14 citations · 19 across the 8 of their papers we have counts for
12 papers
Shared Geometry Is Not Shared Physics: A Layerwise Test of the Platonic Representation Hypothesis in Astronomy
Kshitij Duraphe, Aravind Kannappan, Dun Li Chan +4
In this paper we investigate whether geometrically aligned astronomical representations are also scientifically interchangeable. We analyze every layer's representation from 36 pre…
Do Astronomical Foundation Models Know When They Will Fail?
Vaidehi Bulusu, Mehul Goyal, Md Rabius Sany Apu +2
Astronomical foundation models are increasingly used as reusable representations across surveys, but it is unclear when the same object remains consistently represented across inst…
Gestalt: a meta-foundation model for astronomy
Michael J. Smith, Shashwat Sourav
The Platonic Representation Hypothesis predicts that sufficiently scaled foundation models converge on a shared representation of the world. As each non-converged model gives a noi…
What AstroPT knows about galaxies, and what that can teach us about LLMs
UniverseTBD, :, Kshitij Duraphe +3
Interpretability research increasingly asks when concepts emerge during training and whether linear probes recover real structure, but in language models these claims are hard to v…
The Galaxy's Guide to the Tokenizer: A Benchmark for Scientific Foundation Models
Sogol Sanjaripour, Michael J. Smith, Manuel Pérez-Carrasco +3
Tokenization is central to adapting scientific data for transformer-based foundation models, yet its impact on learned representations remains poorly understood. We compare four to…
Augmenting representations with scientific papers
Nicolò Oreste Pinciroli Vago, Rocco Di Tella, Carolina Cuesta-Lázaro +3
Astronomers have acquired vast repositories of multimodal data, including images, spectra, and time series, complemented by decades of literature that analyzes astrophysical source…
AstroLLaVA: towards the unification of astronomical data and natural language
Sharaf Zaman, Michael J. Smith, Pranav Khetarpal +8
We present AstroLLaVA, a vision language model for astronomy that enables interaction with astronomical imagery through natural dialogue. By fine-tuning the LLaVA model on a divers…
A Survey on Hypothesis Generation for Scientific Discovery in the Era of Large Language Models
Atilla Kaan Alkan, Shashwat Sourav, Maja Jablonska +14
Hypothesis generation is a fundamental step in scientific discovery, yet it is increasingly challenged by information overload and disciplinary fragmentation. Recent advances in La…
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
The Multimodal Universe Collaboration, Jeroen Audenaert, Micah Bowles +26
We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MU…
pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy
Kartheik G. Iyer, Mikaeel Yunus, Charles O'Neill +27
The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We pr…
AstroPT: Scaling Large Observation Models for Astronomy
Michael J. Smith, Ryan J. Roberts, Eirini Angeloudi +1
This work presents AstroPT, an autoregressive pretrained transformer developed with astronomical use-cases in mind. The AstroPT models presented here have been pretrained on 8.6 mi…
AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets
Ernest Perkowski, Rui Pan, Tuan Dung Nguyen +11
We explore the potential of enhancing LLM performance in astronomy-focused question-answering through targeted, continual pre-training. By employing a compact 7B-parameter LLaMA-2…