[2603.03655] Mozi: Governed Autonomy for Drug Discovery LLM Agents

Computer Science > Artificial Intelligence arXiv:2603.03655 (cs) [Submitted on 4 Mar 2026] Title:Mozi: Governed Autonomy for Drug Discovery LLM Agents Authors:He Cao, Siyu Liu, Fan Zhang, Zijing Liu, Hao Li, Bin Feng, Shengyuan Bai, Leqing Chen, Kai Xie, Yu Li View a PDF of the paper titled Mozi: Governed Autonomy for Drug Discovery LLM Agents, by He Cao and 9 other authors View PDF HTML (experimental) Abstract:Tool-augmented large language model (LLM) agents promise to unify scientific reasoning with computation, yet their deployment in high-stakes domains like drug discovery is bottlenecked by two critical barriers: unconstrained tool-use governance and poor long-horizon reliability. In dependency-heavy pharmaceutical pipelines, autonomous agents often drift into irreproducible trajectories, where early-stage hallucinations multiplicatively compound into downstream failures. To overcome this, we present Mozi, a dual-layer architecture that bridges the flexibility of generative AI with the deterministic rigor of computational biology. Layer A (Control Plane) establishes a governed supervisor--worker hierarchy that enforces role-based tool isolation, limits execution to constrained action spaces, and drives reflection-based replanning. Layer B (Workflow Plane) operationalizes canonical drug discovery stages -- from Target Identification to Lead Optimization -- as stateful, composable skill graphs. This layer integrates strict data contracts and strategic human-in-the-loop ...