MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis

ArXi:2604.11188v1 Announce Type: cross Synthesizing high-quality mathematical reasoning data without human priors remains a significant challenge. Current approaches typically rely on seed data mutation or simple prompt engineering, often suffering from mode collapse and limited logical complexity. This paper proposes a hierarchical synthesis framework that formulates data synthesis as an unsupervised optimization problem over a constraint graph followed by semantic instantiation, rather than treating it as a direct text generation task. We.