Multi-Modal Frontiers & Vector Architecture

Real-time vector streaming keeps embeddings and retrieval indices fresh as data changes, so search and RAG answers reflect the lat…

GraphRAG combines retrieval with a knowledge graph so answers can follow relationships, not just similarity. It excels on multi-ho…

Cross-modal alignment maps text, images, audio, and other signals into a shared embedding space so one modality can retrieve anoth…

Small language models run on modest hardware with low latency and predictable cost, making them attractive for edge and high-volum…

Mixture of experts activates only a subset of parameters per token, delivering large-model quality at lower compute. The trade is …

State space models handle long sequences with linear-time complexity and continuous-time dynamics, making them strong on streaming…

Model weights, embeddings, and training data are valuable intellectual property that can be stolen through APIs, insider access, o…

Open-source visual language models bring image understanding and generation to self-hosted stacks with control over data and cost.…

Edge AI orchestration coordinates models, data, and devices across distributed environments where connectivity and compute are lim…

Sampling strategies and decoding choices affect not just output quality but energy and cost per token. Efficient sampling reduces …