Eliot: Interactively $\underline{E}$xploring Fast-Changing Scientific $\underline{Li}$terature Trends with $\underline{O}$nline Da$\underline{t}$a and Learning

2026-05-26

AI Summary

The exponential growth of scientific literature poses significant challenges in tracking the dynamic evolution of research fields. To address this, this work proposes an interactive and traceable system that, upon user query, retrieves relevant arXiv papers in real time and automatically generates semantic clusters and keyword labels based on titles and abstracts, visualizing thematic evolution over time. Departing from conventional manual categorization or domain-specific scripts, the system employs MiniLM embeddings, 10-dimensional UMAP for dimensionality reduction, and hierarchical clustering to enable end-to-end automated analysis. Evaluated across eight arXiv subject areas, 85% of the generated cluster labels were rated by users as semantically coherent, with domain experts particularly highlighting the system’s utility for rapid overview and exploratory analysis in fast-evolving technical domains.

Abstract

The rapid growth of scientific publishing has made it increasingly difficult to track how fast-moving areas evolve. Search engines and LLM-based assistants retrieve or summarize papers, but often hide how the corpus was selected, organized, or connected to temporal patterns. We present $\texttt{Eliot}$, a publicly deployed interactive system for traceable exploration of evolving scientific literature. Motivated by two studies on Large Language Models (LLMs) and Automated Planning and Scheduling (APS), $\texttt{Eliot}$ generalizes literature-evolution analysis beyond hand-built taxonomies and domain-specific scripts. Given explicit query terms and filters, it retrieves arXiv papers at query time, represents each paper by title and abstract, clusters the corpus into themes, assigns representative keywords, and visualizes each cluster's publication-year distribution. We evaluate $\texttt{Eliot}$ as both an applied system and an interactive research aid. An offline configuration study across eight arXiv domains compares document representations, dimensionality reduction methods, and clustering algorithms using intrinsic clustering and topic-coherence metrics; the results support MiniLM embeddings with 10-dimensional UMAP and Agglomerative Clustering as a practical default. A scenario-based survey and expert focus group assess interpretability and use contexts: participants rated cluster labels as meaningful in 85% of scenario responses, and feedback indicated that $\texttt{Eliot}$ is most valuable for auditable overviews of rapidly changing technical areas. These results suggest that query-time clustering and temporal inspection can complement search and generation tools by helping researchers inspect and refine the evidence behind literature trends.

Problem

• scientific literature trends
• fast-changing research areas
• traceable exploration
• temporal patterns
• literature evolution

Innovation

• query-time clustering
• interactive literature exploration
• temporal trend visualization
• traceable scientific analysis
• embedding-based document representation