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f2cf1f1 f2ca97c f2cf1f1 f2ca97c f2cf1f1 f2ca97c f2cf1f1 f2ca97c f2cf1f1 f2ca97c f2cf1f1 f2ca97c f2cf1f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | # Method independence
FinGovBench evaluates governance of a financial AI workflow. It does not require any
particular model architecture or graph-based submission.
## Independent benchmark object
Each public case declares the operational workflow boundary, versioned components,
dependencies, evidence available at the case's `as_of` time, assurance claims, authority
tier, permitted actions and observed change. This is a system specification analogous to
an architecture or lineage record. It contains no method-specific probabilities, path
contributions or certificates.
Gold outcomes are defined by the financial event and governance contract:
- SEC cases use an official accession-level reported-value pair and a declared workflow
contract; their labels remain provisional until independent domain adjudication.
- controlled cases use a prespecified event, authority boundary and executable contract.
- the evaluator, not a submitted method, determines whether the selected action and
revalidation scope permit release.
## What a submission must do
A method receives the same workflow declaration and must return the affected
components, governance action, revalidation components and release decision. A submission
may use rules, a flat uncertainty model, an LLM, a knowledge graph or another method.
It may transform the neutral workflow declaration into its preferred internal format.
## Optional graph-based methods
Graph-based uncertainty propagation is one optional evaluated approach. It must read
exactly the same public closed-loop inputs as lexical and flat-uncertainty comparators. No
method-specific telemetry or labels are provided.
## Anti-circularity requirements
Before version 1.0:
1. independent reviewers must adjudicate event validity, required action and revalidation
scope without seeing any method prediction;
2. submitted predictions cannot alter gold labels or case inclusion;
3. all methods receive the same public inputs and sealed test labels;
4. genuine and controlled results remain separate; and
5. any threshold or learned policy freezes on development data before test evaluation.
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