# pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy

arXiv: https://arxiv.org/abs/2408.01556 · HTML: https://mlsanity.com/paper/2408.01556/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2024.

## Built on by (most cited first, 6 of 6)

- [ReplicationBench: Can AI Agents Replicate Astrophysics Research Papers?](https://mlsanity.com/paper/2510.24591/) (2025)
- [Completing A Systematic Review in Hours instead of Months with Interactive AI Agents](https://mlsanity.com/paper/2504.14822/) (2025)
- [What is the Role of Large Language Models in the Evolution of Astronomy Research?](https://mlsanity.com/paper/2409.20252/) (2024)
- [From Queries to Criteria: Understanding How Astronomers Evaluate LLMs](https://mlsanity.com/paper/2507.15715/) (2025)
- [Evaluating Retrieval-Augmented Generation Agents for Autonomous Scientific Discovery in Astrophysics](https://mlsanity.com/paper/2507.07155/) (2025)
- [AstroRAG -- A Pagerank-Based Retrieval-Augmented Generation Pipeline for Question Answering in Astronomy](https://mlsanity.com/paper/2605.25039/) (2026)

## Abstract (arXiv)

The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with keywords. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), Pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use keywords or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool's versatility through case studies, showcasing its application in various research scenarios. The system's performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, Pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature.