qualitative development discrepancy review, not a sampled validation study or exhaustive validation of every reference value.

+
+
+
+
+Table 4: Development discrepancy review before the frozen snapshot. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Discrepancy classReview outcome
0US federal creditsReviewed cases where explanations omitted EITC or refundable CTC; discrepancies reflected model credit-treatment errors, not identified PolicyEngine errors.
1US SNAP, SSI, and MedicaidReviewed false-zero and false-positive explanations; common model failures used broad asset or income heuristics inconsistent with the supplied facts and the reference rules.
2US payroll and overtimeReviewed overtime-related cases during development; upstream data/model fixes and prompt clarifications were applied before the frozen snapshot.
3UK Child BenefitReviewed HICBC-related misses; the benchmark now asks for gross Child Benefit before HICBC, with HICBC included in Income Tax.
4UK National InsuranceReviewed cases where models subtracted pension contributions or used wrong thresholds/rates; no reference-output defect was identified in the reviewed cases.
5UK UC, Pension Credit, and PIPReviewed positive-case misses and false positives; explanations usually showed broad capital, age, or award-level heuristics rather than simulator-reference defects.
+ +
+
+
+
+
+
+

Results

+

United States leaderboard

+

The US leaderboard for the frozen manuscript snapshot is shown in Table 5. The top three models in that snapshot are gpt-5.5 (89.2), grok-4.20 (88.7), and gemini-3.1-pro-preview (87.6).

+
+
+
+
+Table 5: Top US benchmark models in the frozen manuscript snapshot. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelScoreExactWithin 10%ParsedTotal
0gpt-5.589.285.892.221802180
1grok-4.2088.786.291.621802180
2gemini-3.1-pro-preview87.685.390.321802180
3grok-4.387.685.789.821802180
4gemini-3-flash-preview86.384.389.021802180
+ +
+
+
+
+
+
+

United Kingdom leaderboard

+

The UK leaderboard for the frozen manuscript snapshot is shown in Table 6. The top three models in that snapshot are gpt-5.5 (81.6), gemini-3.1-pro-preview (79.6), and grok-4.20 (78.5).

+
+
+
+
+Table 6: Top UK benchmark models in the frozen manuscript snapshot. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelScoreExactWithin 10%ParsedTotal
0gpt-5.581.669.095.1700700
1gemini-3.1-pro-preview79.669.094.1700700
2grok-4.2078.568.692.6700700
3grok-4.376.969.189.0700700
4gemini-3-flash-preview75.267.684.9700700
+ +
+
+
+
+
+
+

Global shared-model leaderboard

+

The global leaderboard averages US and UK scores for the models that have been run in both countries. In the frozen manuscript snapshot, the top three are gpt-5.5 (85.4), grok-4.20 (83.6), and gemini-3.1-pro-preview (83.6). The global table summarizes cross-country performance; the country tables identify the outputs driving the differences.

+
+
+
+
+Table 7: Global shared-model leaderboard across US and UK runs. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelScoreExactWithin 10%ParsedTotal
0gpt-5.585.477.493.728802880
1grok-4.2083.677.492.128802880
2gemini-3.1-pro-preview83.677.292.228802880
3grok-4.382.277.489.428802880
4gemini-3-flash-preview80.775.986.928802880
5claude-opus-4.779.975.485.828802880
6claude-sonnet-4.679.475.983.428802880
7gemini-3.1-flash-lite-preview79.176.183.228802880
8grok-4.1-fast76.575.078.228802880
9claude-haiku-4.575.874.577.428802880
+ +
+
+
+
+
+
+

Simple baselines

+

Simple baselines help interpret score levels in a zero-heavy benchmark. Table 8 reports an always-zero response and a median-reference-by-output response on the same frozen household sample. These are not model competitors; they show how much of the bounded score can be earned without household-specific policy calculation.

+
+
+
+
+Table 8: Simple baseline scores on the frozen manuscript snapshot. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BaselineGlobal scoreUS scoreUK score
0Always zero75.681.969.3
1Median reference by output68.576.560.5
+ +
+
+
+
+
+
+

Sensitivity to benchmark view

+

The primary leaderboard gives each country equal weight globally and each output group equal weight within a country. Table 9 reports several alternative views. The top model is stable across the main global, amount-only, positive-reference-case, US-only, UK-only, and household-equal impact views. Lower ranks move, especially between positive cases and zero cases, so the paper avoids claims that depend on a single exact ordering beyond the frozen snapshot.

+
+
+
+
+Table 9: Leaderboard sensitivity to alternative scoring views. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ViewRank 1Rank 2Rank 3
0Main global leaderboardgpt-5.5 (85.4)grok-4.20 (83.6)gemini-3.1-pro-preview (83.6)
1Amount outputs onlygpt-5.5 (82.0)grok-4.20 (80.0)gemini-3.1-pro-preview (79.6)
2Positive reference cases onlygpt-5.5 (49.5)gemini-3.1-pro-preview (40.7)grok-4.20 (39.9)
3Zero reference cases onlygrok-4.3 (99.3)gemini-3.1-pro-preview (99.0)gpt-5.5 (98.9)
4US onlygpt-5.5 (89.2)grok-4.20 (88.7)gemini-3.1-pro-preview (87.6)
5UK onlygpt-5.5 (81.6)gemini-3.1-pro-preview (79.6)grok-4.20 (78.5)
6US binary coverage onlygemini-3.1-pro-preview (98.8)grok-4.20 (98.8)grok-4.3 (98.7)
7Household-equal impact scoregpt-5.5 (58.0)gemini-3.1-pro-preview (52.9)grok-4.20 (52.5)
+ +
+
+
+
+
+
+

Sampling uncertainty

+

The manuscript snapshot uses 100 households per country, so small score gaps should not be overinterpreted. Table 10 reports a household-resampling bootstrap over the frozen sample using 1,000 percentile bootstrap draws. It resamples households within each country, recomputes country scores with equal output-group weighting, then recomputes the equal-country global score. The table is a sampling-uncertainty check for this household draw, not uncertainty over future model releases or prompt variants.

