An AI agent for treatment reasoning over a biomedical tool universe
Researchers introduce ATHENA-R1, an AI agent trained via reinforcement learning to perform treatment reasoning across 212 biomedical tools. It outperforms GPT-5 on benchmarks and is preferred by experts and physicians.
[2606.28692] An AI agent for treatment reasoning over a biomedical tool universe
[Submitted on 27 Jun 2026]
Title:An AI agent for treatment reasoning over a biomedical tool universe
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Abstract:Treatment reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biomedical knowledge to select an appropriate therapy. It is inherently iterative: candidates are weighed against many constraints, revised as evidence emerges, and grounded in verifiable sources. Here we introduce ATHENA-R1, an AI agent for treatment reasoning across all FDA approved drugs since 1939, trained by reinforcement learning over a universe of 212 biomedical tools. At each step it identifies missing information, selects and runs relevant tools, and incorporates the evidence. To train it without human-annotated traces, we build a two-level self-learning framework: multi-agent systems construct the tools, tasks, and reasoning trajectories for supervised fine-tuning, then reinforcement learning with scientific feedback rewards reasoning quality (evidence gathering, grounded tool use, logical non-redundancy). Across five benchmarks of 3,168 drug reasoning tasks and 456 patient treatment cases, ATHENA-R1 outperforms language models and tool-use systems, reaching 94.7% accuracy on open-ended drug reasoning and 82.9% on treatment reasoning, 17.8 and 10.7 points above GPT-5. In blinded evaluations by experts from 28 rare disease organizations, it is preferred over reference models on all criteria, and physicians rated it favorably on complex hospitalized cardiovascular and infectious-disease cases. Adverse-event hypotheses it generated, tested in electronic health records from 5.4 million patients, reached adjusted odds ratios of 1.48-1.84, with no elevation among negative controls. Because it requires knowing what evidence to seek before concluding, treatment reasoning has long been hard for AI; we show it can be reframed as a learnable process of iterative evidence gathering that reinforcement learning can train AI to perform.
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Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.28692 [cs.AI]
(or arXiv:2606.28692v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.28692
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From: Shanghua Gao [view email] [v1] Sat, 27 Jun 2026 02:24:56 UTC (8,433 KB)
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