GitSwarm: Cumulative Inference Over Structured Persistent Memory

Long-horizon problem solving and scientific research require computation to accumulate across successive attempts. Partial solutions, experimental findings, and even unsuccessful approaches can inform later work, yet organizing inference-time computation into a reusable body of knowledge remains a challenge. We introduce cumulative inference and instantiate it in GitSwarm, an asynchronous, decentralized system in which homogeneous agents collaborate through structured persistent memory. Agents independently explore, experiment, verify, refine, and synthesize previous work in a shared, branchable Git repository. Atomic commits preserve intermediate artifacts, while explicit provenance records dependencies across branches. We evaluate GitSwarm on long-horizon problem solving and GPU-backed experimental research. On IMOProofBench-Advanced, it solves all 30 problems in one run using GPT-5.5. On ProgramBench, it achieves an 79.4% mean score as compared to 65.1% achieved by the reported baseline. Across three neural architecture research tasks — Residual Matrix Transformer, Looped Transformer, and NanoChat — GitSwarm discovers non-trivial improvements over the respective starting architectures through successive experimentation. Analysis of ProgramBench runs shows that 94.7% of contributions are subsequently built upon, while the selected solution’s ancestry covers 82–93% of the contribution graph. Together, these results demonstrate strong problem-solving performance and extensive accumulation of intermediate work, and extend repository-mediated inference to sustained experimental research.