Agora: Git as Shared Memory for Collective AutoResearch
Agora records multi-agent research as an append-only Git DAG; 13 LLM workers ran nearly 12 days on a weight-transfer problem.
Agora stores every result, hypothesis, and verification as an immutable commit in a Git-stored DAG, with a derived index exposing the frontier and verification status of claims. In a nearly 12-day run, 13 language-model workers with no assigned tasks or central planner published 1,703 contributions on initializing a frozen 119.6M-parameter attention-SSM hybrid from 141 donor models. They improved the evaluator from 3.39 to 1.899 bits per byte, closing 62% of the gap to a trained GPT-2 124M, with 165 independent reproductions posted and none failing.
- Research recorded as immutable commits in an append-only Git DAG
- 13 LLM workers operated with no central planner for nearly 12 days
- 1,703 contributions; evaluator improved from 3.39 to 1.899 bits per byte
- 165 independent reproductions posted, none failed
- Diversity-aware selection rule prevents collapse onto a single leader
From the original article
1 min read at huggingface.co
Autonomous research loops such as AutoResearch show that one coding agent can improve a training setup unattended. Run several of them and each session starts from scratch, so more agents tend to mean more duplicated search rather than more discovery.
By Yifan Zhang, Yunheng Zou, Shaokun Zhang · huggingface.co
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