MAVEN shows tool-calling agents still struggle to generalize

MAVEN is a useful research update because agent reliability increasingly depends on tool orchestration, not just base-model benchmark scores. The arXiv paper presents Modular Agentic Verification and Execution Network, a lightweight symbolic scaffold for decomposition, adaptive tool orchestration, and intermediate verification. The authors evaluate across BFCL v3, TauBench, Tau2Bench, AceBench, and a new MAVEN-Bench stress test for multi-step mathematical and physical reasoning. Their headline result is that MAVEN improves a GPT-OSS-120b base model from 48% to 71% accuracy on direct MAVEN-Bench runs without additional training, while remaining competitive with proprietary frontier baselines at an estimated one-tenth cost. Confidence should stay medium until independent reproduction, but the direction is important: process scaffolds and verification loops may matter as much as raw model choice for real agents.

Key details: MAVEN, arXiv:2605.30738, May 29, 2026, Modular Agentic Verification and Execution Network, MAVEN-Bench, GPT-OSS-120b, 48% to 71% accuracy, BFCL v3.

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