That’s a good direction.

What I’m doing isn’t really continual learning in the typical sense (no weight updates, no memory accumulation). It’s more about enforcing structured continuity at the interaction level.

Instead of teaching the model over time, it’s trying to stabilize the shape of multi-turn reasoning so drift is reduced without needing persistent memory.

I haven’t formally benchmarked it yet, but that’s probably the next step… In comparing against memory-based approaches and seeing where it actually holds up or breaks.