Researching the Current LLM Memory Problem

Hook

I have been working with many kinds of LLMs recently. It is very clear that LLMs forget important things because of the limited context window. This sparked my interest.

Problem

LLMs forget important information due to context-window limits.

Approach

I am planning to research this area across three major topics:

1. More context in a context window: How to store more important content in a fixed context window. I am especially curious about edge computing, such as reducing memory usage for AI inference on embedded systems.
2. Long-term memory: How to establish long-term memory for LLMs.
3. Security: How to store and use context securely and efficiently, so user data is never exposed to the LLM.

The key is to understand the concepts of modern LLM architecture and the real bottlenecks.

Tradeoffs

I am still in the early research stage, so this is scoped to defining the core problem areas first.

Links and References

None yet.

Key Takeaways

• LLM memory limits are a practical issue I want to study.
• I am focusing on context capacity, long-term memory, and secure context use.
• Understanding modern LLM architecture and real bottlenecks is the key starting point.