Research map / Vision-language and multimodal / Human and embodied modeling
Generation of Visual Representations for Multi-Modal Mathematical Knowledge
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Presents MaRE, a web tool that transforms Bayesian-notation probability equations into area diagrams, contingency tables, and probability trees via Representational Systems Theory, with RIST cognitive cost estimates.
Problem
Generating multiple visual representations of mathematical problems (e.g., probability) while guaranteeing correctness and interpretability of transformations between representations; existing LLM and diagram tools do not guarantee semantic correctness.
Approach
Uses a formal framework (Representational Systems Theory) with structure transfer via expert-developed transfer schemas in the Oruga back-end to derive diagrams and tables from a formal Bayes-notation input, so transformations are rigorous, plus a RIST-based cognitive cost table per user profile.
Key results
- MaRE transforms Bayesian-notation probability equations into area diagrams, contingency tables, and probability trees with one click, with RIST cognitive cost tables produced simultaneously.
- The paper reports a demonstration of the tool and does not report quantitative evaluation results.
Limitations
- Currently parses and renders only probability representations, though the framework is claimed to be domain-general.
- No quantitative evaluation of user outcomes or system performance is reported in the paper.
- Relies on expert-developed transfer schemas, which must be authored for each domain.
Experimental setup
Baselines: ChatGPT-4 with Show Me Diagrams plugin Wolfram Mathematica POTATO MM-evocat Setosa conditional probability visualization
Builds on: Representational Systems Theory Representational Interpretive Structure Theory Oruga
Closest papers by meaning
- MV-MATH: Evaluating Multimodal Math Reasoning in Multi-Visual Contexts (CVPR 2025 · similarity 0.85)
- MINT-CoT: Enabling Interleaved Visual Tokens in Mathematical Chain-of-Thought Reasoning (NeurIPS 2025 · similarity 0.85)
- Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency (NeurIPS 2026 · similarity 0.85)
- We-Math 2.0: A Versatile MathBook System for Incentivizing Visual Mathematical Reasoning (ICLR 2026 · similarity 0.84)
- VisioMath: Benchmarking Figure-based Mathematical Reasoning in LMMs (ICLR 2026 · similarity 0.84)
- CogFlow: Bridging Perception and Reasoning through Knowledge Internalization for Visual Mathematical Problem Solving (ICLR 2026 · similarity 0.84)
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