OpenAI’s latest memory update is a big step forward in AI persistence. But what comes next ?
Persistent recall is a game changer, allowing LLMs to retain useful context across interactions. However, memory systems could evolve beyond just storing and retrieving data : they could become more adaptive, dynamic, and context-aware
We’ve been exploring a hybrid approach that combines :
• Memory recall with adaptive scoring
• Contextual embeddings and retrieval mechanisms
• Memory agents and reinforcement learning
This could enable LLMs to refine their memory over time, balancing persistence with adaptability… learning not just what to retain, but how to refine memory dynamically
But can Al memory truly evolve without control mechanisms ?
What role should context scoring, retrieval strategies, and reinforcement learning play in shaping the next generation of Al memory ?
What do you think ? How far should AI memory go ?
• Should LLMs be able to “forget” strategically ?
• How do we balance persistent memory with adaptability ?
Would love to hear thoughts from the community on this !