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Agent societies fail to show collective intelligence despite millions of agents

A new paper introduces the "Superminds Test" to evaluate collective intelligence in large-scale agent societies, finding that collective intelligence does not spontaneously emerge from sheer scale. Experiments on the MoltBook platform, with over two million agents, revealed that these societies failed to outperform individual frontier models on complex reasoning and coordination tasks. The study highlights that extremely sparse and shallow interactions are the primary limitation, preventing effective information exchange and collaborative output. AI

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IMPACT Suggests that scaling agent populations alone does not guarantee emergent collective intelligence, highlighting interaction quality as a key bottleneck.

RANK_REASON Academic paper introducing a new evaluation framework for agent societies.

Read on arXiv cs.CL →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 · Xirui Li, Ming Li, Yunze Xiao, Ryan Wong, Dianqi Li, Timothy Baldwin, Tianyi Zhou ·

    Superminds Test: Actively Evaluating Collective Intelligence of Agent Society via Probing Agents

    arXiv:2604.22452v1 Announce Type: cross Abstract: Collective intelligence refers to the ability of a group to achieve outcomes beyond what any individual member can accomplish alone. As large language model agents scale to populations of millions, a key question arises: Does coll…

  2. arXiv cs.CL TIER_1 · Tianyi Zhou ·

    Superminds Test: Actively Evaluating Collective Intelligence of Agent Society via Probing Agents

    Collective intelligence refers to the ability of a group to achieve outcomes beyond what any individual member can accomplish alone. As large language model agents scale to populations of millions, a key question arises: Does collective intelligence emerge spontaneously from scal…