The authors built ARMOR, a system that decides whether a proposed chemical reaction will actually work by intelligently combining predictions from multiple AI tools. Instead of just averaging tool outputs or always trusting one tool, ARMOR learns which tools are reliable for which kinds of reactions, prioritizes the best tools, and uses memory-based reasoning to resolve cases where tools disagree. On a public chemistry dataset, this adaptive combination beats both single-tool approaches and simpler ways of aggregating multiple tools, with the biggest gains on reactions where tools give conflicting predictions.
Main takeaways:
- Reaction feasibility prediction tools (AI models for chemistry) vary widely in performance across different reactions, so no single tool is always best
- ARMOR organizes tools into a hierarchy that prioritizes top performers and defers to others when needed, rather than treating all tools equally
- Learns tool-specific patterns (when each tool is reliable) and uses memory-augmented reasoning to resolve conflicts
- Outperforms single-tool methods and simpler aggregation approaches, especially on reactions where tools disagree
- Code is available for replication