Zhang writes about Agora, a system that repurposes Git as shared memory for fleets of autonomous research agents, storing their contributions as an append-only directed acyclic graph where every claim is an immutable commit with parent edges encoding dependencies. In a 12-day run, 13 language-model workers with no assigned tasks or central planner tackled a weight-transfer problem—initializing a frozen 119.6M-parameter attention-SSM hybrid from 141 pretrained donor models without training data or gradient updates—and published 1,703 contributions, closing 62% of the gap to a trained GPT-2 124M (3.39 → 1.899 bits per byte). The winning recipe compresses donor next-token statistics into the target's embedding and output head, then adds short-range context through sparse edits to attention, feed-forward, and state-space blocks; its 145-commit ancestry spans 15 accounts and was independently reproduced 165 times with zero failures.
- A single mid-run human intervention was required to break a monoculture that the diversity-aware selection rule alone could not prevent
- A derived index exposes the frontier, neglected branches, and per-claim verification status
- The target's dimensions match no donor, making direct weight transfer impossible
- The authors acknowledge the experiment does not yet establish whether shared research state improves discovery per unit of compute and outline the controlled comparison that would settle this
- A single mid-run human intervention was required to break a monoculture that the diversity-aware selection rule alone could not prevent
- A derived index exposes the frontier, neglected branches, and per-claim verification status
- The target's dimensions match no donor, making direct weight transfer impossible
- The authors acknowledge the experiment does not yet establish whether shared research state improves discovery per unit of compute and outline the controlled comparison that would settle this
