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The University of Hong Kong
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Memory Correlation Bias can mislead multi-agent systems into false majorities, but CAMA effectively counters this by recovering independent evidence from correlated memories.
Client-specific sufficiency estimation in FedSGA allows for more efficient federated learning, boosting performance while slashing unnecessary computation costs.
LLM agents can be made more reliable by structurally verifying their internal reasoning, rather than relying on consensus which conflates agreement with faithfulness.
