7 papers
AquiLLM: An Architecture for Supporting Tacit Knowledge Capture in Research Groups
Jack Stark, Srinath Saikrishnan, Vikram Seenivasan +3
Recent advances in retrieval-augmented generation (RAG) and large language models (LLMs) enable researchers to integrate AI into scientific workflows. However, using proprietary co…
CornerCase: Automated Extremal Testing of Protocol Implementations using LLMs
Rathin Singha, Kuan Qian, Srinath Saikrishnan +6
Many software bugs in network protocol implementations arise near specification boundaries, such as inputs just within or outside allowed ranges, or messages that are valid in isol…
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…
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…
Multi-Modal Masked Autoencoders for Learning Image-Spectrum Associations for Galaxy Evolution and Cosmology
Morgan Himes, Samiksha Krishnamurthy, Andrew Lizarraga +5
Upcoming surveys will produce billions of galaxy images but comparatively few spectra, motivating models that learn cross-modal representations. We build a dataset of 134,533 galax…
Extremal Testing for Network Software using LLMs
Rathin Singha, Harry Qian, Srinath Saikrishnan +4
Physicists often manually consider extreme cases when testing a theory. In this paper, we show how to automate extremal testing of network software using LLMs in two steps: first,…