# Crystalline: Lowering the Cost for Developers to Collect and Organize Information for Decision Making

arXiv: https://arxiv.org/abs/2202.02175 · HTML: https://mlsanity.com/paper/2202.02175/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2022.

## Built on by (most cited first, 7 of 7)

- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions](https://mlsanity.com/paper/2310.01331/) (2023)
- [Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Models](https://mlsanity.com/paper/2310.02161/) (2023)
- [The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces](https://mlsanity.com/paper/2303.14334/) (2023)
- [Orca: Browsing at Scale Through User-Driven and AI-Facilitated Orchestration Across Malleable Webpages](https://mlsanity.com/paper/2505.22831/) (2025)
- [Gensors: Authoring Personalized Visual Sensors with Multimodal Foundation Models and Reasoning](https://mlsanity.com/paper/2501.15727/) (2025)
- [Designing for Self-Regulation in Informal Programming Learning: Insights from a Storytelling-Centric Approach](https://mlsanity.com/paper/2507.22671/) (2025)

## Abstract (arXiv)

Developers perform online sensemaking on a daily basis, such as researching and choosing libraries and APIs. Prior research has introduced tools that help developers capture information from various sources and organize it into structures useful for subsequent decision-making. However, it remains a laborious process for developers to manually identify and clip content, maintaining its provenance and synthesizing it with other content. In this work, we introduce a new system called Crystalline that attempts to automatically collect and organize information into tabular structures as the user searches and browses the web. It leverages natural language processing to automatically group similar criteria together to reduce clutter as well as passive behavioral signals such as mouse movement and dwell time to infer what information to collect and how to visualize and prioritize it. Our user study suggests that developers are able to create comparison tables about 20% faster with a 60% reduction in operational cost without sacrificing the quality of the tables.