alphaXiv(@askalphaxiv):"Metis: Memory Foundation Model" Most AI agents still use memory as an external RAG-style module, so the model retrieves old text instead of actually remembering. This paper makes memory native to the Transformer. So past interactions are compressed into dynamic layer states and read through memory attention during normal forward passes. The model weights stay frozen at inference, but its memory state updates without gradients, giving the model persistent memory inside the backbone. Still early and lossy, but this is yet another paper with a big step toward agents that remember natively instead of outsourcing memory to a database.

2026.07.31 18:16

"Metis: Memory Foundation Model" Most AI agents still use memory as an external RAG-style module, so the model retrieves old text instead of actually remembering. This paper makes memory native to the Transformer. So past interactions are compressed into dynamic layer states and read through memory attention during normal forward passes. The model weights stay frozen at inference, but its memory state updates without gradients, giving the model persistent memory inside the backbone. Still early and lossy, but this is yet another paper with a big step toward agents that remember natively instead of outsourcing memory to a database.