Agora is a system that lets AI agents collaborate on research by storing all work as immutable Git commits in a directed acyclic graph, with a scoring system that rewards verified results. Researchers tested it with 13 language-model agents working to initialize a neural network without training, and they achieved 62% of the performance gap to a trained baseline in 12 days.

• Thirteen agents working independently with no central planner published 1,703 contributions over 12 days, with the best result closing 62% of the gap between random initialization (3.39 bpb) and a trained GPT-2 baseline (1.0 bpb).
• The winning approach used bigram statistics extracted from six donor models' predictions, factorized by SVD, plus sparse deterministic edits to sublayers—no gradient updates or training data involved.
• Early gains came fast: the first eight improvements accounted for roughly 70% of total progress, but agents converged into a monoculture around one recipe until shown a visualization of their own concentration.