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A model can classify every cash-delivery case correctly and still fail the task if its response is not valid JSON in the required schema. In Hongli Wang’s small synthetic benchmark, a Gemini 3.7 Flash response received a strict score of 0% because it was wrapped in Markdown fences—even though removing only those fences from a preserved copy yielded a diagnostic 48/48 field matches. That diagnostic was not the recorded task result.
What the benchmark tests
Wang’s benchmark combines two requirements: reason about evidence for cash-related delivery events, then return the result in an exact machine-readable format. It uses 16 original fictional cases and nine delivery states. For each case, the requested JSON object must contain exactly four keys: id, status, cash_eligible, and revenue_usd.
The model receives the frozen rubric and case narratives, but not the separate gold labels. The primary score averages 48 exact field comparisons across the 16 cases. IDs are validated to match rows, rather than scored as a field; rows are matched by ID, and order compliance is reported separately.
The synthetic cash rules
The rubric defines which receipts count for this exercise; it is not a statement about market value or legal treatment. Under its rules, qualifying receipts include:
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Points and unconverted ETH do not qualify. If a qualifying receipt is in a non-USD currency and no conversion is documented, its USD amount remains unknown. This makes evidence quality part of the classification: the answer must not turn an unverified amount into a known one.
Why valid JSON is part of correctness
The evaluator enforces a strict output contract. It rejects prose around the answer, Markdown fences, missing or duplicate cases, duplicate keys, extra fields, invalid types, and non-finite amounts. A response that communicates the intended labels to a person can therefore fail when a parser cannot consume it exactly as specified.
That is what happened in the first reported Gemini 3.7 Flash interactive run: the raw response was fenced in Markdown and received a strict 0% evaluator result. On a preserved copy, removing only the fence produced a 48/48 field-match diagnostic. That cleanup check helps isolate formatting from field accuracy, but it does not replace or change the recorded task score.
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Unknown, false, and zero are not interchangeable
The rubric also depends on preserving distinctions between missing evidence and a negative finding. null can represent an unknown value, false a known negative eligibility result, and 0 an evidenced amount of zero. Substituting one for another changes the meaning of the output, even if the JSON parses.
Wang reports that nine unknown received-asset eligibility values became false in the local model’s answers. The report also notes errors involving successful platform registrations, an untested implementation, and an unverified gift card. The shared lesson is narrow but important: absence of proof should not silently become proof of ineligibility, and an unverified claim should not be promoted to an established fact.
Reported results—and what they establish
The results below are reported in Wang’s October 5, 2026 account. The two formal results and their schema assertions are described as Kaggle-platform-reported; the author says they were not independently recomputed. The other figures are author-reported observations, not comparable formal leaderboard results.
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| Run | Reported result | Context |
|---|---|---|
| Gemini 3.7 Flash | 100.0% strict | Formal v1 task build; Kaggle-platform-reported, 2026 |
| GPT-5.4 mini | 97.9% strict | Formal v1 task run; Kaggle-platform-reported, 2026 |
| Gemini 3.7 Flash | 0% strict; 48/48 field-match diagnostic after fence removal | First interactive run; author-reported evaluator result, 2026. The diagnostic was not the recorded task result. |
| Qwen 3.6-35B-A3B, quantized | 35/48 field matches; 6/16 complete cases | Earlier offline CPU run, with a different recorded system-instruction context; author-reported preparation result, 2026, not included in the leaderboard |
The local evaluator’s 23 automated tests passed, including single-field mutations and malformed-output checks. That supports the evaluator’s described behavior; it is not evidence of general model ability.
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Why the scores should not be generalized
“One perfect platform result does not establish repeatability,” Wang writes. The formal Gemini result and the interactive run used distinct execution contexts, and the report does not describe a controlled repeated-seed experiment. The benchmark is small, fictional, public, and driven by an explicit rubric; it has no held-out set or repeated-seed study. Public labels may also permit memorization.
The comparison is therefore about observed runs under different conditions, not a stable ranking of models for financial work. A strict score captures both decision quality and compliance with this particular schema. The cleaned interactive response is a useful diagnostic of one formatting failure, but it cannot stand in for another formal run.
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The report further cautions that its narrower completion diagnostic does not by itself capture accepted-to-merged upgrades. That limitation reinforces the need to interpret any single metric according to exactly what it measures.
What this means when building a structured-output task
For a consumer that requires exact JSON, semantic judgment and interface compliance are separate failure points. A robust evaluation should make both visible rather than treating a plausible-looking answer as success.
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- Specify the allowed keys, types, case count, and treatment of unknown values before evaluation.
- Validate syntax and schema independently from field-level correctness, so a formatting failure can be distinguished from a reasoning error.
- Test malformed responses and one-field mutations. Wang reports 23 local evaluator tests, but those tests validate implementation behavior rather than model reliability.
- Report execution context and verification status alongside scores: formal versus interactive, model identifier, strict score versus diagnostic, and platform-reported versus independently checked.
- Use repeated runs and a held-out set before drawing stronger conclusions about consistency or performance beyond a small synthetic task.
The article describes SDK integration checks as implementation and interface checks, not evidence that a model performs reliably outside this benchmark.
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