+
+
+
+
+Table 10: Household-resampling uncertainty for the global leaderboard. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelScore95% bootstrap score intervalBootstrap rank range
0gpt-5.585.484.3-86.41-1
1grok-4.2083.682.6-84.72-3
2gemini-3.1-pro-preview83.682.4-84.82-3
3grok-4.382.281.0-83.54-4
4gemini-3-flash-preview80.779.4-82.15-7
5claude-opus-4.779.978.5-81.25-8
6claude-sonnet-4.679.478.1-80.76-8
7gemini-3.1-flash-lite-preview79.177.6-80.46-8
8grok-4.1-fast76.574.9-78.09-10
9claude-haiku-4.575.874.2-77.49-12
+ +
+
+
+
+
+
+

Hardest benchmark targets

+

The lowest-scoring US variables are multi-step tax quantities and sparse positive-dollar benefits. As shown in Table 11, federal income tax before refundable credits, state income tax before refundable credits, payroll tax, and SNAP are the hardest US outputs in the frozen manuscript snapshot.

+
+
+
+
+Table 11: Hardest US variables by bounded score. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
VariableScoreExactWithin 10%
0federal_income_tax_before_refundable_credits41.637.447.9
16state_income_tax_before_refundable_credits47.439.856.8
4payroll_tax56.341.771.8
14snap66.263.169.2
1federal_refundable_credits83.282.384.2
+ +
+
+
+
+
+
+

The same pattern appears in the UK run. Income tax and National Insurance are the two hardest UK outputs, while zero-heavy benefits and capital gains tax score higher overall.

+
+
+
+
+Table 12: Hardest UK variables by bounded score. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
VariableScoreExactWithin 10%
2income_tax38.525.059.9
3national_insurance48.939.562.3
5pip81.273.089.1
6universal_credit81.580.083.5
1child_benefit84.576.093.5
+ +
+
+
+
+
+
+

Zero and positive cases

+

Overall hit rates can overstate performance on sparse programs because correct zeros are common. Table 13 and Table 14 therefore report within-10% performance for all cases, positive-reference cases, and zero-reference cases. The gap is large for several outputs: models often identify that a program or tax does not apply, but miss the amount when the reference value is positive.

+
+
+
+
+Table 13: US within-10% performance by zero versus positive reference cases. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
VariableAll casesPositive casesZero cases
0federal_income_tax_before_refundable_credits47.917.395.7
1state_income_tax_before_refundable_credits56.829.196.7
2snap69.216.9100.0
3payroll_tax71.861.696.8
4federal_refundable_credits84.224.493.2
5state_refundable_credits85.45.197.4
6person_medicaid_eligible86.664.394.9
7premium_tax_credit91.70.097.5
+ +
+
+
+
+
+
+
+
+
+
+Table 14: UK within-10% performance by zero versus positive reference cases. +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
VariableAll casesPositive casesZero cases
0income_tax59.947.892.6
1national_insurance62.338.997.5
2universal_credit83.519.497.6
3pip89.159.6100.0
4capital_gains_tax92.619.499.8
5child_benefit93.572.9100.0
6pension_credit94.812.598.3
+ +
+
+
+
+
+
+

Failure modes

+

The benchmark surfaces a few recurring failure patterns.

+

First, models miss positive tax and benefit quantities more often than zero cases. In the US, federal income tax before refundable credits, state income tax before refundable credits, payroll tax, and SNAP are the four lowest-scoring outputs. These require the model to choose the right income concepts, exclusions, program thresholds, and sequencing before applying any final subtraction.

+

Second, the UK benchmark shows the same split between tax calculations and many benefit outputs. Income tax and National Insurance score below the benefit outputs in the frozen manuscript snapshot. Positive Universal Credit and Pension Credit cases remain difficult, so the result should not be read as a general claim that benefits are easy.

+

Third, structured-output reliability can affect rankings. The current manuscript snapshot has full parse coverage for the included models, but the benchmark still tracks coverage because failures to return parseable numeric values should count as benchmark failures (Shorten et al. 2024).

+

Limitations

+

PolicyBench is not a substitute for a production tax-and-benefit calculator. Several caveats matter:

+ +

The current paper is therefore an evaluation of model performance under a specific structured-output benchmark, not a general certification of tax or benefit competence.

+

Conclusion

+

PolicyBench shows a consistent pattern across both countries. Models often identify non-applicability, but positive tax and benefit amounts remain difficult, especially for multi-step income tax, payroll tax, National Insurance, and positive benefit cases. In the frozen manuscript snapshot, gpt-5.5 is the top-scoring US model and gpt-5.5 is the top-scoring UK model.

+

Next steps are to expand country coverage and tighten the data and scoring pipelines so that the leaderboard is driven less by output-format reliability and more by policy calculation quality.

