SOURCE-LINKED INTELLIGENCE
Learning to Evaluate Before Improving: Automatic Rubric Induction for Automatic Research Agents
Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experimentation, and report generation. However, open-ended research tasks often do not clearly specify the analyses, methods, and success criteria required to complete the task. As a result, agents may miss important analyses, use inappropriate methods, or draw conclusions that are insufficiently supported by evidence. To address the problem, we present AutoSciRub, an evaluation-first framework that induces a task-specific executable rubric before rese
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- arXiv · AI, language, vision and robotics · 2026-08-31T16:48:51.000Z
First collected: 2026-09-21T06:41:57.136Z. This is not the publication date.