# NoteEx: Interactive Visual Context Manipulation for LLM-Assisted Exploratory Data Analysis in Computational Notebooks

arXiv: https://arxiv.org/abs/2511.07223 · HTML: https://mlsanity.com/paper/2511.07223/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 66 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 12 of 12)

- [Make Your LLM Fully Utilize the Context](https://mlsanity.com/paper/2404.16811/) (2024)
- [User-LLM: Efficient LLM Contextualization with User Embeddings](https://mlsanity.com/paper/2402.13598/) (2024)
- [WaitGPT: Monitoring and Steering Conversational LLM Agent in Data Analysis with On-the-Fly Code Visualization](https://mlsanity.com/paper/2408.01703/) (2024)
- [FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System](https://mlsanity.com/paper/1908.00681/) (2019)
- [Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System](https://mlsanity.com/paper/2304.00477/) (2023)
- [InsightLens: Augmenting LLM-Powered Data Analysis with Interactive Insight Management and Navigation](https://mlsanity.com/paper/2404.01644/) (2024)
- [Lost-in-Distance: Impact of Contextual Proximity on LLM Performance in Graph Tasks](https://mlsanity.com/paper/2410.01985/) (2024)
- [StickyLand: Breaking the Linear Presentation of Computational Notebooks](https://mlsanity.com/paper/2202.11086/) (2022)
- [Effective Context Selection in LLM-based Leaderboard Generation: An Empirical Study](https://mlsanity.com/paper/2407.02409/) (2024)
- [NBSearch: Semantic Search and Visual Exploration of Computational Notebooks](https://mlsanity.com/paper/2102.01275/) (2021)
- [Composing Data Stories with Meta Relations](https://mlsanity.com/paper/2501.03603/) (2025)
- [Make It Make Sense! Understanding and Facilitating Sensemaking in Computational Notebooks](https://mlsanity.com/paper/2312.11431/) (2023)

## Built on by (most cited first, 2 of 2)

- [AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agentic Data Analysis](https://mlsanity.com/paper/2608.17834/) (2026)
- [Point, Revise, Review: Grounded Agentic Analysis in Reactive Notebooks with marimo-lens](https://mlsanity.com/paper/2609.19839/) (2026)

## Abstract (arXiv)

Computational notebooks have become popular for Exploratory Data Analysis (EDA), augmented by LLM-based code generation and result interpretation. Effective LLM assistance hinges on selecting informative context -- the minimal set of cells whose code, data, or outputs suffice to answer a prompt. As notebooks grow long and messy, users can lose track of the mental model of their analysis. They thus fail to curate appropriate contexts for LLM tasks, causing frustration and tedious prompt engineering. We conducted a formative study (n=6) that surfaced challenges in LLM context selection and mental model maintenance. Therefore, we introduce NoteEx, a JupyterLab extension that provides a semantic visualization of the EDA workflow, allowing analysts to externalize their mental model, specify analysis dependencies, and enable interactive selection of task-relevant contexts for LLMs. A user study (n=12) against a baseline shows that NoteEx improved mental model retention and context selection, leading to more accurate and relevant LLM responses.