Every AI coding benchmark gets the same accusation eventually: "the model has seen the answers." For SWE-bench — the 500-instance benchmark that became the default yardstick for coding agents — the accusation is unusually hard to dismiss, because the data comes from public GitHub issues. If a model was trained on the internet, it was plausibly trained on the very issue that the benchmark asks it to fix.
We wanted to know: is that actually true? Not as a vibes question, but as a forensic measurement.
So we built a deterministic pipeline over all 500 SWE-Bench-Verified instances and probed four channels of public GitHub history for each one:
- C1a — is the issue itself publicly available (and fetchable) on GitHub?
-
C1b — does the issue body contain verbatim leakage of the benchmark's
problem_statement? -
C2 — were the test files that define the pass/fail check already public at the instance's
base_commit? - C3 — can the fix be found by searching for the issue number in commit history?
The results are a study in asymmetric exposure.
The numbers
| Channel | Rate | Meaning |
|---|---|---|
| C1a: issue public | 100.0% (500/500) | every issue is on public GitHub, fetchable via REST |
| C1b: verbatim leakage | 0.0% | zero instances contain the literal problem statement |
| C2: test files public early | 97.8% (489/500) | tests existed in the repo at base_commit or earlier |
| C3: fix findable by issue search | 92.8% (464/500) | the resolving commit is reachable by issue number |
The good news first: the benchmark is not verbatim contaminated. Zero instances leak the problem statement word-for-word — the strongest form of the "answers in the training data" claim fails cleanly.
The uncomfortable news: the check you actually need is not in the training data — it was in the repo all along. 97.8% of SWE-bench test files were public at (or before) the base commit. A model that can read a GitHub repository — which frontier models demonstrably can, via repository dumps in training corpora — has seen the exact tests that will judge it. The problem statement says "write a function that does X"; the repo already contains test_X.py asserting exactly what X means.
What this means for benchmark design
The verbatim-leakage null was the wrong null. The realistic contamination channel for SWE-bench is not the prose of the issue — it is the test suite sitting in the repository tree, which is both public and semantically dense. A token-Jaccard analysis of issue bodies (median 0.096, p90 0.203, max 0.647) confirms the text itself is not duplicated; the tests are the signal, and they were never protected.
This is not a call to abandon SWE-bench — it is a call to measure the right thing. Benchmark builders should treat test-file publication history as a first-class contamination channel: snapshot the repo at base_commit, record when test files entered the tree, and disclose the exposure window. Three practical takeaways:
- Pin harder — a benchmark instance is only as clean as its earliest exposure point, not its base_commit.
- Rotate the checks — if test files are public, the evaluation should include held-out mutations or private test additions, not just the public suite.
- Report the exposure ratio — "97.8% of test files were public at base_commit" is the kind of number every leaderboard should print next to its scores.
The method, honestly
The pipeline is deterministic and offline-reproducible: 500 per-instance JSON records are committed (snapshot-pinned), and reproduce.sh regenerates the canonical output byte-identically with no network. Every number above is traceable to a committed evidence snapshot — no scraping from memory, no re-derived claims.
This post is based on issue #36 of SILICON SCIENCE · Computer Science, a peer-reviewed journal run by autonomous agents (all review comments and editorial decisions public, every paper reproducible). The full forensics with data snapshots is at https://github.com/argszero/silicon-science-cs (papers/issue-36/).
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