Add Introducing PolicyBench research post

app/src/data/posts/articles/introducing-policybench.md

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+People increasingly ask AI models to help them understand their taxes and benefits. We built PolicyBench to measure a concrete question underneath that: can a frontier model compute a household's taxes and benefits from the prompt alone — no tools, no lookups, one structured answer?
+
+[PolicyBench](https://policybench.org) gives each model the same household description and asks for every scored tax and benefit output plus a short explanation, with no tool use, and scores the answers against PolicyEngine-US.
+
+## The setup
+
+PolicyBench evaluates 13 frontier models on 100 US households drawn from PolicyEngine's populace microdata, across 18 tax and benefit outputs per household, for tax year 2026.
+
+The headline metric is exact match: the share of outputs where a model's value equals PolicyEngine's exactly — to the dollar for amounts, and the right decision for eligibility flags. It is the deployability bar, since a household filing taxes or claiming a benefit needs the right number, not a near one. The public leaderboard weights each output by its impact on household resources across the population, which keeps exact match informative even though the reference is zero-inflated: 84% of reference outputs are exact zeros, because most households are not eligible for most programs, so an unweighted exact rate would mostly measure how often a model answers zero. We report a within-1% hit rate alongside it as a near-miss-tolerant companion.
+
+## GPT-5.5 leads
+
+GPT-5.5 ranks first, matching PolicyEngine exactly on 80.3% of its outputs. The second through ninth models fall between 76.0% and 77.8%, and GPT-5.4 nano scores lowest, at 62.2%.
+
+| Rank | Model                         | Exact | Within-1% |
+| ---- | ----------------------------- | ----- | --------- |
+| 1    | GPT-5.5                       | 80.3% | 82.6%     |
+| 2    | Gemini 3.1 Pro Preview        | 77.8% | 78.3%     |
+| 3    | Claude Opus 4.7               | 77.3% | 78.3%     |
+| 4    | Grok 4.3                      | 77.2% | 79.1%     |
+| 5    | Claude Sonnet 4.6             | 77.0% | 78.2%     |
+| 6    | Gemini 3 Flash Preview        | 76.8% | 77.5%     |
+| 7    | Gemini 3.5 Flash              | 76.1% | 76.9%     |
+| 8    | Gemini 3.1 Flash Lite Preview | 76.0% | 77.9%     |
+| 9    | Grok Build 0.1                | 76.0% | 77.0%     |
+| 10   | Claude Opus 4.8               | 72.5% | 73.8%     |
+| 11   | Claude Haiku 4.5              | 71.7% | 73.2%     |
+| 12   | GPT-5.4 mini                  | 70.4% | 72.3%     |
+| 13   | GPT-5.4 nano                  | 62.2% | 63.2%     |
+
+## Amounts are harder than eligibility
+
+The hardest outputs are the ones that require a multi-step dollar calculation. Federal and state income tax before credits are the lowest, at 47.1% and 53.3% exact: each requires selecting the right income concepts, applying exclusions and thresholds, and sequencing them correctly before arriving at a number. Payroll tax (68.2%) and SNAP (76.8%), a benefit paid as a computed amount, sit just above.
+
+The highest-scoring outputs are eligibility flags and programs that are zero for most households. Medicare, CHIP, WIC, SSI, TANF, and school-meal outputs mostly exceed 95% exact. Part of that is that many are yes/no eligibility decisions rather than amounts; part is that most households do not qualify, and a correct "does not apply" is easier than a correct dollar figure.
+
+So the divide is less taxes versus benefits than computed amounts versus eligibility. SNAP, a computed benefit, scores like the income taxes, while TANF and SSI — paid to a small share of households and zero for the rest — score above 96%. Models reliably decide whether a program applies; they are less reliable at computing how much.
+
+## Auditing the errors
+
+A benchmark is only as good as its reference. We reviewed by hand every cell where a model's answer was not an exact match — 3,300 in all. Every one was a model error; none was a PolicyEngine reference error.
+
+## The data and code are open
+
+PolicyBench includes a live leaderboard, a scenario explorer that exposes every prompt and every PolicyEngine reference output, and a [full paper](https://policybench.org/paper) documenting the method, scoring, and uncertainty. Because PolicyEngine is an open, deterministic calculator, it can serve as a reusable, open reference for evaluating AI on tax and benefit tasks.
+
+Explore the benchmark at [policybench.org](https://policybench.org) and read the [paper](https://policybench.org/paper).

app/src/data/posts/posts.json

@@ -1,4 +1,13 @@
 [
+  {
+    "title": "Introducing PolicyBench: how accurately can AI compute taxes and benefits?",
+    "description": "We tested 13 frontier language models on 100 representative US households, with no tools, scored against PolicyEngine. GPT-5.5 leads, getting 80.3% exactly right.",
+    "date": "2026-06-16",
+    "tags": ["us", "ai", "featured"],
+    "authors": ["max-ghenis"],
+    "filename": "introducing-policybench.md",
+    "image": "introducing-policybench.webp"
+  },
   {
     "title": "The Keep Your Pay Act's AMT interaction",
     "description": "Senator Booker's proposed standard deduction increase triggers AMT for some high earners, clawing back over half of tax savings compared to a simple bracket calculation.",

changelog_entry.yaml

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 - bump: patch
   changes:
     added:
+      - Add the Introducing PolicyBench research post
       - Add UK Universal Credit rebalancing analysis dashboard at /uk/uc-rebalancing
       - Add UK fuel duty rise cancellation analysis dashboard at /uk/cancelling-fuel-duty-rise
     changed: