Toward Generalist Autonomous Research via Hypothesis-Tree Refinement
Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction. Researchers test candidate directions, interpret the evidence, and carry the resulting lessons into later attempts. We study how an AI agent can run this loop autonomously over long horizons. We introduce Arbor, a general framework for autonomous research that combines a long-lived coordinator, short-lived executors, and Hypothesis Tree Refinement (HTR), a persistent tree that links hypotheses, artifacts, evidence, and distilled insights across time. The coordinator manages global research strategy over the tree, while executors implement and test individual hypotheses in isolated worktrees. A...
| Authors | Jiajie Jin, Yuyang Hu, Kai Qiu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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