MAVEN: Improving Generalization in Agentic Tool Calling
MAVEN (Modular Agentic Verification and Execution Network) is a lightweight symbolic reasoning scaffold designed to enhance generalization in tool-calling environments through structured decomposition, adaptive tool orchestration, and intermediate verification. On the MAVEN-Bench stress test, MAVEN improves the GPT-OSS-120b base model from 48% to 71% accuracy without additional training, using an open-weight backbone at roughly 1/10 the cost of proprietary baselines.
[2605.30738] MAVEN: Improving Generalization in Agentic Tool Calling
[Submitted on 29 May 2026]
Title:MAVEN: Improving Generalization in Agentic Tool Calling
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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 evaluate MAVEN across established tool-calling benchmarks, including BFCL v3, TauBench, Tau2Bench, AceBench, and introduce MAVEN-Bench, a stress-test benchmark for multi-step mathematical and physical reasoning with explicit verification and adversarial task composition. MAVEN-Bench exposes a substantial gap between partial reasoning quality and end-to-end task success; in direct MAVEN-Bench runs, MAVEN improves its GPT-OSS-120b base model from 48% to 71% accuracy without additional training. It also remains competitive with frontier proprietary baselines while using an open-weight backbone with an estimated cost ratio of roughly 1/10, suggesting that lightweight verification-centered scaffolds can strengthen compositional reasoning and motivate more process-aware evaluation of agents in the wild.
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Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.30738 [cs.AI]
(or arXiv:2605.30738v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.30738
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Vishvesh Bhat [view email] [v1] Fri, 29 May 2026 02:02:31 UTC (319 KB)
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