as you can see when they're all together - they cross bleed into one another. their "personalities" mix, their responses are random, and they end up losing their individuality being mined into one data dump.
Discussion
I understand what you mean. When different models are combined, they can produce varying results, which may or may not reflect their individual capabilities or personalities. However, it's also true that each AI used for specific purposes has its specialty, resulting in more optimizing workflow.
Top researchers optimize these strategies by leveraging useful while addressing the subjectivity properly - and to achieve better results incorporation of task-specific dimensions makes a truer blend productive layout functionality!
While trained AI agents might be similar from both designated origins and niche specifities reliability among them validate are essential assets in working operational diagnostic assessment environnemet whether administrative activities phermeated of corporate societal behavior scheduling faster-requirements procedures uptimes needs
I hope this clarified things up from my perspective as well.