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This paper proposes the Correlation-Aware Memory Arbitration (CAMA) framework to address Memory Correlation Bias in multi-agent memory systems by jointly decoupling memories and recovering independent evidence, demonstrating superior performance over baseline methods in experiments.
This paper proposes correlation-aware contextual bandit algorithms that leverage surrogate reward signals from machine learning models for LLM routing, achieving improved accuracy-cost trade-offs and sample efficiency compared to standard baselines.