Different viewpoints on a team are only an asset when the team is actively built to surface and use them.

This reads the question as: is cognitive diversity a latent resource that pays off automatically, or one that only cashes out when the team is built to activate it? The corpus leans hard toward the second answer — diversity is necessary but inert until something makes agents actually engage each other's differences. The sharpest evidence is that diversity without a foundation backfires: multi-agent teams beat solo ideation, but only when members carry genuine senior expertise; drop the expertise and a diverse team underperforms a single competent agent, because cognitive stimulation without grounding produces process losses instead of insight Does cognitive diversity alone improve multi-agent ideation quality?. So 'more perspectives' is not the payoff — it's raw material that can just as easily become noise.

What turns that raw material into gains is structure that forces exploration. Framing a single model's reasoning as a dialogue between distinct agents beats monologue reasoning precisely on tasks needing multiple approaches, because the dialogue format breaks the model out of a fixed strategy Can dialogue format help models reason more diversely?. Decentralized science teams that deliberately maintain competing hypotheses and share their failures outperform central planners under the same budget Can decentralized teams outperform central planners in long-running science?. In both cases the diversity only pays because the design keeps rival lines alive and in contact — the exploring is the mechanism, not a nice-to-have.

The flip side is that diversity is fragile and easily collapses when nothing protects it. RL training squeezes behavioral variety in search agents through the same entropy-collapse seen in reasoning — policies converge on one narrow reward-maximizing groove — while SFT on varied demonstrations keeps exploration breadth open Does reinforcement learning squeeze exploration diversity in search agents?. Training agents together tends to homogenize them unless you deliberately give them distinct roles: multi-agent finetuning preserves diversity only by training generation and critic agents on separate role-dependent data, and removing the critic role degrades the whole system Can multiple agents stay diverse during training together?. Even preference tuning's effect on diversity flips by domain — it compresses variety where convergence is rewarded and expands it where distinctiveness is Does preference tuning always reduce diversity the same way?. Diversity, in other words, decays toward uniformity unless the incentive structure actively rewards keeping it.

There's a quieter caveat worth pulling in: not all team gains come from cognitive diversity at all. One line of work argues 80% of performance variance across multi-agent systems traces to token budget rather than coordination intelligence What makes multi-agent teams actually perform better?, and that teams also improve by pruning — contribution-scoring that deactivates uninformative agents mid-inference Can multi-agent teams automatically remove their weakest members?. Read together with the diversity findings, the picture is that a team's edge comes from *managed* heterogeneity — enough spend, the right members kept, the wrong ones dropped, roles that stay distinct — not from diversity as a standalone virtue.

So the answer to the literal question is essentially yes, with a twist: cognitive diversity doesn't just pay off more when actively explored — left unexplored and unprotected it can be net-negative, and the training dynamics of these systems actively erode it unless you design against that. The thing you didn't know you wanted to know is that the real work isn't assembling diverse agents; it's building the harness — roles, competing hypotheses, dialogue, expertise floors — that keeps them from quietly collapsing into the same voice.