๐ช๐ต๐ ๐๐๐ ๐ ๐ฒ๐บ๐ผ๐ฟ๐ ๐๐ถ๐ฒ๐ ๐ง๐ผ ๐ฌ๐ผ๐
I built a knowledge graph from my work sessions. It felt complete. I asked it questions. I got clean answers.
Then I ran a test. I asked a different model to rebuild the system. The model found 97.7% of the words. It found only 61.1% of the structure.
This 36 point gap reveals a problem. I call it premature retrieval closure. The output looks confident. You stop checking it. You assume the structure is whole.
Other systems face this wall:
- Letta deals with lossy compression in summaries.
- CASS fights iterative drift.
- Pavlyshyn uses certainty scores for facts.
- Hyperspell builds a correction layer.
These teams know extraction is hard. They build tools to hide the gap. The more polished the structure, the less you check the source.
I changed my design. I demoted the graph. The raw session record is now the source of truth. The graph is secondary evidence. It is allowed to be wrong.
Check your own memory system. Ask one question: what does your agent treat as the truth?
Trace one answer back.
- Does it end at the raw record? The structure is a convenience.
- Does it end at a summary? The polish is lying to you.
Build your systems so the original record remains.
Source: https://dev.to/john_wade_dev/when-four-memory-systems-hit-the-same-wall-16ei Optional learning community: https://t.me/GyaanSetuAi