AI Infra2017
Provides reusable PyTorch modules and utilities to build, train and scale Graph Neural Networks — includes many implemented GNN layers, benchmark datasets, minibatch/sampling loaders, and support for large-scale, heterogeneous, temporal and point-cloud graphs.
AI Train2023
Modular PyTorch-based framework for building, training, and deploying physics-informed ML models (neural operators, PINNs, GNNs, diffusion). Provides GPU‑optimized training, domain-specific datapipes for meshes/point clouds, distributed scaling and a model zoo.
AI Model2023
Provides code and pretrained models for WeatherNext 2 and WeatherNext Cyclones — ML-based global, medium-range atmospheric forecasting models from Google DeepMind/Google Research. Includes Colab demos, pretrained weights, and guides for cloud access and inference.
AI Infra2025
Combines a vector store, Cypher-style graph queries, and on-device LLM inference in one Rust engine, with a graph neural network that reranks results and adapts to query patterns in under a millisecond. Services ship as self-contained .rvf containers.
AI Video Papers2026
Decouples perception and reasoning for hours-long videos by streaming inputs into a three-tier Hierarchical Graph Memory and using an agentic Observation–Reason–Action retrieval loop; reduces reasoning context to ~2% of full video while improving benchmark accuracy.
Trains a transformer-based graph encoder with RL-guided adaptive masking so retrieved subgraphs embed relationships that better align with frozen LLM text encoders, improving GraphRAG performance with non-parametric retrievers on GraphQA benchmarks.
Performs native structural reasoning for proteins, small molecules and inorganic crystals by tokenizing coordinates, topologies and periodic connectivities into a unified structure-aware vocabulary. Treats structural tokens as addressable evidence to produce interpretable prediction traces and improves accuracy across biology, chemistry and materials benchmarks.
AI Agent Papers2026
Proposes “Graph Engineering”: using explicit, dynamic graphs to represent tasks, agents, tools, and system state so LLM-based agent systems can coordinate, persist, and evolve. Surveys principles, methods, applications, and curates related resources.