AI RESEARCH
Reasoning over mathematical objects: on-policy reward modeling and test time aggregation
arXiv CS.AI
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ArXi:2603.18886v1 Announce Type: new The ability to precisely derive mathematical objects is a core requirement for downstream STEM applications, including mathematics, physics, and chemistry, where reasoning must culminate in formally structured expressions. Yet, current LM evaluations of mathematical and scientific reasoning rely heavily on simplified answer formats such as numerical values or multiple choice options due to the convenience of automated assessment. In this paper we provide three contributions for improving reasoning over mathematical objects: (i) we build and release.