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

2026-04-29 · Source: cs.AI updates on arXiv.org · Field: Science & Research — Physical Sciences & Chemistry, Mathematics & Computational Sciences, Research Methodology & Innovation · Depth: Expert, extended

Summary

The $\mathop{\textsc{ARMOR}}\_$ framework is an agentic system designed to improve reaction feasibility prediction in computational chemistry by adaptively leveraging multiple AI tools. It addresses the challenge that individual tools exhibit varying performance across different chemical reactions. $\mathop{\textsc{ARMOR}}\_$ achieves this through three core components: a tool hierarchy that categorizes tools by overall performance, a utility-aware prioritization module that selects tools based on reaction-specific patterns, and a memory-augmented conflict resolution mechanism that reconciles conflicting predictions. Evaluated on the FREA dataset, $\mathop{\textsc{ARMOR}}\_$ consistently outperforms single-tool methods and various aggregation/selection baselines, achieving 91.62% overall accuracy, a significant improvement over the strongest individual tool's 87.90%. Its gains are particularly notable in complex cases with conflicting tool predictions.

Key takeaway

For AI Scientists and Machine Learning Engineers developing chemical prediction systems, consider implementing agentic frameworks like $\mathop{\textsc{ARMOR}}\_$ to overcome the limitations of single-tool approaches. Your systems will benefit from explicitly modeling tool-specific utilities and resolving prediction conflicts, especially for challenging reactions. This approach can lead to more accurate and balanced predictions, reducing the risk of inefficient resource use or incorrect experimental directions in chemical synthesis.

Key insights

Adaptive multi-tool reasoning with explicit utility modeling and conflict resolution significantly enhances reaction feasibility prediction.

Principles

• No single tool excels across all reaction types.
• Explicitly modeling tool utility improves prediction accuracy.
• Hierarchical tool organization balances robustness and specialization.

Method

$\mathop{\textsc{ARMOR}}\_$ constructs a tool hierarchy, extracts and refines tool-specific utility patterns, and resolves prediction conflicts using memory-augmented reasoning over contrastive demonstrations to select the optimal tool.

In practice

• Organize tools into performance-based hierarchies.
• Characterize tool strengths with descriptive patterns.
• Use historical conflict examples for robust decision-making.

Topics

• Reaction Feasibility Prediction
• Agentic Framework
• Multi-tool Reasoning
• Tool Utility Modeling
• Tool Conflict Resolution

Code references

• meta-llama/llama3
• OSU-NLP-Group/FREA
• tyo-nu/Dora_xgb
• MolecularAI/Chemformer
• pschwllr/MolecularTransformer

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.