Beyond Individual Intelligence
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A multi-agent systems survey covering 200+ papers, organized along three axes: collaboration mechanisms, failure attribution, and self-evolution. Each axis is treated as a distinct research line. The self-evolution chapter maps how memory, meta-learning, and procedure-editing approaches intersect.
Three orthogonal axes: Collaboration mechanisms cover who communicates with whom and how. Failure attribution covers methods for localizing errors across agents. Self-evolution covers how a system updates its own behavior over time.
Failure attribution as a first-class topic: Errors propagate through coordination protocols in multi-agent systems, making attribution difficult. The survey treats attribution methodology as a research area rather than a debugging activity.
Self-evolution as a field map: The chapter identifies overlap between memory work, meta-learning, and procedure-editing approaches, and surfaces open questions in each area.
Why it matters: The taxonomy provides a vocabulary for comparing multi-agent systems along axes that prior work has often conflated.
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