A survey that unifies prior work on multi-agent LLM systems via the LIFE framework, mapping dependencies across collaboration, failure attribution, and autonomous self-evolution while identifying cross-stage challenges.
arXiv preprint arXiv:2507.08944 , year =
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A decision-theoretic model based on the observed Confirmation-Diagnosis-Correction-Redo user pattern places intermediate confirmations in AI agent tasks, yielding 81% user preference and 13.54% faster completion versus confirm-at-end.
CONCAT introduces a consensus- and confidence-driven ad hoc teaming method that reduces communication overhead in LLM-based multi-agent systems by up to 50% latency while improving efficiency ratio without any training.
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