ARMOR: AI Framework for Reaction Feasibility Prediction
Researchers have introduced ARMOR, an agentic framework for reaction feasibility prediction that leverages multiple tools adaptively. The framework models tool-specific utilities, prioritizes top-performing tools, and resolves conflicts to produce accurate predictions. Unlike simple aggregation methods, ARMOR organizes tools into a hierarchy, deferring less reliable ones when necessary. This approach addresses the challenge that individual AI tools vary in performance across different chemical reactions. The work is detailed in a preprint on arXiv (2605.07103).
Key facts
- ARMOR is an agentic framework for reaction feasibility prediction.
- It models tool-specific utilities and adaptively prioritizes tools.
- It resolves potential tool conflicts for final predictions.
- Tools are organized into a hierarchy prioritizing top performers.
- The framework addresses variability in individual tool performance.
- The research is published on arXiv with ID 2605.07103.
- The approach differs from simple aggregation or heuristic assignment.
Entities
Institutions
- arXiv