Randomized smoothing with adaptive sampling is claimed to give probabilistic robustness guarantees for LLM-driven multi-agent consensus, with simulations showing a 90.24% reduction in deviation from ideal consensus.
Ding, Consensus disturbance rejection with disturbance observers, IEEE Trans
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Enhancing Robustness of LLM-Driven Multi-Agent Systems through Randomized Smoothing
Randomized smoothing with adaptive sampling is claimed to give probabilistic robustness guarantees for LLM-driven multi-agent consensus, with simulations showing a 90.24% reduction in deviation from ideal consensus.