Mathematical information retrieval (MIR) depends on jointly modeling natural-language context and mathematical expressions. While BERT-based dense retrievers are effective, they often dilute mathematical semantics because textual content dominates most training data and mathematical formulas differ fundamentally from natural language in structure and composition. Consequently, these models rely heavily on surrounding text, which reduces robustness in math-intensive scenarios with limited textual description. We propose MaRF, a dual-encoder representation-level fusion framework for MIR that explicitly integrates formula semantics into context-aware dense retrieval. By combining contextual and formula-specific representations, MaRF captures complementary information from both textual and symbolic views. Experiments on the ARQMath-3 benchmark demonstrate that MaRF substantially improves retrieval performance and robustness, outperforming strong baselines across MIR tasks. The source code and datasets are available at https://github.com/MLPgroup/MaRF.

Vector representations have been pivotal in advancing natural language processing (NLP), with prior research focusing on embedding techniques for mathematical expressions using mathematically equivalent formulations. While effective, these approaches are constrained by the size and diversity of training data. In this work, we address these limitations by introducing E-Gen, a novel e-graph-based dataset generation scheme that synthesizes large and diverse mathematical expression datasets, surpassing prior methods in size and operator variety. Leveraging this dataset, we train embedding models using two strategies: (1) generating mathematically equivalent expressions, and (2) contrastive learning to explicitly group equivalent expressions. We evaluate these embeddings on both in-distribution and out-of-distribution mathematical language processing tasks, comparing them against prior methods. Finally, we demonstrate that our embedding-based approach outperforms state-of-the-art large language models (LLMs) on several tasks, underscoring the necessity of optimizing embedding methods for the mathematical data modality. The source code and datasets are available at https://github.com/MLPgroup/E-Gen.