Wiki Foundation Model unifies graphs, embeddings for agents

Flat vector retrieval fails at complex multi-hop reasoning, while conventional GraphRAG collapses under distributed communication overheads. WFM demonstrates that next-generation agent memory requires treating knowledge graphs as differentiable neural topologies with native GPU collectives rather than disjoint pipelines.

• –Bridges discrete knowledge graph links with dense chunk embeddings, preserving relational hierarchy without losing continuous semantic context during multi-hop reasoning.
• –Introduces attention variance regularization across relation-aware message passing to stabilize gradients and prevent attention saturation over deep reasoning paths.
• –Engineers an NCCL boundary exchange protocol that hoists static partition indices into fixed-shape GPU-to-GPU collectives, bypassing CPU serialization for a 10.5x distributed training speedup.
• –Provides an agent-native memory substrate that turns structured markdown wikis into continuously trainable, high-throughput retrieval representations.