2 papers
cs.AI2026
FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation
Samuel Hildebrand, Curtis Taylor, Sean Oesch +5
Retrieval-augmented generation (RAG) has emerged as a promising paradigm for improving factual accuracy in large language models (LLMs). We introduce a benchmark designed to evalua…
cs.LG2026
Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals
Jordan Tschida, Matthew Yohe, Edward Kane +10
Time series classification (TSC) of biological signals has progressed from handcrafted, modality-specific approaches to deep architectures capable of representing the diverse wavef…