You’re right dumping all memory into the context window doesn’t scale. But with CORE, we don’t do that.
We use a reified knowledge graph for memory, where:
Each fact is a first-class node (with timestamp, source, certainty, etc.)
- Nodes are typed (Person, Tool, Issue, etc.) and richly linked
- Activity (e.g. a Slack message) is decomposed and connected to relevant context
This structure allows precise subgraph retrieval based on semantic, temporal, or relational filters—so only what’s relevant is pulled into the context window.
It’s not just RAG over documents. It’s graph traversal over structured memory. The model doesn’t carry memory—it queries what it needs.
So yes, the memory problem is real—but reified graphs actually make it tractable.
Comments
You’re right dumping all memory into the context window doesn’t scale. But with CORE, we don’t do that.
We use a reified knowledge graph for memory, where: Each fact is a first-class node (with timestamp, source, certainty, etc.) - Nodes are typed (Person, Tool, Issue, etc.) and richly linked - Activity (e.g. a Slack message) is decomposed and connected to relevant context
This structure allows precise subgraph retrieval based on semantic, temporal, or relational filters—so only what’s relevant is pulled into the context window. It’s not just RAG over documents. It’s graph traversal over structured memory. The model doesn’t carry memory—it queries what it needs.
So yes, the memory problem is real—but reified graphs actually make it tractable.