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cs.AI2026
AquiLLM: Evaluating Faithfulness in Open-Weight RAG-LLM Systems for Scientific Research
Bernie Boscoe, Srinath Saikrishnan, Vikram Seenivasan +6
Scientific research increasingly relies on large, heterogeneous data sources, motivating interest in retrieval-augmented generation (RAG) systems that provide natural language acce…
astro-ph.IM2026
Improving Generalization and Uncertainty Quantification of Photometric Redshift Models
Jonathan Soriano, Tuan Do, Srinath Saikrishnan +4
Accurate redshift estimates are a vital component in understanding galaxy evolution and precision cosmology. In this paper, we explore approaches to increase the applicability of m…
astro-ph.IM2026
Combining datasets with different ground truths using Low-Rank Adaptation to generalize image-based CNN models for photometric redshift prediction
Vikram Seenivasan, Srinath Saikrishnan, Andrew Lizarraga +3
In this work, we demonstrate how Low-Rank Adaptation (LoRA) can be used to combine different galaxy imaging datasets to improve redshift estimation with CNN models for cosmology. L…