Can AI Agents Synthesize Scientific Conclusions?

The paper introduces SciConBench, a benchmark consisting of 9.11K questions and expert-written conclusions designed to assess the synthesis capabilities of AI agents in scientific domains. It employs an automated evaluation pipeline that measures factual precision and recall while addressing data leakage through the SciConHarness clean-room evaluation framework. Results indicate that the best-performing agent achieves a factual F1 score of only 0.337, highlighting significant challenges in reliable scientific conclusion synthesis and the necessity for rigorous evaluation methodologies in AI applications.