Molecular discovery requires navigating vast combinatorial search spaces while balancing competing biological objectives. AI agents can automate expert-defined molecular design protocols, but automati
MoLRSI is an AI agent that optimizes molecular design workflows and retains cross-task memory to improve search policies, achieving higher experimental hit rates in antibiotic and peptide binder discovery than expert-led methods.
Molecular discovery requires navigating vast combinatorial search spaces while balancing competing biological objectives. AI agents can automate expert-defined molecular design protocols, but automation alone does not address how to optimize workflows for new tasks or improve their development through experience. Here we introduce MoLRSI, an AI agent that combines systematic optimization of workflows and model architectures with persistent cross-task memory to recursively improve its search policy for molecular discovery. Workflow performance, refinement outcomes, and execution feedback guide subsequent searches, allowing the agent to improve both its workflows and its strategy for…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
MoLRSI: A Recursive Self-Improving AI agent for Molecular Discovery was first seen on Hugging Face · Daily papers on Nov 4, 2026. Since then it has appeared in 12 checks on 2 different days. Its best position was #13 of 30, reached on Nov 5, 01:28 UTC. It dropped off the list after Nov 5, 05:28 UTC.
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Not at the moment. It was last on the list on Nov 5, 05:28 UTC, after 2 days of trending.
#13 on Hugging Face · Daily papers, on Nov 5, 01:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 4, 2026 and has been on the list on 2 days (12 checks) since.