There’s a literature on LLM output homogenization developing. The “Artificial Hivemind” work puts inter-response similarity around 0.80–0.90 even at high temperature. A 2026 factorial audit of persona interventions found something more useful to anyone actually building: persona detail doesn’t produce linear gains, and the guidance is to invest in breadth over depth. Another paper found ordinary personas outperform famous-creative-person personas, because ordinary ones inject more distinct cues. I’ve been running a multi-reader system for months and both findings match what I see. Depth is where I wasted the most time. Elaborate profiles produce elaborate voices that still notice the same things. You get four different writing styles reporting one reading. The intervention that actually moved results was architecture, not description. Every reader works in a separate session, produces a complete written position before encountering any other, and only then meets. That means a convergence between two readers is evidence about the text rather than about the conversation context, because they had no conversation. Which gives a test worth running on your own personas: put your personas in isolation and check whether they ever converge without contact. Total disagreement means they’re allocating roles. Total agreement means they’re one voice. Partial convergence, where the overlaps track the material and the splits track what each persona attends to, I’m taking as hope that the isolation is working. is the only pattern that means anything. submitted by /u/solomonj48103
Originally posted by u/solomonj48103 on r/ArtificialInteligence