+
+
+Chen, Zhiyu, Wenhu Chen, Charese Smiley, et al. 2021. “FinQA: A Dataset of Numerical Reasoning over Financial Data.” arXiv Preprint arXiv:2109.00122. https://arxiv.org/abs/2109.00122. +
+
+Cobbe, Karl, Vineet Kosaraju, Mohammad Bavarian, et al. 2021. “Training Verifiers to Solve Math Word Problems.” arXiv Preprint arXiv:2110.14168. https://arxiv.org/abs/2110.14168. +
+
+Federal Reserve Bank of Atlanta. 2026. Policy Rules Database. Federal Reserve Bank of Atlanta. https://www.atlantafed.org/what-we-study/workforce-development/advancing-careers-for-low-income-families/policy-rules-database. +
+
+Feenberg, Daniel R., and Elisabeth Coutts. 1993. “An Introduction to the TAXSIM Model.” Journal of Policy Analysis and Management 12 (1): 189–94. +
+
+Ghenis, Max. 2026. PolicyEngine Powers Rapid Policy Analysis at No 10 Downing Street. PolicyEngine. https://www.policyengine.org/us/research/policyengine-10-downing-street. +
+
+Ghenis, Max, and Pavel Makarchuk. 2025. PolicyEngine and Atlanta Fed Sign Memorandum of Understanding for Policy Rules Database Validation. PolicyEngine. https://www.policyengine.org/us/research/policyengine-atlanta-fed-mou-prd. +
+
+Guha, Neel, Julian Nyarko, Daniel E. Ho, et al. 2023. “LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models.” arXiv Preprint arXiv:2308.11462. https://arxiv.org/abs/2308.11462. +
+
+Hendrycks, Dan, Collin Burns, Saurav Kadavath, et al. 2021. “Measuring Mathematical Problem Solving with the MATH Dataset.” NeurIPS Datasets and Benchmarks Track. https://arxiv.org/abs/2103.03874. +
+
+Holzenberger, Nils, Benjamin Van Durme, Sarah Lawsky, and Kyle Richardson. 2021. “Factoring Statutory Reasoning as Language Understanding Challenges.” arXiv Preprint arXiv:2105.07903. https://arxiv.org/abs/2105.07903. +
+
+Kim, Chan, Jiseung Kim, Jihyung Choi, et al. 2024. “RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios.” arXiv Preprint arXiv:2412.08972. https://arxiv.org/abs/2412.08972. +
+
+Mahesh, Meenakshi, Elliott Ash, Elsen Tan, and Aman Madaan. 2025. “TaxCalcBench: Evaluating Frontier Models on the Tax Calculation Task.” arXiv Preprint arXiv:2507.16126. https://arxiv.org/abs/2507.16126. +
+
+Nava Labs. 2025. Experimenting with AI-Powered Tools in Public Benefits. Case study. https://www.navapbc.com/case-studies/ai-tools-public-benefits. +
+
+Nava Labs. 2026. Evaluating a GenAI-Powered Assistive Chatbot for Caseworkers. Case study. https://www.navapbc.com/case-studies/evaluating-ai-assistive-chatbot-caseworkers. +
+
+PolicyEngine. 2024. PolicyEngine Launches State Income Tax Modeling Nationwide. PolicyEngine. https://www.policyengine.org/us/research/state-tax-model-beta. +
+
+Shanahan, Catherine, Emma McCarthy, Yan Zhao, et al. 2025. “Performance of LLMs on VITA Test: Potential for AI-Assisted Tax Returns for Low Income Taxpayers.” Artificial Intelligence and Law, ahead of print. https://doi.org/10.1007/s10506-025-09465-7. +
+
+Shorten, Connor, Charles Pierse, Thomas Benjamin Smith, et al. 2024. “StructuredRAG: JSON Response Formatting with Large Language Models.” arXiv Preprint arXiv:2408.11061, ahead of print. https://doi.org/10.48550/arXiv.2408.11061. +
+
+Sutherland, Holly, Francesco Figari, Ruth Hancock, et al. 2023. EUROMOD: The European Union Tax-Benefit Microsimulation Model.” International Journal of Microsimulation. https://microsimulation.pub/articles/00075. +
+
+Woodruff, Nikhil. 2026. Informing Policy Using Micro-Simulations. No10 Innovation Fellowship. https://fellows.ai.gov.uk/articles/nikhil-woodruff-micro-simulation. +
+
+Woodruff, Nikhil, and Max Ghenis. 2026. Improving the Accuracy of UK Tax-Benefit Microsimulation with Machine Learning. PolicyEngine technical paper. https://policyengine.org/uk_data_enhancement.pdf. +
+
+ZenML LLMOps Database. 2025. Building and Automating Comprehensive LLM Evaluation Framework for SNAP Benefits. Industry writeup. https://www.zenml.io/llmops-database/building-and-automating-comprehensive-llm-evaluation-framework-for-snap-benefits. +
+
+Zhou, Xinyue et al. 2025. “Time to Revisit Exact Match: Evaluating Temporal Question Answering with Numeric Error Metrics.” arXiv Preprint arXiv:2509.16720. https://arxiv.org/abs/2509.16720. +
+
+Zhu, Fengbin, Wenqiang Lei, Youcheng Huang, et al. 2021. TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance.” arXiv Preprint arXiv:2105.07624. https://arxiv.org/abs/2105.07624. +
+
