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Mozi: Governed Autonomy for Drug Discovery LLM Agents
arXiv – CS AI|He Cao, Siyu Liu, Fan Zhang, Zijing Liu, Hao Li, Bin Feng, Shengyuan Bai, Leqing Chen, Kai Xie, Yu Li|
🤖AI Summary
Researchers have introduced Mozi, a dual-layer architecture designed to make AI agents more reliable for drug discovery by implementing governance controls and structured workflows. The system addresses critical issues of unconstrained tool use and poor long-term reliability that have limited LLM deployment in pharmaceutical research.
Key Takeaways
- →Mozi introduces a dual-layer architecture combining flexible AI reasoning with deterministic computational biology controls.
- →The system addresses key barriers of unconstrained tool governance and poor long-horizon reliability in drug discovery AI agents.
- →Layer A establishes supervised hierarchies with role-based tool isolation and constrained action spaces.
- →Layer B operationalizes drug discovery stages as stateful, composable skill graphs with human-in-the-loop checkpoints.
- →Testing on PharmaBench showed superior orchestration accuracy compared to existing baselines.
#ai-agents#drug-discovery#llm#pharmaceutical#research#governance#biotech#machine-learning#scientific-computing
Read Original →via arXiv – CS AI
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