PRBench Finance

PoTRE: Heterogeneous multi-agent ensemble achieves state-of-the-art 49.92% on Humanity's Last Exam

Researchers introduce PoTRE (Poly-Topological Reasoning Ensembles), a test-time inference framework that decomposes reasoning into four specialized agents—adversarial refinement, hierarchical planning, spectrum search, and direct chain—with a task-adaptive aggregation layer. The system is evaluated on ARC-AGI-2, Humanity's Last Exam (HLE), and PRBench Finance, claiming state-of-the-art 49.92% accuracy on HLE, surpassing the previous best official score. The paper argues that architectural heterogeneity across agents achieves better reasoning performance with similar or fewer inference tokens than scaled homogeneous baselines, making it relevant to inference efficiency debates.