Presents ATHENA, an attention-based architecture that expands candidate mathematical thoughts step by step, improving math-word-problem solving under limited or varied training signals.
Overview
Math word problems reward careful intermediate reasoning, but models often collapse too early onto a single path. ATHENA is an attention-based architecture that expands candidate mathematical thoughts step by step.
By growing and comparing intermediate thoughts, ATHENA improves math-word-problem solving when training signals are limited or varied.
Why it matters
Better intermediate search can help models stay flexible before committing to an answer—especially in domains where a small early mistake cascades into a wrong final result.