ARMOR improves reaction feasibility prediction by adaptively prioritizing tools based on learned utilities and resolving conflicts with memory-augmented reasoning, outperforming single-tool and simple aggregation baselines especially on conflicting cases.
InProceedings of the 2022 conference of the North American chapter of the association for computational linguistics: human language technologies
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ARMOR: An Agentic Framework for Reaction Feasibility Prediction via Adaptive Utility-aware Multi-tool Reasoning
ARMOR improves reaction feasibility prediction by adaptively prioritizing tools based on learned utilities and resolving conflicts with memory-augmented reasoning, outperforming single-tool and simple aggregation baselines especially on conflicting cases.