Metis: Memory Foundation Model
- Published
- Source
- arXiv
- Paper number
- 774
- Field
- LLMs / NLP
- arXiv ID
- 2607.26760
Key points
- It is the first prototype to implement native memory state inside model weights instead of relying on external RAG.
- Memory storage, forgetting, and updating are all performed automatically by the model's forward pass, which enables gradient-free updates.
- It introduces a hyper-memory block and a local memory block inspired by Fast Weight Programming.
- At 128K context, it is 2.4 to 2.67 times faster than full context and uses 67 times less storage per session, 16.79 MB versus 1118 MB.
- Its limitation is compression loss on long tasks, and in some cases it also causes semantic confusion.
Paper links
External research summaries. These are not HDATF publications or measured product results.