U ni M ath- C o T : A Unified Framework for Multimodal Mathematical Reasoning with Re-Inference Affirmation (2025)

Abstract: Large Language Models (LLMs) have achieved considerable success in text-based mathematical reasoning, yet their potential remains underexplored in the multimodal mathematics domain where joint text and image understanding is imperative. A key bottleneck hindering progress is the scarcity of high-quality, genuinely multimodal benchmarks. To address this gap, we construct a unified benchmark by consolidating and curating three public multimodal mathematics datasets. We subsequently propose the UniMath-CoT framework, which establishes a robust performance baseline by combining Chain-of-Thought (CoT) principles with efficient Supervised Fine-Tuning (SFT) based on Low-Rank Adaptation (LoRA). Furthermore, to bolster the model’s reasoning robustness, we introduce an innovative verification mechanism, AARI (Answer Affirmation by Re-Inference), which leverages a specialized re-inference protocol to have the model self-scrutinize and validate its initial conclusions. Our comprehensive experiments show that this integrated strategy substantially boosts performance, surpassing a wide range of open-source models and markedly closing the gap with leading proprietary systems.

## introduction mathematical reasoning is a cornerstone of human intelligence. automating this process, particularly for problems presented in a multimodal format that integrates text with diagrams and figures, represents a significant frontier for artificial intelligence @xcite . this capability has profound implications for domains like personalized education, scientific discovery, and engineering, where complex information is often conveyed visually. the advent of powerful vision-language models (vlms), such as gpt-4v, gemini, and llava @xcite @xcite , has opened new avenues for tackling this challenge. these models can, in principle, process interleaved text and images to produce human-like reasoning steps, moving beyond the text-only limitations of earlier models @xcite . however, the landscape of multimodal mathematical reasoning remains fragmented. numerous datasets exist @xcite @xcite , but they possess distinct formats, scopes, and a significant number of instances where the visual component is not essential for solving the problem. this fragmentation and lack of a unified, high-quality benchmark impede the rigorous evaluation of vlms and hinder progress in the field. to address these challenges and systematically advance the state of multimodal mathematical reasoning, this paper makes three primary contributions: ## methodology our methodology is designed to systematically enhance and evaluate the mathematical reasoning capabilities of vision-language models (vlms). it is founded on three core components: a newly curated benchmark, a fine-tuned reasoning framework, and a novel inference strategy for answer verification as shown in figure 2 . ## conclusion and limitation in this work, we addressed key obstacles in multimodal mathematical reasoning, namely benchmark fragmentation and the need for robust verification. we introduced a tripartite contribution: (1) unimath, a unified and rigorously curated benchmark for genuinely multimodal math problems; our work provides a clear roadmap for building powerful, open-source reasoning systems that rival proprietary models. crucially, aari demonstrates that inference-time self-correction is a highly effective strategy for boosting model factuality and reliability, a principle with strong potential for generalization beyond mathematics to other complex domains. future work can extend this foundation by exploring iterative self-correction mechanisms and expanding the unimath benchmark to new modalities like video-based challenges.