| --- |
| license: other |
| license_name: wealthschema-benchmark-license |
| license_link: https://www.wealthschema.com/license |
| task_categories: |
| - question-answering |
| language: |
| - en |
| tags: |
| - finance |
| - financial-planning |
| - benchmark |
| - evaluation |
| - tax |
| - llm-evaluation |
| - stale-figure-rate |
| pretty_name: WealthSchema Planning Benchmark |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: fiduciarybench |
| data_files: |
| - split: public |
| path: data/fiduciarybench-public.jsonl |
| - config_name: v2 |
| data_files: |
| - split: open |
| path: data/planning-benchmark-v2.jsonl |
| - config_name: v1 |
| data_files: |
| - split: open |
| path: data/planning-benchmark-v1.jsonl |
| --- |
| |
| # WealthSchema Planning Benchmark · now part of FiduciaryBench |
|
|
| **New (2026-09): the `fiduciarybench` config** carries the public split of |
| FiduciaryBench's conduct suites — Reg BI suitability, RMD mechanics, and |
| wash-sale mechanics — where every answer key is computed by code or derived |
| from a quoted primary-source passage (SEC, FINRA, IRS/Treasury, U.S. Code) |
| that exact-matches a locally held, versioned corpus. Per-item provenance |
| pages, methodology, and the standing challenge policy: |
| https://www.wealthschema.com/benchmark · Harness: `pip install fiduciarybench`. |
| The planning configs below are FiduciaryBench's planning-calculations family. |
|
|
| **Is your AI giving 2026 advice — or 2025 advice?** Evaluation tasks for AI |
| financial-advice systems, with answer keys built from primary-source-verified |
| U.S. regulatory figure tables — including the enumerated wrong-but-plausible |
| stale values (*forbidden figures*) with reason codes, so stale answers are |
| countable, not anecdotal. |
|
|
| - **v2 (current):** 50 tasks — the open split of the AI Eval Sets TY2026 |
| corpus. Rule-grounding and threshold/cliff families, floor/standard |
| difficulty. Each task carries `answer_key.required_figures` (value + unit + |
| establishing source document, e.g. IRS Notice 2025-67) and |
| `answer_key.forbidden_figures` (stale/superseded/derived values with reason |
| codes: `prior_year_value`, `superseded`, `derived_not_published`, |
| `fabricated_forward_figure`, …). Scoring is fully mechanical — no LLM judge. |
| - **v1 (superseded, kept for citation continuity):** the original 14-task |
| benchmark with `expected`/`computation` keys. |
|
|
| ## Headline metric: the Stale Figure Rate (SFR) |
|
|
| SFR = attempts asserting ≥1 forbidden figure as current ÷ figure-bearing |
| attempts. In the pre-registered pilot of record (40 held-out tasks, 4 systems |
| from 3 labs, k=3), **29% of figure-bearing attempts (86/300) cited a stale or |
| fabricated regulatory figure**; per-system SFR ranged 7%–63%, and errors |
| repeated across attempts (pass^3 ≈ pass@1). Full protocol, substitution log, |
| and per-system results: <https://www.wealthschema.com/resources/methodology/planning-benchmark-pilot-of-record> |
| · live scoreboard: <https://www.wealthschema.com/benchmark>. |
|
|
| ## Using it |
|
|
| Blind evaluation: present `prompt.system` + `prompt.user`, k=3 attempts; |
| score mechanically against `answer_key` (required figures present, forbidden |
| figures absent as the operative value, typed expectations matched). The live |
| API serves the same data with a blind mode: |
| `https://www.wealthschema.com/api/benchmark/v2?withhold_answers=true`. |
|
|
| Report pass@1, pass^k, and SFR **with the denominator, k, and vintage**. |
|
|
| ## Splits and what's deliberately absent |
|
|
| This open split is 50 of a 471-task corpus. The adversarial |
| categorical-flip family, all decision-recall tasks, and a held-out reserve |
| (including all 40 pilot tasks) are never published — that is what keeps the |
| maintainer-run scoreboard re-runnable and third-party comparisons meaningful. |
| The commercial families are sold as one-time eval packs: |
| <https://www.wealthschema.com/ai-eval-sets>. |
|
|
| ## License and training use |
|
|
| Free to use for evaluation, benchmarking, and research with attribution |
| ("WealthSchema Planning Benchmark, wealthschema.com/benchmark"). |
| **`training_use_permitted: false` on every record** — do not train, |
| fine-tune, or include these tasks in training corpora; training on |
| evaluation data destroys its value for everyone it is compared against. |
| Figures are informational, cited to primary government documents, and are |
| not tax, legal, or financial advice. |
|
|
| All scenarios are synthetic; no real persons, households, or accounts. |
| Vintage: each answer key is correct for its stated tax year (`vintage`, |
| `as_of_date`). Figures roll every January; a new vintage of this dataset |
| ships each year. |
|
|
| Questions: support@wealthschema.com · <https://www.wealthschema.com/for-agents> |
|
|
