Meta Superintelligence Labs just turned a Git repo into shared memory for a swarm of agents. GitSwarm: Decentralized Compounding Inference (Meta Superintelligence Labs, with Mila, CMU and Princeton) makes inference compound instead of restarting from scratch on every run. → Homogeneous async agents work with no central planner, each deciding whether to explore, verify, refine or combine prior work → Every contribution is an atomic commit in a shared, branchable Git repo, with semantic dependencies recording what it builds on → ProgramBench: 79.4% mean score vs 65.1% for the strongest baseline, scaling from 73.1% with 100 workers to 79.4% with 500 → IMOProofBench-Advanced: all 30 problems solved in one run with GPT-5.5, using under one million output tokens → Reuse is real: 94.7% of ProgramBench contributions were later built upon, and the winning solution's ancestry covers 82 to 93% of the graph → On 3 neural architecture tasks, workers ran 395 experiments in the first 26 hours and improved on the starting architectures Takeaway for agent builders: if intermediate work disappears when an episode ends, you are paying to rediscover it. Persist it, with its dependencies, and let later agents compound it. Paper: Worth trying this pattern with your own agents in @muse