MAVEN: Improving Generalization in Agentic Tool Calling
Bridging the gap between individual tool usage and robust multi-step reasoning with modular symbolic verification.
Generalization across agentic tool-calling environments remains a central challenge for reliable agentic reasoning systems. Although large language models achieve strong results on individual benchmarks, their ability to compose reasoning strategies, preserve intermediate states,...
ArXiv CS.AI
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MAVEN demonstrates that integrating symbolic verification scaffolds can significantly boost the compositional reasoning capabilities of existing LLMs without requiring expensive model retraining. This shift toward process-aware architectures suggests a path to building reliable, cost-effective agents that excel at multi-step tasks in complex environments.
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