ARMOR: An Agentic Framework for Reaction Feasibility Prediction via Adaptive Utility-aware Multi-tool Reasoning

Researchers introduce ARMOR, an agentic framework that improves chemical reaction feasibility prediction by intelligently combining multiple AI tools rather than relying on single models. The system uses hierarchical tool organization and memory-augmented reasoning to resolve conflicting predictions, demonstrating significant performance gains especially when different tools disagree on outcomes.

ARMOR addresses a fundamental limitation in computational chemistry: no single AI tool consistently predicts reaction feasibility across all scenarios. The framework treats tool selection as an adaptive problem rather than a static one, organizing prediction tools into a hierarchy that prioritizes high-performing options while maintaining fallback mechanisms. This mirrors broader challenges in AI systems where ensemble approaches often outperform individual models, particularly when constituent tools have complementary strengths.

The research builds on accelerating adoption of large language models and specialized AI tools in chemistry research. As computational chemistry increasingly relies on diverse AI methodologies—from graph neural networks to transformer-based models—the ability to coordinate these tools effectively becomes critical. ARMOR's memory-augmented reasoning component specifically targets conflict resolution, suggesting the framework learns which tool combinations work best for particular reaction classes.

For the scientific computing and chemistry AI sectors, ARMOR has meaningful implications. Pharmaceutical research, materials science, and chemical manufacturing depend heavily on feasibility prediction accuracy. Improved prediction tools reduce experimental iteration cycles and accelerate discovery pipelines. The framework's success on conflicting-prediction cases indicates practical utility where uncertainty is highest and consensus is most valuable.

Future developments likely involve scaling ARMOR to proprietary chemical databases and integrating it with laboratory automation systems. The research also raises questions about optimal tool hierarchies for domain-specific problems, potentially inspiring similar frameworks in other scientific domains where multiple specialized models exist.

• →ARMOR combines multiple AI tools hierarchically to improve chemical reaction feasibility prediction beyond any single tool's capabilities.
• →The framework excels specifically at resolving conflicts when different prediction tools disagree on reaction outcomes.
• →Memory-augmented reasoning enables adaptive tool prioritization rather than fixed weighting schemes.
• →Performance gains are most pronounced on reactions where existing tools produce contradictory predictions.
• →The approach generalizes principles applicable to other scientific domains requiring multi-tool model coordination.