Meta paper introduces GitSwarm, where AI agents share work through GitMachine translation

New Meta paper shows that AI agents can build on each other's work, even failed attempts, when every step lives in a shared Git repo. Most ways to give agents more compute treat each run on its own. When a run ends, its partial results and failures vanish, so later runs may need to rediscover them. GitSwarm runs many identical agents on a shared repo, with no boss assigning tasks. Each agent reads past work, picks what to try, and commits its result with a list of earlier commits it used, even from other branches. On 50 program-rebuilding tasks from ProgramBench, GitSwarm scored 79.4%, while a single Codex agent told to keep working peaked at 65.1% at similar compute. Later agents built on 94.7% of saved contributions. Instead of pushing a single agent to keep going, run several over a shared Git history that keeps every attempt, failures included.