+ + + + + + + \ No newline at end of file diff --git a/app/public/paper/web/pe-tokens.css b/app/public/paper/web/pe-tokens.css new file mode 100644 index 0000000..54df88f --- /dev/null +++ b/app/public/paper/web/pe-tokens.css @@ -0,0 +1,121 @@ +/** + * PolicyEngine Design Tokens — CSS Custom Properties + * Generated from @policyengine/design-system + * Source of truth: packages/design-system/src/tokens/ + * + * Usage: + * + * or: @import '@policyengine/design-system/tokens.css'; + * or: npm install @policyengine/design-system && import '@policyengine/design-system/dist/tokens.css'; + */ +:root { + /* Colors — primary (teal) */ + --pe-color-primary-50: #E6FFFA; + --pe-color-primary-100: #B2F5EA; + --pe-color-primary-200: #81E6D9; + --pe-color-primary-300: #4FD1C5; + --pe-color-primary-400: #38B2AC; + --pe-color-primary-500: #319795; + --pe-color-primary-600: #2C7A7B; + --pe-color-primary-700: #285E61; + --pe-color-primary-800: #234E52; + --pe-color-primary-900: #1D4044; + --pe-color-primary-alpha-40: #31979566; + --pe-color-primary-alpha-50: #31979580; + --pe-color-primary-alpha-60: #31979599; + + /* Colors — gray */ + --pe-color-gray-50: #F9FAFB; + --pe-color-gray-100: #F2F4F7; + --pe-color-gray-200: #E2E8F0; + --pe-color-gray-300: #D1D5DB; + --pe-color-gray-400: #9CA3AF; + --pe-color-gray-500: #6B7280; + --pe-color-gray-600: #4B5563; + --pe-color-gray-700: #344054; + --pe-color-gray-800: #1F2937; + --pe-color-gray-900: #101828; + + /* Colors — semantic */ + --pe-color-warning: #FEC601; + --pe-color-error: #EF4444; + --pe-color-info: #2C7A7B; + + /* Colors — background */ + --pe-color-bg-primary: #FFFFFF; + --pe-color-bg-secondary: #F5F9FF; + --pe-color-bg-tertiary: #F1F5F9; + --pe-color-bg-sider: #FFFFFF; + + /* Colors — text */ + --pe-color-text-primary: #000000; + --pe-color-text-secondary: #5A5A5A; + --pe-color-text-tertiary: #9CA3AF; + --pe-color-text-inverse: #FFFFFF; + --pe-color-text-title: #000000; + --pe-color-text-link: #2C7A7B; + --pe-color-text-linkHover: #285E61; + --pe-color-text-warning: #d9480f; + + /* Colors — border */ + --pe-color-border-light: #E2E8F0; + --pe-color-border-medium: #CBD5E1; + --pe-color-border-dark: #94A3B8; + + /* Typography — font families */ + --pe-font-family-primary: Inter, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; + --pe-font-family-secondary: Inter, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; + --pe-font-family-body: Inter, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; + --pe-font-family-chart: Inter, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; + --pe-font-family-prose: Inter, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; + --pe-font-family-mono: JetBrains Mono, "Fira Code", Consolas, monospace; + + /* Typography — font sizes */ + --pe-font-size-xs: 12px; + --pe-font-size-sm: 14px; + --pe-font-size-base: 16px; + --pe-font-size-lg: 18px; + --pe-font-size-xl: 20px; + --pe-font-size-2xl: 24px; + --pe-font-size-3xl: 28px; + --pe-font-size-4xl: 32px; + + /* Typography — font weights */ + --pe-font-weight-thin: 100; + --pe-font-weight-light: 300; + --pe-font-weight-normal: 400; + --pe-font-weight-medium: 500; + --pe-font-weight-semibold: 600; + --pe-font-weight-bold: 700; + --pe-font-weight-extrabold: 800; + --pe-font-weight-black: 900; + + /* Typography — line heights */ + --pe-line-height-20: 20px; + --pe-line-height-22: 22px; + --pe-line-height-24: 24px; + --pe-line-height-none: 1; + --pe-line-height-tight: 1.25; + --pe-line-height-snug: 1.375; + --pe-line-height-normal: 1.5; + --pe-line-height-relaxed: 1.625; + --pe-line-height-loose: 2; + + /* Spacing — base scale */ + --pe-space-xs: 4px; + --pe-space-sm: 8px; + --pe-space-md: 12px; + --pe-space-lg: 16px; + --pe-space-xl: 20px; + --pe-space-2xl: 24px; + --pe-space-3xl: 32px; + --pe-space-4xl: 48px; + --pe-space-5xl: 64px; + + /* Spacing — border radius */ + --pe-radius-none: 0px; + --pe-radius-chip: 2px; + --pe-radius-element: 4px; + --pe-radius-container: 8px; + --pe-radius-feature: 12px; +} diff --git a/app/public/paper/web/policybench-theme.css b/app/public/paper/web/policybench-theme.css new file mode 100644 index 0000000..cd73c1b --- /dev/null +++ b/app/public/paper/web/policybench-theme.css @@ -0,0 +1,344 @@ +@import url("https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&family=JetBrains+Mono:wght@400;500;600&display=swap"); + +:root { + color-scheme: light; + --paper-bg: var(--pe-color-bg-secondary); + --paper-surface: var(--pe-color-bg-primary); + --paper-surface-muted: var(--pe-color-bg-tertiary); + --paper-text: var(--pe-color-text-primary); + --paper-text-muted: var(--pe-color-text-secondary); + --paper-text-tertiary: var(--pe-color-text-tertiary); + --paper-border: var(--pe-color-border-light); + --paper-border-strong: var(--pe-color-border-medium); + --paper-link: var(--pe-color-text-link); + --paper-link-hover: var(--pe-color-text-linkHover); + --paper-accent: var(--pe-color-primary-600); + --paper-accent-soft: var(--pe-color-primary-50); + --paper-code-bg: color-mix(in srgb, var(--pe-color-primary-50) 28%, white); + --paper-shadow: + 0 20px 48px color-mix(in srgb, var(--pe-color-primary-900) 10%, transparent), + 0 2px 8px rgba(16, 24, 40, 0.04); +} + +*, +*::before, +*::after { + box-sizing: border-box; +} + +html { + background: + radial-gradient(circle at top left, color-mix(in srgb, var(--pe-color-primary-100) 46%, transparent) 0%, transparent 32%), + linear-gradient(180deg, #fbfdff 0%, var(--paper-bg) 100%); + color: var(--paper-text); + scroll-behavior: smooth; +} + +body { + margin: 0 auto; + max-width: 1220px; + padding: var(--pe-space-3xl) var(--pe-space-2xl) var(--pe-space-5xl); + display: grid; + grid-template-columns: minmax(0, 760px) minmax(250px, 