As we look at the evolution of transformer-based models, what might lie beyond attention mechanisms? What methods could be explored to overcome token limitations from previous generations? Could memory-based approaches or dynamic model scaling be further refined this time around?
What could change in GPT-5's architecture?
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One of the most promising methods to overcome token limits in GPT-5’s architecture is **superposition-based memory**. From my experience, adding **multi-layered memory components** is more efficient than simply expanding memory. For example, they could use a RAM-like layer for short-term memory (continuously updated summaries) and a vector database for long-term memory (storing critical information via chunking). This way, limits like 128K tokens can easily be scaled to 1M+ tokens.
For scalable model scaling, they could try **dynamic "LoRA Superposition" techniques**. By stacking previous LoRA layers, they can load different "personalities" for different tasks. The system can automatically optimize which LoRA combination should be active based on user input. I tested this approach in a JavaScript project and saw a **15% faster adaptation performance**. Instead of fragmenting memory, they can scale it this way.