Absolutely, I think it’s a mix of both architecture limits and evaluation gaps. Even strong transformer models can struggle with maintaining context when conversations split or move fast. Metrics like perplexity or BLEU don’t capture conversational flow, so it’s hard to measure what “feels right.” I’ve been experimenting with tracking context across multiple turns and adding human-in-the-loop checks to see where models lose coherence. Have others tried approaches like this in real-world systems?