An Evaluation-Centric Paradigm for Scientific Visualization Agents

2025-09-18

AI Summary

A large-scale, standardized evaluation benchmark for scientific visualization (SciVis) agents is currently lacking, hindering quantitative capability assessment and technological advancement in the era of multimodal large language models (MLLMs). Method: This work adopts an evaluation-centric research paradigm, systematically analyzing the evaluation requirements and challenges for SciVis agents, and introduces the first task-oriented, scalable, and comprehensive evaluation framework. The framework integrates multimodal large language models, automated visualization generation, and fine-grained capability decomposition to enable unified assessment of core competencies—including visual understanding, reasoning, generation, and interactive capabilities. Contribution/Results: We propose a “centralized evaluation” paradigm and demonstrate its feasibility via proof-of-concept implementation. This lays the foundation for an open, reproducible, community-driven benchmark for SciVis agents, fostering collaborative progress and autonomous optimization in the field.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Recent advances in multi-modal large language models (MLLMs) have enabled increasingly sophisticated autonomous visualization agents capable of translating user intentions into data visualizations. However, measuring progress and comparing different agents remains challenging, particularly in scientific visualization (SciVis), due to the absence of comprehensive, large-scale benchmarks for evaluating real-world capabilities. This position paper examines the various types of evaluation required for SciVis agents, outlines the associated challenges, provides a simple proof-of-concept evaluation example, and discusses how evaluation benchmarks can facilitate agent self-improvement. We advocate for a broader collaboration to develop a SciVis agentic evaluation benchmark that would not only assess existing capabilities but also drive innovation and stimulate future development in the field.

Problem

• Lack of comprehensive evaluation benchmarks for SciVis agents
• Challenges in measuring progress and comparing visualization agents
• Need for standardized evaluation to drive innovation in SciVis

Innovation

• Evaluation paradigm for visualization agents
• Multi-modal LLMs for autonomous visualization
• Benchmark development for agent self-improvement