OpenAI on July 8 published a rare "self-audit": using a Codex-driven investigator agent plus 5 senior engineers double-blind reviewed the 731 public problems of SWE-Bench Pro. The automated pipeline flagged 200 "bad problems" (27.4%); human review pushed it to 249 (34.1%); combined estimate, about 30% of problems cannot reliably reflect model capability. The issues are classified into four categories: hidden tests being too strict, prompt description being insufficient, test coverage being low, problem description contradicting test logic — all in the data layer, nothing to do with the model itself. OpenAI explicitly retracted its year-old position of "recommending the community to switch from SWE-Bench Verified to SWE-Bench Pro" — the second self-retraction of an evaluation recommendation in 12 months. More alarming is the "numbers exploding": in the eight months before the re-audit, the frontier model's pass@1 went from 23.3% to 80.3% — in this near-triple jump, a sizable portion probably has to be chalked up to "models getting better at reading the test-setter's default implementation details" rather than real engineering capability. After removing the 30% bad problems, the gap between #1 and #5 is probably far less exaggerated than the hype, and the "just beat Claude again" rhetoric the vendors love also needs to be discounted. The more valuable methodological piece is OpenAI's "agent + human" dual-track pipeline: Codex can already run tests, check code, and catch failure modes like an auditor, scaling up expensive manual QA. This heralds a new form of LLM evaluation — letting LLMs first question the benchmark itself, then humans nitpick, in a cycle of improvement. The most realistic reminder for vendors and developers: when Coding Agents post impressive scores on SWE-Bench Pro, first ask "is it just blindly following the implementation details hinted at in the prompt"; for selection, don't just stare at one or two percentage points on the leaderboard, the fix rate on real repository tasks is more reliable.