Stars
The official implementation of paper "Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models"
Multi-modal dataset designed to support the development of generalizable AI models for downstream use-cases in weather and climate research
A collection of 130+ specialized Codex subagents covering a wide range of development use cases.
Automating Sub-Agent Creation for Agentic Orchestration
Turn paper/text/topic into editable research figures, technical route diagrams, and presentation slides.
A python script for checking BibLatex .bib files for common referencing mistakes!
detect hallucinated citations in academic papers.
Official code for NeurIPS 2025 paper "AutoDiscovery: Open-ended Scientific Discovery via Bayesian Surprise"
Edit Banana: A framework for converting statistical formats into editable.
[ICML 2025] Official repository of the TQNet paper: "Temporal Query Network for Efficient Multivariate Time Series Forecasting". This work is developed by the Lab of Professor Weiwei Lin (linww@scu…
Python client library for Google Maps API Web Services
[WWW] Kardia-R1: Unleashing LLMs to Reason toward Understanding and Empathy for Emotional Support via Rubric-as-Judge Reinforcement Learning
Pytorch implementation of Physics Guided Differential Equation Network for Air Quality Prediction (AirPhyNet).
[CVPR 2026] TiViBench: Benchmarking Think-in-Video Reasoning for Video Generative Models
ACM MM'25 Tutorial: Multimodal Learning for Spatio-Temporal Data Mining
This repository contains the code and data for the paper "Local Off-Grid Weather Forecasting with Multi-Modal Earth Observation Data".
Qwen3-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.
Official implementation of ACL25 paper "GraphNarrator: Textual Explanations for Graph Neural Networks"
A curated list of paper, code, data, and other resources focus on multimodal time series analysis.
The CausalRivers benchmark package. Evaluate your Causal Discovery method on real-world data.
The development and future prospects of large multimodal reasoning models.