GA-VisAgent: A Multi-Agent application for code generation and visualization in interactive learning

arXiv cs.LG / 5/5/2026

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Key Points

The paper introduces GA-VisAgent, a multi-agent interactive learning application that generates Geometric Algebra (GA) code and visualizes results to make GA concepts easier to understand.
GA-VisAgent builds on a Geometric Algebra LLM (GAGPT) and combines task planning with ReAct reasoning to split complex GA work into standardized subtasks such as geometric products, rotations, and reflections.
Users can provide inputs as either natural language or mathematical formulas, and the system generates executable code along with interactive visualizations to improve comprehension.
Experiments on 40 conformal GA tasks show a 90% code-generation success rate, a reported 70% improvement over GPT-4o, addressing prior accuracy issues from LLM-generated GA scripts.
The authors position GA-VisAgent as an extensible paradigm for teaching GA and building visualization tools, with an online service planned at the project URL.

Abstract

Geometric Algebra (GA) presents challenges to learners due to its highly abstract mathematical structure and complex operational rules, as translating algebraic manipulations into concrete geometric interpretations is a non-intuitive process when developing related code. Currently, some existing GA software packages rely on manually written scripts for code generation and visualization, but their high learning curve hinders widespread adoption. Meanwhile, methods based on Large Language Models (LLMs) often produce logical errors when generating specific GA scripts, such as GAALOPScript, resulting in generally low accuracy. To address these issues, this study proposes GA-VisAgent -- a multi-agent interactive learning application for GA code generation and visualization -- building upon a Geometric algebra large language model (GAGPT). Integrating task planning mechanisms with ReAct reasoning strategies, GA-VisAgent can decompose complex operations into five standardized subtasks, including core operations like geometric products, rotations, and reflections. It supports natural language and mathematical formulas as input to automatically generate executable code, accompanied by interactive visualizations to aid user comprehension. Experimental results show that GA-VisAgent achieved a 90% code generation success rate across 40 typical Conformal GA tasks, representing a 70% improvement over GPT-4o. This application introduces an extensible new paradigm for teaching GA and developing visualization tools for related mathematical concepts. The online service for this project will be available at http://gagis.cn/gacrac.