ScenarioBench: Trace-Grounded Compliance Evaluation for Text-to-SQL and RAG
Bibliographic record
Abstract
ScenarioBench is a policy-grounded, trace-aware benchmark for evaluating Text-to-SQL and retrieval-augmented generation in compliance settings. Each YAML scenario ships with a no-peek gold-standard package-expected decision, minimal witness trace, governing clause set, and canonical SQL-enabling end-to-end scoring of both what a system decides and why it decides so. Systems must justify outputs with clause IDs retrieved from the same policy canon, making explanations falsifiable and audit-ready. The evaluator reports core signals-decision/trace quality, retrieval and SQL correctness (by result-set equivalence), latency, and a hallucination rate. A normalized Scenario Difficulty Index (SDI) and its budgeted variant (SDI-R) aggregate these while pricing retrieval difficulty and time. Unlike Spider/BIRD or KILT/RAG-style setups, ScenarioBench enforces clause-ID grounding under a strict nopeek rule. On a seed synthetic suite (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N=16$</tex>), accuracy saturates (Acc <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$=\mathbf{M}-\mathbf{F} 1=1.000)$</tex>, and a single budgeted reflection closes trace gaps (TrC & Coverage <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.541 \rightarrow 1.000$</tex>) with zero hallucination at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim+1 \text{ms}$</tex>-showing that marginal gains come from why-quality under explicit time budgets. Artifact & Code (v0.1.0): https://github.com/ShabnamAtf/ScenarioBench/releases/tag/v0.1.0
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".