Toward Generalist Autonomous Research via Hypothesis-Tree Refinement

Key points

Autonomous Optimization, or AO, is defined as a task in which an initial output is improved through repeated experiments without step-by-step human supervision.
The hypothesis tree implements cumulative research state by binding each node to a hypothesis, a version of the output, experimental evidence, and refined insight.
It separates the coordinator, which handles the global strategy, from the executor, which runs individual experiments in isolated worktrees.
Across six real research tasks, including model training, harness work, and data synthesis, it reports more than 2.5x average relative gains over Codex and Claude Code.
On MLE-Bench Lite, it achieves 86.36 percent Any Medal with GPT-5.5, and ablations confirm that HTR and insight propagation are key.
It demonstrates generalization by transferring the BrowseComp optimization harness to a previously unseen search-agent task.

External research summaries. These are not HDATF publications or measured product results.