Letting AI Formulate the Research Question: Autonomous Research Agents

Key points

The system gives an off-the-shelf coding agent an instruction document and two skills for question formulation and peer review. All five consecutive runs completed everything from question formulation to a revised, re-computable paper with local computation on a laptop and no research input from humans.
Over 13 development runs, changing the one or two sentences that set the research area coincided with large shifts in candidate topics. Adding a single sentence hinting at societal problems moved 14 of the same model's 15 candidates to societal problems.
In the evaluation runs the agents wrote 77 candidate questions across nine societal fields, all concerning society or the environment. Most candidates were discarded because public ground truth was missing or technology access was gated.
The critic changed how the chosen question was compared and measured in every run, and the reviewer re-ran every first draft from its verification entry point and reproduced the reported numbers. All 40 review findings were adopted or partly adopted.
In the external AI evaluation, Gemini 4 Argon judged all five papers workshop level with a mean overall score of 5.4, while Muse Wonder was stricter with a mean of 4.1. Both reviewers rated novelty and generality low.
As limitations, five runs with one research model are a small sample with no ablation, all evaluations rely on AI model judgments, and a human evaluation is planned separately.

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