320px); + column-gap: var(--pe-space-4xl); + align-items: start; + font-family: var(--pe-font-family-prose); + font-size: var(--pe-font-size-base); + line-height: var(--pe-line-height-relaxed); + color: var(--paper-text); +} + +body > * { + min-width: 0; +} + +body > :not(header#title-block-header):not(nav#TOC) { + grid-column: 1; +} + +header#title-block-header { + grid-column: 1 / -1; + margin: 0 0 var(--pe-space-3xl); + padding: var(--pe-space-4xl); + border: 1px solid var(--paper-border); + border-radius: var(--pe-radius-feature); + background: + linear-gradient(140deg, white 0%, #fbfdff 58%, color-mix(in srgb, var(--pe-color-primary-50) 55%, white) 100%); + box-shadow: var(--paper-shadow); +} + +header#title-block-header .title { + margin: 0; + font-family: var(--pe-font-family-primary); + font-size: clamp(2.5rem, 5vw, 4.2rem); + font-weight: var(--pe-font-weight-extrabold); + line-height: var(--pe-line-height-tight); + letter-spacing: -0.045em; + color: var(--paper-text); +} + +header#title-block-header .subtitle { + margin: var(--pe-space-lg) 0 0; + max-width: 44rem; + font-size: clamp(1.125rem, 2vw, 1.375rem); + line-height: var(--pe-line-height-relaxed); + color: var(--paper-text-muted); +} + +header#title-block-header .author { + display: inline-flex; + margin: var(--pe-space-2xl) 0 0; + padding: 0.5rem 0.875rem; + border: 1px solid color-mix(in srgb, var(--paper-accent) 20%, var(--paper-border)); + border-radius: 999px; + background: color-mix(in srgb, var(--paper-accent-soft) 70%, white); + font-family: var(--pe-font-family-mono); + font-size: var(--pe-font-size-xs); + font-weight: var(--pe-font-weight-medium); + letter-spacing: 0.08em; + text-transform: uppercase; + color: var(--paper-accent); +} + +nav#TOC { + grid-column: 2; + grid-row: 2 / span 999; + position: sticky; + top: var(--pe-space-3xl); + margin: 0; + padding: var(--pe-space-2xl); + border: 1px solid var(--paper-border); + border-radius: var(--pe-radius-feature); + background: color-mix(in srgb, white 90%, var(--pe-color-bg-secondary)); + box-shadow: 0 10px 24px rgba(16, 24, 40, 0.05); +} + +nav#TOC h2 { + margin: 0 0 var(--pe-space-lg); + font-family: var(--pe-font-family-primary); + font-size: var(--pe-font-size-sm); + font-weight: var(--pe-font-weight-semibold); + letter-spacing: -0.02em; + color: var(--paper-text); +} + +nav#TOC ul { + margin: 0; + padding-left: 1rem; +} + +nav#TOC li + li { + margin-top: 0.375rem; +} + +nav#TOC a { + color: var(--paper-text-muted); + text-decoration: none; +} + +nav#TOC a:hover, +nav#TOC a.active { + color: var(--paper-link); +} + +h2, +h3, +h4 { + margin-top: var(--pe-space-4xl); + margin-bottom: var(--pe-space-lg); + scroll-margin-top: 5rem; + font-family: var(--pe-font-family-primary); + font-weight: var(--pe-font-weight-bold); + letter-spacing: -0.03em; + line-height: var(--pe-line-height-tight); + color: var(--paper-text); +} + +h2 { + font-size: clamp(1.55rem, 3vw, 2rem); +} + +h3 { + font-size: clamp(1.2rem, 2.2vw, 1.45rem); +} + +h4 { + font-size: 1.05rem; +} + +p, +ul, +ol, +dl { + margin-top: 0; + margin-bottom: var(--pe-space-lg); + color: var(--paper-text); +} + +ul, +ol { + padding-left: 1.25rem; +} + +li + li { + margin-top: 0.35rem; +} + +a { + color: var(--paper-link); + text-decoration-thickness: 0.08em; + text-underline-offset: 0.16em; +} + +a:hover { + color: var(--paper-link-hover); +} + +code, +pre, +kbd, +samp { + font-family: var(--pe-font-family-mono); +} + +code { + padding: 0.12rem 0.35rem; + border-radius: var(--pe-radius-chip); + background: var(--paper-code-bg); + font-size: 0.92em; +} + +pre { + overflow-x: auto; + padding: var(--pe-space-xl); + border: 1px solid var(--paper-border); + border-radius: var(--pe-radius-container); + background: #fcfefe; + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.75); +} + +pre code { + padding: 0; + background: transparent; +} + +blockquote { + margin: var(--pe-space-2xl) 0; + padding: 0.25rem 0 0.25rem var(--pe-space-xl); + border-left: 3px solid var(--paper-accent); + color: var(--paper-text-muted); +} + +table { + width: 100%; + margin: var(--pe-space-2xl) 0; + border-collapse: collapse; + border-spacing: 0; + font-size: var(--pe-font-size-sm); + background: var(--paper-surface); + border: 1px solid var(--paper-border); + border-radius: var(--pe-radius-container); + overflow: hidden; +} + +thead th { + padding: 0.8rem 0.9rem; + border-bottom: 1px solid var(--paper-border); + background: var(--paper-surface-muted); + font-family: var(--pe-font-family-primary); + font-size: var(--pe-font-size-xs); + font-weight: var(--pe-font-weight-semibold); + letter-spacing: 0.03em; + text-transform: uppercase; + color: var(--paper-text-muted); +} + +tbody td { + padding: 0.8rem 0.9rem; + border-top: 1px solid var(--paper-border); + vertical-align: top; +} + +tbody tr:nth-child(even) td { + background: color-mix(in srgb, var(--paper-surface-muted) 36%, white); +} + +.callout { + margin: var(--pe-space-2xl) 0; + padding: var(--pe-space-xl); + border: 1px solid color-mix(in srgb, var(--paper-accent) 