Jr. AI Scientist and Its Risk Report: Autonomous Scientific Exploration
  from a Baseline Paper
Authors: name: Atsuyuki Miyai, name: Mashiro Toyooka, name: Takashi Otonari, name: Zaiying Zhao, name: Kiyoharu Aizawa
Organization: Hal Lab UTokyo

Understanding the current capabilities and risks of AI Scientist systems is
essential for ensuring trustworthy and sustainable AI-driven scientific
progress while preserving the integrity of the academic ecosystem. To this end,
we develop Jr. AI Scientist, a state-of-the-art autonomous AI scientist system
that mimics the core research workflow of a novice student researcher: Given
the baseline paper from the human mentor, it analyzes its limitations,
formulates novel hypotheses for improvement, validates them through rigorous
experimentation, and writes a paper with the results. Unlike previous
approaches that assume full automation or operate on small-scale code, Jr. AI
Scientist follows a well-defined research workflow and leverages modern coding
agents to handle complex, multi-file implementations, leading to scientifically
valuable contributions. For evaluation, we conducted automated assessments
using AI Reviewers, author-led evaluations, and submissions to Agents4Science,
a venue dedicated to AI-driven scientific contributions. The findings
demonstrate that Jr. AI Scientist generates papers receiving higher review
scores than existing fully automated systems. Nevertheless, we identify
important limitations from both the author evaluation and the Agents4Science
reviews, indicating the potential risks of directly applying current AI
Scientist systems and key challenges for future research. Finally, we
comprehensively report various risks identified during development. We hope
these insights will deepen understanding of current progress and risks in AI
Scientist development.