# Can AI agents understand spoken conversations about data visualizations in online meetings?

arXiv: https://arxiv.org/abs/2510.00245 · HTML: https://mlsanity.com/paper/2510.00245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 4 of 4)

- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Benchmarking Visual Language Models on Standardized Visualization Literacy Tests](https://mlsanity.com/paper/2503.16632/) (2025)
- [Improved Iterative Refinement for Chart-to-Code Generation via Structured Instruction](https://mlsanity.com/paper/2506.14837/) (2025)
- [MSG-Chart: Multimodal Scene Graph for ChartQA](https://mlsanity.com/paper/2408.04852/) (2024)

## Abstract (arXiv)

In this short paper, we present work evaluating an AI agent's understanding of spoken conversations about data visualizations in an online meeting scenario. There is growing interest in the development of AI-assistants that support meetings, such as by providing assistance with tasks or summarizing a discussion. The quality of this support depends on a model that understands the conversational dialogue. To evaluate this understanding, we introduce a dual-axis testing framework for diagnosing the AI agent's comprehension of spoken conversations about data. Using this framework, we designed a series of tests to evaluate understanding of a novel corpus of 72 spoken conversational dialogues about data visualizations. We examine diverse pipelines and model architectures, LLM vs VLM, and diverse input formats for visualizations (the chart image, its underlying source code, or a hybrid of both) to see how this affects model performance on our tests. Using our evaluation methods, we found that text-only input modalities achieved the best performance (96%) in understanding discussions of visualizations in online meetings.