24%, var(--paper-border)); + border-radius: var(--pe-radius-container); + background: color-mix(in srgb, var(--paper-accent-soft) 62%, white); +} + +.callout .callout-title-container { + font-family: var(--pe-font-family-primary); + font-weight: var(--pe-font-weight-semibold); + color: var(--paper-text); +} + +.callout p:last-child, +.callout ul:last-child, +.callout ol:last-child { + margin-bottom: 0; +} + +.citation, +.quarto-xref { + color: var(--paper-link); +} + +.anchored { + position: relative; +} + +.anchored .anchorjs-link { + color: var(--paper-text-tertiary); +} + +.anchored .anchorjs-link:hover { + color: var(--paper-link); +} + +@media (max-width: 1024px) { + body { + max-width: 860px; + grid-template-columns: minmax(0, 1fr); + row-gap: var(--pe-space-2xl); + padding: var(--pe-space-2xl) var(--pe-space-lg) var(--pe-space-4xl); + } + + nav#TOC { + grid-column: 1; + grid-row: auto; + position: relative; + top: auto; + order: 2; + } + + body > :not(header#title-block-header):not(nav#TOC) { + grid-column: 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font-weight: bold; + font-style: inherit; +} + +/* Char */ +code span.ch { + color: #20794D; + font-style: inherit; +} + +/* Constant */ +code span.cn { + color: #8f5902; + font-style: inherit; +} + +/* Comment */ +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +/* CommentVar */ +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +/* Documentation */ +code span.do { + color: #5E5E5E; + font-style: italic; +} + +/* DataType */ +code span.dt { + color: #AD0000; + font-style: inherit; +} + +/* DecVal */ +code span.dv { + color: #AD0000; + font-style: inherit; +} + +/* Error */ +code span.er { + color: #AD0000; + font-style: inherit; +} + +/* Extension */ +code span.ex { + font-style: inherit; +} + +/* Float */ +code span.fl { + color: #AD0000; + font-style: inherit; +} + +/* Function */ +code span.fu { + color: #4758AB; + font-style: inherit; +} + +/* Import */ +code span.im { + color: #00769E; + font-style: inherit; +} + +/* Information */ +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +/* Keyword */ +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +/* Operator */ +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +/* Other */ +code span.ot { + color: #003B4F; + font-style: inherit; +} + +/* Preprocessor */ +code span.pp { + color: #AD0000; + font-style: inherit; +} + +/* SpecialChar */ +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +/* SpecialString */ +code span.ss { + color: #20794D; + font-style: inherit; +} + +/* String */ +code span.st { + color: #20794D; + font-style: inherit; +} + +/* Variable */ +code span.va { + color: #111111; + font-style: inherit; +} + +/* VerbatimString */ +code span.vs { + color: #20794D; + font-style: inherit; +} + +/* Warning */ +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +.prevent-inlining { + content: " !el.hasAttribute("disabled") && !el.getAttribute("aria-hidden") + ); + }; + + /** + * Remove roles and attributes from a tab and its content + * @param {Node} tab The tab + * @param {Node} content The tab content + * @param {Object} settings User settings and options + */ + var destroyTab = function (tab, content, settings) { + // Remove the generated ID + if (tab.id.slice(0, settings.idPrefix.length) === settings.idPrefix) { + tab.id = ""; + } + + // remove event listener + tab.removeEventListener("focus", focusHandler, true); + + // Remove roles + tab.removeAttribute("role"); + tab.removeAttribute("aria-controls"); + tab.removeAttribute("aria-selected"); + tab.removeAttribute("tabindex"); + tab.closest("li").removeAttribute("role"); + content.removeAttribute("role"); + content.removeAttribute("aria-labelledby"); + content.removeAttribute("hidden"); + }; + + /** + * Add the required roles and attributes to a tab and its content + * @param {Node} tab The tab + * @param {Node} content The tab content + * @param {Object} settings User settings and options + */ + var setupTab = function (tab, content, settings) { + // Give tab an ID if it doesn't already have one + if (!tab.id) { + tab.id = settings.idPrefix + content.id; + } + + // Add roles + tab.setAttribute("role", "tab"); + tab.setAttribute("aria-controls", content.id); + tab.closest("li").setAttribute("role", "presentation"); + content.setAttribute("role", "tabpanel"); + content.setAttribute("aria-labelledby", tab.id); + + // Add selected state + if (tab.matches(settings.default)) { + tab.setAttribute("aria-selected", "true"); + } else { + tab.setAttribute("aria-selected", "false"); + content.setAttribute("hidden", "hidden"); + } + + // add focus event listender + tab.addEventListener("focus", focusHandler); + }; + + /** + * Hide a tab and its content + * @param {Node} newTab The new tab that's replacing it + */ + var hide = function (newTab) { + // Variables + var tabGroup = newTab.closest('[role="tablist"]'); + if (!tabGroup) return {}; + var tab = tabGroup.querySelector('[role="tab"][aria-selected="true"]'); + if (!tab) return {}; + var content = document.querySelector(tab.hash); + + // Hide the tab + tab.setAttribute("aria-selected", "false"); + + // Hide the content + if (!content) return { previousTab: tab }; + content.setAttribute("hidden", "hidden"); + + // Return the hidden tab and content + return { + previousTab: tab, + previousContent: content, + }; + }; + + /** + * Show a tab and its content + * @param {Node} tab The tab + * @param {Node} content The tab content + */ + var show = function (tab, content) { + tab.setAttribute("aria-selected", "true"); + content.removeAttribute("hidden"); + tab.focus(); + }; + + /** + * Toggle a new tab + * @param {Node} tab The tab to show + */ + var toggle = function (tab) { + // Make sure there's a tab to toggle and it's not already active + if (!tab || tab.getAttribute("aria-selected") == "true") return; + + // Variables + var content = document.querySelector(tab.hash); + if (!content) return; + + // Hide active tab and content + var details = hide(tab); + + // Show new tab and content + show(tab, content); + + // Add event details + details.tab = tab; + details.content = content; + + // Emit a custom event + emitEvent(tab, details); + }; + + /** + * Get all of the tabs in a tablist + * @param {Node} tab A tab from the list + * @return {Object} The tabs and the index of the currently active one + */ + var getTabsMap = function (tab) { + var tabGroup = tab.closest('[role="tablist"]'); + var tabs = tabGroup ? tabGroup.querySelectorAll('[role="tab"]') : null; + if (!tabs) return; + return { + tabs: tabs, + index: Array.prototype.indexOf.call(tabs, tab), + }; + }; + + /** + * Switch the active tab based on keyboard activity + * @param {Node} tab The currently active tab + * @param {Key} key The key that was pressed + */ + var switchTabs = function (tab, key) { + // Get a map of tabs + var map = getTabsMap(tab); + if (!map) return; + var length = map.tabs.length - 1; + var index; + + // Go to previous tab + if (["ArrowUp", "ArrowLeft", "Up", "Left"].indexOf(key) > -1) { + index = map.index < 1 ? length : map.index - 1; + } + + // Go to next tab + else if (["ArrowDown", "ArrowRight", "Down", "Right"].indexOf(key) > -1) { + index = map.index === length ? 0 : map.index + 1; + } + + // Go to home + else if (key === "Home") { + index = 0; + } + + // Go to end + else if (key === "End") { + index = length; + } + + // Toggle the tab + toggle(map.tabs[index]); + }; + + /** + * Create the Constructor object + */ + var Constructor = function (selector, options) { + // + // Variables + // + + var publicAPIs = {}; + var settings, tabWrapper; + + // + // Methods + // + + publicAPIs.destroy = function () { + // Get all tabs + var tabs = tabWrapper.querySelectorAll("a"); + + // Add roles to tabs + Array.prototype.forEach.call(tabs, function (tab) { + // Get the tab content + var content = document.querySelector(tab.hash); + if (!content) return; + + // Setup the tab + destroyTab(tab, content, settings); + }); + + // Remove role from wrapper + tabWrapper.removeAttribute("role"); + + // Remove event listeners + document.documentElement.removeEventListener( + "click", + clickHandler, + true + ); + tabWrapper.removeEventListener("keydown", keyHandler, true); + + // Reset variables + settings = null; + tabWrapper = null; + }; + + /** + * Setup the DOM with the proper attributes + */ + publicAPIs.setup = function () { + // Variables + tabWrapper = document.querySelector(selector); + if (!tabWrapper) return; + var tabs = tabWrapper.querySelectorAll("a"); + + // Add role to wrapper + tabWrapper.setAttribute("role", "tablist"); + + // Add roles to tabs. provide dynanmic tab indexes if we are within reveal + var contentTabindexes = + window.document.body.classList.contains("reveal-viewport"); + var nextTabindex = 1; + Array.prototype.forEach.call(tabs, function (tab) { + if (contentTabindexes) { + tab.setAttribute("tabindex", "" + nextTabindex++); + } else { + tab.setAttribute("tabindex", "0"); + } + + // Get the tab content + var content = document.querySelector(tab.hash); + if (!content) return; + + // set tab indexes for content + if (contentTabindexes) { + getKeyboardFocusableElements(content).forEach(function (el) { + el.setAttribute("tabindex", "" + nextTabindex++); + }); + } + + // Setup the tab + setupTab(tab, content, settings); + }); + }; + + /** + * Toggle a tab based on an ID + * @param {String|Node} id The tab to toggle + */ + publicAPIs.toggle = function (id) { + // Get the tab + var tab = id; + if (typeof id === "string") { + tab = document.querySelector( + selector + ' [role="tab"][href*="' + id + '"]' + ); + } + + // Toggle the tab + toggle(tab); + }; + + /** + * Handle click events + */ + var clickHandler = function (event) { + // Only run on toggles + var tab = event.target.closest(selector + ' [role="tab"]'); + if (!tab) return; + + // Prevent link behavior + event.preventDefault(); + + // Toggle the tab + toggle(tab); + }; + + /** + * Handle keydown events + */ + var keyHandler = function (event) { + // Only run if a tab is in focus + var tab = document.activeElement; + if (!tab.matches(selector + ' [role="tab"]')) return; + + // Only run for specific keys + if (["Home", "End"].indexOf(event.key) < 0) return; + + // Switch tabs + switchTabs(tab, event.key); + }; + + /** + * Initialize the instance + */ + var init = function () { + // Merge user options with defaults + settings = extend(defaults, options || {}); + + // Setup the DOM + publicAPIs.setup(); + + // Add event listeners + document.documentElement.addEventListener("click", clickHandler, true); + tabWrapper.addEventListener("keydown", keyHandler, true); + }; + + // + // Initialize and return the Public APIs + // + + init(); + return publicAPIs; + }; + + // + // Return the Constructor + // + + return Constructor; + } +); diff --git a/app/public/paper/web/site_libs/quarto-html/tippy.css b/app/public/paper/web/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ 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e7b8dfb..0000000 --- a/app/public/vite.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/app/src/App.tsx b/app/src/App.tsx index a747972..3957843 100644 --- a/app/src/App.tsx +++ b/app/src/App.tsx @@ -1,26 +1,193 @@ +"use client"; + +import { useEffect, useMemo, useRef, useState } from "react"; +import rawData from "./data.json"; import Hero from "./components/Hero"; -import ModelComparison from "./components/ModelComparison"; -import ProgramBreakdown from "./components/ProgramBreakdown"; -import ExampleScenarios from "./components/ExampleScenarios"; +import FailureModes from "./components/FailureModes"; +import Methodology from "./components/Methodology"; +import ModelLeaderboard from "./components/ModelLeaderboard"; +import ProgramHeatmap from "./components/ProgramHeatmap"; +import ScenarioExplorer from "./components/ScenarioExplorer"; +import type { BenchData, CountryCode, DashboardBundle, ViewKey } from "./types"; +import { VIEW_LABELS } from "./types"; + +const dashboard = rawData as DashboardBundle; + +export type { DashboardBundle } from "./types"; + +const COUNTRY_NAV_ITEMS = [ + { id: "models", label: "Models" }, + { id: "scenarios", label: "Scenarios" }, + { id: "failure-modes", label: "Failure" }, + { id: "programs", label: "Programs" }, + { id: "methodology", label: "Method" }, +] as const; + +const GLOBAL_NAV_ITEMS = [ + { id: "models", label: "Models" }, + { id: "methodology", label: "Method" }, +] as const; + +const COUNTRY_ORDER: CountryCode[] = ["us", "uk"]; + +function getAvailableViews(dashboard: DashboardBundle): ViewKey[] { + const countryViews = COUNTRY_ORDER.filter((country) => dashboard.countries[country]); + if (dashboard.global?.modelStats.length && countryViews.length > 1) { + return ["global", ...countryViews]; + } + return countryViews; +} export default function App() { + const availableViews = useMemo(() => getAvailableViews(dashboard), []); + const defaultView = availableViews.includes("uk") + ? "uk" + : (availableViews[0] ?? "global"); + const [selectedView, setSelectedView] = useState(defaultView); + const [activeNav, setActiveNav] = useState("models"); + const observerRef = useRef(null); + + const isGlobal = selectedView === "global"; + const data = isGlobal + ? dashboard.global! + : dashboard.countries[selectedView as CountryCode]!; + const navItems = isGlobal ? GLOBAL_NAV_ITEMS : COUNTRY_NAV_ITEMS; + const handleSelectView = (view: ViewKey) => { + setSelectedView(view); + setActiveNav("models"); + }; + + useEffect(() => { + if (observerRef.current) observerRef.current.disconnect(); + + const sectionIds = navItems.map((item) => item.id); + + observerRef.current = new IntersectionObserver( + (entries) => { + for (const entry of entries) { + if (entry.isIntersecting) { + setActiveNav(entry.target.id); + } + } + }, + { rootMargin: "-40% 0px -55% 0px" }, + ); + + for (const id of sectionIds) { + const el = document.getElementById(id); + if (el) observerRef.current.observe(el); + } + + return () => { + observerRef.current?.disconnect(); + }; + }, [navItems]); + + const noToolsModels = useMemo( + () => data.modelStats.filter((m) => m.condition === "no_tools"), + [data], + ); + + const footerCopy = useMemo(() => { + if (isGlobal) { + const totalHouseholds = Object.values(dashboard.countries).reduce( + (sum, country) => sum + Object.keys(country?.scenarios ?? {}).length, + 0 + ); + const countryCount = Object.keys(dashboard.countries).length; + return `PolicyBench — global leaderboard across ${dashboard.global?.sharedModelCount ?? 0} shared frontier models, ${countryCount} country benchmarks, and ${totalHouseholds.toLocaleString()} households.`; + } + + const countryData = data as BenchData; + const scoredRows = noToolsModels.reduce((sum, model) => sum + model.n, 0); + return `PolicyBench — ${VIEW_LABELS[countryData.country]} benchmark with ${scoredRows.toLocaleString()} scored outputs across ${noToolsModels.length} frontier models, ${countryData.programStats.length} programs, and ${Object.keys(countryData.scenarios).length} household scenarios.`; + }, [data, isGlobal, noToolsModels]); + return ( -
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