A model can return perfectly valid JSON and still apply a patch incorrectly. In a small Kaggle benchmark, GPT-5.4 nano produced valid JSON with valid field types in all 36 cases, but matched the expected state in only 24. The test’s central lesson is that parsing, schema compliance, and correct state updates are separate things to measure.
What the benchmark tests
World Programming’s Valid JSON is not enough: testing bilingual patch contracts on Kaggle, published October 1, 2026, describes Bilingual Patch Contracts, a hand-authored diagnostic suite for testing whether a model follows instructions that update structured state.
The suite has 12 semantic scenarios. Each scenario has three instruction-body variants—English, Chinese, and code-switched—for 36 prompts total. The variants share the same initial state and expected answer. The contract prefix and required output keys remain in English, so this is not a fully Chinese interaction benchmark.
The scenarios probe behaviors that can make a seemingly simple patch error-prone:
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- Applying a later correction or a negated instruction.
- Preserving the distinction between null and an empty value.
- Handling ordered, case-sensitive tags.
- Converting hours to minutes and applying sequential conditions.
- Treating instruction-like text as literal data rather than as a new instruction.
- Copying Unicode, backslashes, quotation marks, and a newline exactly.
What counts as a pass
A passing response must be a single JSON object with exactly five keys, the required types, and every expected value. The scorer does not remove Markdown, repair malformed output, or use another model to judge correctness.
Some differences that do not change the represented data are accepted: whitespace, key order, and equivalent Unicode escapes. Others fail: duplicate keys, extra fields, nonfinite values, booleans or floating-point numbers in integer fields, and arrays in the wrong order.
This creates distinct evaluation questions. Is the response parseable JSON? Does it satisfy the required schema? Does it contain the exact intended state? A response can pass the first two and fail the third.
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Results from the October 1, 2026 run
The author reports running the complete version 2 suite on Kaggle on October 1, 2026. The report says raw responses were downloaded, all 36 unique case IDs were checked against frozen prompts and answers, and saved scores were independently recalculated.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall| Model | Strict exact match | Valid JSON | Valid schema |
|---|---|---|---|
| Gemini 3.7 Flash | 36/36 (100%) | 36/36 | 36/36 |
| GPT-5.4 nano | 24/36 (66.7%) | 36/36 | 36/36 |
| Claude Haiku 4.5 | 0/36 (0%) | 0/36 | 0/36 |
| Qwen3-Next-80B-A3B-Instruct | No complete score | No complete score | No complete score |
These are the benchmark author’s figures for one run of 12 handcrafted scenarios represented by 36 language-variant prompts; they are not population estimates or independent replications. Qwen3-Next-80B-A3B-Instruct was attempted, but pilot and version 2 runs stopped with HTTP 429 responses and a provider heavy-load message. It was excluded rather than scored as zero.
How valid output can still be wrong—or unusable
GPT-5.4 nano: correct format, wrong state
GPT-5.4 nano returned syntactically valid JSON and valid field types in every case, yet 12 responses had incorrect values. In the case-sensitive tags example, it left lowercase beta in the state even though the instruction said to remove it. A parser and type checker would accept that response; only checking the intended state reveals the mistake.
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Claude Haiku 4.5: Markdown breaks the strict interface
Claude Haiku 4.5 wrapped every answer in a Markdown code fence despite an explicit instruction not to use Markdown. Under a strict consumer expecting a raw JSON document, the fenced response is not valid JSON as a complete response, even when the values inside the fence are correct. The author separately reports that removing only complete outer fences would make 33 of 36 cases pass value checks. That is a counterfactual diagnostic, not the benchmark score, and it does not change the reported leaderboard result.
What the language comparisons do—and do not—show
For GPT-5.4 nano, the mixed-language total was two cases higher than its English total. Looking at matched scenarios gives a more limited picture: seven passed in both English and mixed, three failed in both, and two passed only in mixed. English-versus-Chinese comparisons were also mixed.
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Those observations identify examples worth inspecting; they do not establish that the model is generally stronger in Chinese or code-switching. The instruction bodies were hand-authored, and phrasing and token lengths were not perfectly controlled. The shared English contract instructions also limit how far the results support claims about multilingual interaction.
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How to interpret the score
This is a small diagnostic benchmark, not a general model ranking. Its 36 prompts are paired language variants of 12 underlying semantic scenarios—not 36 independent semantic problems. A single run cannot establish production reliability, and Gemini’s perfect score is a ceiling on this suite: it shows success on these examples but cannot distinguish reliability beyond them.
The author says version 2 corrected task registration so Kaggle selects the whole-suite aggregate rather than a helper function; prompts, fixtures, and scorer were unchanged. One numeric task calculates strict exact matches divided by 36, and with one task the overall score equals that task score. Infrastructure errors abort the suite instead of silently reducing the denominator.
The reported setup used ordinary text generation with temperature 0 and seed 0 requested through the SDK, with a fresh isolated conversation for each case. It did not use constrained JSON decoding, schema enforcement, or tools. Provider behavior may vary across runs. Latency, cost, and tool calling were not benchmarked.
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What a patch evaluation should report
The benchmark illustrates why an evaluation of structured updates should separate interface compliance from semantic correctness. A useful report can show, at minimum:
- Whether the complete response parses as JSON without cleanup.
- Whether it obeys the schema, including required keys, types, and restrictions on extra or duplicate fields.
- Whether every resulting value matches the expected state, including ordering and exact-copy requirements.
- Whether output-format failures, such as Markdown fences, are counted separately from incorrect state values.
- How language variants are paired, and how many underlying scenarios—not just prompts—were tested.
The article’s public backing notebook is described as containing the embedded cases, expected states, scorer, and run artifacts including contract_results.json and contract_summary.json. The reported findings are the author’s own results, not an independent replication.
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