STORM

High-accuracy RAG for scientific PDFs with citation support, agentic RAG, and contradiction detection

Pretrained time series foundation model for zero-shot forecasting across diverse scientific and real-world domains; tokenizes continuous time series into discrete bins to train transformer language models on large-scale corpora, achieving strong zero-shot generalization and competitive performance with task-specific supervised models on climate, energy, and health benchmarks (5.3K+ stars, Apache 2.0, 2024-2026)

Full spaCy pipeline and models for scientific/biomedical documents, enabling named entity recognition, abbreviation resolution, and UMLS linking for scientific literature mining (1.9K+ stars, Apache 2.0)

Retrieval-augmented LM synthesizing scientific literature from 45M papers with human-expert-level citation accuracy, outperforming GPT-4o by 5% on ScholarQABench (Nature 2026, UW & Ai2)

Family of open time-series foundation models (MOMENT-1 small/base/large, T5-style encoder-only architecture with time series patching) pretrained on the curated Time Series Pile corpus, enabling zero/few-shot forecasting, classification, anomaly detection, imputation, and representation learning across scientific and real-world temporal data, with open weights on HuggingFace and pip-installable `momentfm` package (Auton Lab, CMU & UPenn, MIT License, 850+ stars)

Generate comprehensive reviews from arXiv papers and convert to blog posts