that's the exact problem we've been solving! Context bloat vs. memory depth is the core challenge.
our approach tackles this by being selective, not comprehensive. We don't dump everything into context - instead, we:
- use graph structure to identify truly relevant facts (not just keyword matches) - leverage temporal tracking to prioritize current information and filter out outdated beliefs - structure memories as discrete statements that can be included/excluded individually the big advantage? Instead of retrieving entire conversations or documents, we can pull just the specific facts and relevant episodes needed for a given query.
it's like having a good assistant who knows when to remind you about something relevant without overwhelming you with every tangentially related memory.
the graph structure also gives users more transparency - they can see exactly which memories are influencing responses and why, rather than a black-box retrieval system.
ps: one of the authors of CORE