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Why the same request ID replays the old answer
The benchmark treats a request ID as the identity of one logical operation. If a caller sends the same ID with the same payload again, the system is being asked for the outcome of that operation, not for a fresh decision. Confirmed outcomes, including failures, are cached under that ID. The cached conflict is returned even when the conflicting booking has since been removed.
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This is a deliberate choice. It means a retry after a timeout or a lost response gets a consistent answer, and it prevents an old rejection from silently becoming a new success. The cost is that a caller who wants a second attempt must signal that with a different ID.
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A worked example
The benchmark uses two fictional rooms, and its time intervals are integers that are half-open, written as [start, end). The end point is excluded, so a booking ending at 10 and another starting at 10 do not conflict. The example runs as follows:
- Create booking x. It occupies Room A from 0 to 10 (written [0,10)). A create starts at revision 1.
- Request booking y. Request r2 asks for Room A over [5,8). It overlaps x, so r2 is rejected, and the conflict is cached under r2.
- Cancel x. The cancellation is applied at revision 1, which frees Room A for that window.
- Retry the identical r2. Same ID, same payload. The benchmark replays the cached conflict. Booking y is still not created.
- Submit y as a new request, r4. With a new ID, the attempt is evaluated against current state and succeeds.
Two outcomes matter for building on this contract. A retry is an inquiry about a past operation, and a new ID is the only way to start a new operation.
The rules the example depends on
The example is only meaningful inside the benchmark’s full contract. As the author describes it:
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- Creates start at revision 1.
- Replacements and cancellations must name the current revision.
- A rejected replacement leaves the original booking unchanged.
- Proposals, which are checks that might be booked, neither change state nor consume request IDs.
- Confirmed outcomes, including failures, are cached and replayed for the same ID.
- Reusing an ID with a different payload is rejected.
How the benchmark was built
The author reports 8 base traces and 4 dependent metamorphic variants, for 12 test cases in total. The variants rename booking IDs, swap room labels, or shift times. They are transformations of the base traces, so they are not 12 independent observations. Expected answers were enumerated by hand and checked against a Python reference interpreter.
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The figures below come from the author’s own runs. They have not been reproduced independently in the write-up.
| Run | Base traces | Dependent variants | Overall | Output-contract failures | Structured mismatches |
|---|---|---|---|---|---|
| Gemini 2.5 Flash, published rerun | 2/8 | 2/4 | 4/12 | 8 | 0 |
| Gemini 3.7 Flash, as named by the author | 8/8 | 4/4 | 12/12 | 0 | 0 |
| Gemini 2.5 Flash, earlier development evaluation | not stated | not stated | 6/12 | 6 | 0 |
The earlier development evaluation is a separate observation and is not combined with the published rerun. The author attributes the gap between the two Gemini 2.5 Flash runs and the newer model to answer format rather than to reasoning: every answer that reached the structured scorer passed, and the published Gemini 2.5 Flash run produced eight output-contract failures. With 12 cases, these numbers say nothing about which model is generally more capable.
How scoring works and where it can mislead
Exact-trace success after SDK parsing
Scoring checks whether the parsed output matches the expected trace exactly. No LLM judge is involved. The author notes that the scoring does not certify raw JSON strictness, because the Kaggle Benchmarks SDK (version 0.6.1) may normalize output before the scorer sees it. A model that emits loosely formatted JSON can therefore score better than its raw output would justify.
Scoring policy v1 and v2
The reported results use scoring policy v2. An earlier v1 run stopped at the first response that came back as Python where JSON was expected, leaving 11 cases unattempted. Under v2, that specific parsing error is recorded as an output-contract failure and the run continues. API, quota, and unexpected errors still abort the run. Readers comparing runs should check which policy each run used.
What the evidence does not establish
- It does not establish how any production reservation system handles retries. The rule applies only to this benchmark’s declared contract.
- It does not provide industry statistics on reservation replay, idempotency, or booking failure rates.
- It is a 12-case synthetic benchmark, and the variants are derived from the base traces.
- It has not been independently reproduced in the write-up.
If you are designing a similar contract
The benchmark suggests a practical split for retry logic. Keep the request ID stable when retrying the same operation after a timeout, so the caller receives the recorded outcome. Generate a new request ID when the caller wants a fresh decision after conditions have changed. Reject any reuse of an ID with a different payload, so a stored outcome cannot be attached to a different request. Whether your own API works this way is a question for its documentation, not for this benchmark.
Coverage of this specific rule is limited to the benchmark described above, so treat it as a model for contract design rather than a standard.
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