# CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context

arXiv: https://arxiv.org/abs/2302.07302 · HTML: https://mlsanity.com/paper/2302.07302/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2023.

## Built on by (most cited first, 22 of 22)

- [CiteME: Can Language Models Accurately Cite Scientific Claims?](https://mlsanity.com/paper/2407.12861/) (2024)
- [Cocoa: Co-Planning and Co-Execution with AI Agents](https://mlsanity.com/paper/2412.10999/) (2024)
- [ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews](https://mlsanity.com/paper/2306.12587/) (2023)
- [Data-driven Discovery with Large Generative Models](https://mlsanity.com/paper/2402.13610/) (2024)
- [Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks](https://mlsanity.com/paper/2304.02623/) (2023)
- [PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers](https://mlsanity.com/paper/2403.02939/) (2024)
- [DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration](https://mlsanity.com/paper/2408.00447/) (2024)
- [The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces](https://mlsanity.com/paper/2303.14334/) (2023)
- [CorpusStudio: Surfacing Emergent Patterns in a Corpus of Prior Work while Writing](https://mlsanity.com/paper/2503.12436/) (2025)
- [Survey of User Interface Design and Interaction Techniques in Generative AI Applications](https://mlsanity.com/paper/2410.22370/) (2024)
- [Citation Recommendation based on Argumentative Zoning of User Queries](https://mlsanity.com/paper/2501.18292/) (2025)
- [Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents](https://mlsanity.com/paper/2602.01405/) (2026)
- [AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionals](https://mlsanity.com/paper/2412.15444/) (2024)
- [Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers](https://mlsanity.com/paper/2510.00361/) (2025)
- [Words as Bridges: Exploring Computational Support for Cross-Disciplinary Translation Work](https://mlsanity.com/paper/2503.18471/) (2025)
- [Improving Attributed Long-form Question Answering with Intent Awareness](https://mlsanity.com/paper/2603.27435/) (2026)
- [TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models](https://mlsanity.com/paper/2507.18945/) (2025)
- [CausalCite: A Causal Formulation of Paper Citations](https://mlsanity.com/paper/2311.02790/) (2023)
- [RealitySummary: Exploring On-Demand Mixed Reality Text Summarization and Question Answering using Large Language Models](https://mlsanity.com/paper/2405.18620/) (2024)
- [From Toil to Thought: Designing for Strategic Exploration and Responsible AI in Systematic Literature Reviews](https://mlsanity.com/paper/2603.05514/) (2026)
- [PaperRouter-Agent: A Content-Grounded LLM Agent for Personalized Hierarchical Paper Routing](https://mlsanity.com/paper/2607.11564/) (2026)
- [FootprintRAG: Visual Analytics for Evidence Context Refinement in RAG-based Scientific Literature Exploration](https://mlsanity.com/paper/2609.19601/) (2026)

## Abstract (arXiv)

When reading a scholarly article, inline citations help researchers contextualize the current article and discover relevant prior work. However, it can be challenging to prioritize and make sense of the hundreds of citations encountered during literature reviews. This paper introduces CiteSee, a paper reading tool that leverages a user's publishing, reading, and saving activities to provide personalized visual augmentations and context around citations. First, CiteSee connects the current paper to familiar contexts by surfacing known citations a user had cited or opened. Second, CiteSee helps users prioritize their exploration by highlighting relevant but unknown citations based on saving and reading history. We conducted a lab study that suggests CiteSee is significantly more effective for paper discovery than three baselines. A field deployment study shows CiteSee helps participants keep track of their explorations and leads to better situational awareness and increased paper discovery via inline citation when conducting real-world literature reviews.