SciConBench：健康领域 AI 智能体科学结论合成能力评测

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Abstract:Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions. Yet, their ability to do so in high-stakes domains such as health remains unclear. We introduce SciConBench, a large-scale live benchmark of 9.21K questions and expert-written conclusions from systematic reviews to evaluate open-domain scientific conclusion synthesis in health and medical domains. The benchmark draws on an expert-validated automated evaluation pipeline that decomposes conclusions into atomic facts and measures correctness and comprehensiveness via factual precision and recall. To mitigate data leakage, we further introduce SciConHarness, a clean-room evaluation harness that equips agents with controlled web interaction to ensure valid measurement. Evaluating 8 frontier models and deep research agents, we find that factual quality remains low: under clean-room settings, the best agent achieves only a factual F1 of 0.337. Our clean-room setting consistently reduces performance relative to unconstrained evaluation, suggesting that leakage inflates estimates of models' true synthesis capabilities. Finally, we audit consumer-facing agents (e.g., Google AI Overview, OpenEvidence) and find they frequently generate incomplete and sometimes contradictory conclusions, even when the ground-truth answer is available. Overall, our results show that reliable synthesis of scientific conclusions in health remains an open challenge, and that clean-room evaluation is essential for assessing open-domain AI agents.

Submission history

From: Hayoung Jung [view email]
[v1] Tue, 9 Jun 2026 18:16:04 UTC (8,391 KB)
[v2] Tue, 10 Nov 2026 04:31:28 UTC (8,814 KB)