# ComLittee: Literature Discovery with Personal Elected Author Committees

arXiv: https://arxiv.org/abs/2302.06780 · HTML: https://mlsanity.com/paper/2302.06780/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2023.

## Built on by (most cited first, 10 of 10)

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- [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025)
- [Words as Bridges: Exploring Computational Support for Cross-Disciplinary Translation Work](https://mlsanity.com/paper/2503.18471/) (2025)
- [TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models](https://mlsanity.com/paper/2507.18945/) (2025)
- [RealitySummary: Exploring On-Demand Mixed Reality Text Summarization and Question Answering using Large Language Models](https://mlsanity.com/paper/2405.18620/) (2024)
- [Generation, Evaluation, and Explanation of Novelists' Styles with Single-Token Prompts](https://mlsanity.com/paper/2511.20459/) (2025)
- [From Toil to Thought: Designing for Strategic Exploration and Responsible AI in Systematic Literature Reviews](https://mlsanity.com/paper/2603.05514/) (2026)

## Abstract (arXiv)

In order to help scholars understand and follow a research topic, significant research has been devoted to creating systems that help scholars discover relevant papers and authors. Recent approaches have shown the usefulness of highlighting relevant authors while scholars engage in paper discovery. However, these systems do not capture and utilize users' evolving knowledge of authors. We reflect on the design space and introduce ComLittee, a literature discovery system that supports author-centric exploration. In contrast to paper-centric interaction in prior systems, ComLittee's author-centric interaction supports curation of research threads from individual authors, finding new authors and papers with combined signals from a paper recommender and the curated authors' authorship graphs, and understanding them in the context of those signals. In a within-subjects experiment that compares to an author-highlighting approach, we demonstrate how ComLittee leads to a higher efficiency, quality, and novelty in author discovery that also improves paper discovery.