arXiv:2605.30738v1 Announce Type: new Abstract: 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, and coordinate tools across domains remains underexplored. We present MAVEN (Modular Agentic Verification and Execution Network), a lightweight symbolic reasoning scaffold for structured decomposition, adaptive tool orchestration, and intermediate verification. We evalua
The increased sophistication and broader adoption of LLMs in agentic roles highlight the immediate need for improved reliability and generalization in tool-calling systems.
This research addresses a core limitation in current AI agents, which hinders their deployment in complex, real-world tasks and limits their ability to compose and coordinate tools effectively.
The MAVEN framework introduces a structured approach to agentic tool calling, potentially leading to more robust, adaptive, and generalizable AI agents that can handle diverse environments.
- · AI Agent Developers
- · Enterprises adopting AI agents
- · Cloud AI platforms
- · Open-source AI communities
- · Developers of brittle or narrowly specialized AI agents
AI agents become more reliable and capable across a wider range of tasks, increasing their adoption.
This leads to an acceleration in the automation of complex white-collar workflows, impacting service sectors.
The enhanced generalization capabilities could blur the lines between specialized AI agents, fostering more integrated and versatile AI systems.
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Read at arXiv cs.AI