A math repo with hundreds of open pull requests publishes the operating rules it gives donated agents

The repository collects community attempts to improve a conditional bound for integer multiplication, building on an OpenAI manuscript. Its README says Douglas Colkitt maintains the project with OpenAI Codex assistance, and it invites donated agent sessions: anyone "asking an agent to contribute" is pointed to autoresearch.md. At the builder's October 13 read, the page listed 721 open pull requests; that live count changes rapidly. The current reviewed result is a conditional exponent saving of about 0.00167266, submitted as Rohan Arun's pull request #1094. The README calls that an exponent saving, "not measured runtime."

The protocol is the part worth reading. A donated session defaults to about 60 minutes. A script draws a random task from weighted tracks (bit mechanisms and complex mechanisms 20% each, adversarial audit 15%, independent verification 10%, and others), and a second script searches open PRs, discussions and notes for overlap. Agents post a claim on a discussion board with an expiry of roughly two hours; claims are advisory, not locks. The file says plainly that "Random choice is not an atomic work allocator: hundreds of agents still need the live discussion and PR overlap checks." Merging, closing other people's work and changing the selected result stay with the maintainer, and finishing a session with a finding or handoff and no PR is allowed. Each reviewed result ships with a make target that re-runs its checks. Rohan Arun, a credited contributor, posted on X that anyone could contribute by "copying this prompt into Codex/Claude."

Several limits apply. The README states that the work "is not full formal verification, independent human peer review, a worldwide priority claim, or a practical multiplication benchmark," and the bounds depend on an assumed framework. The README does not say what share of the pull requests were written by agents, and this edition did not check the contributor notices. A tracker page hosted by Super Powers AI reports 234 contributors; we did not verify that count.

Why it matters: If you plan to point many unsupervised agent sessions at one repository, this is a working example of the rules that become necessary: a task allocator, an overlap check before work starts, short expiring claims, a record of failed experiments, and a maintainer who alone decides what counts as accepted. The repository does not show that the protocol works well, only that its maintainers consider it necessary at this volume.