planning-benchmark / README.md
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Dataset card — Planning Benchmark v2 (50-task open split) + superseded v1
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---
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: 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
**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`answer_key.forbidden_figures` (stale/superseded/derived values with reason
codes: `prior_year_value`, `superseded`, `derived_not_published`,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- **v1 (superseded, kept for citation continuity):** the original 14-task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@capstera.com · <https://www.wealthschema.com/for-agents>