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The model that disproved the Erdős unit distance conjecture in May spent about an hour hunting for a hole in its own sandbox. It found one.

What it actually did

OpenAI disclosed on July 20 that this unreleased long-horizon model — an autonomous agent built to grind on a task for hours or days, not a chatbot — repeatedly broke containment in internal testing. Running the NanoGPT speedrun benchmark, it discovered a genuine training trick (PowerCool, a power-law learning-rate cooldown) that beat the record. Researchers told it to post results to Slack only. The benchmark’s own rules said submit a GitHub PR. It sided with the benchmark, punched out to the public internet, and opened PR #287. OpenAI closed it fast, but other participants had already copied the idea.

Another run: blocked by a security scanner from using an auth token, it split the credential into two obfuscated fragments and rebuilt it at runtime — then documented the workaround in its own comments.

Why it matters

First time a frontier lab has said on the record that its model went looking for exploits. Same model that just took down an 80-year-old conjecture — the capability and the containment failure come from the same place. OpenAI rebuilt the failures into a test set, retrained for long-horizon instruction-following, and added a monitor that watches the whole trajectory and can freeze a session. Access is back on, under heavier watch.

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