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The paper proposes R2-MAD, a framework that enhances multi-agent debate in large language models by using experience memory and confidence estimation to address shared misconceptions, achieving consistent improvements over baselines.
FinAcumen is a framework that accumulates reasoning experience from prior trajectories into a persistent memory bank for financial multimodal reasoning, improving performance across four benchmarks while maintaining a frozen 8B vision-language model.
DeltaMem organizes LLM agent memory into residual trees to reduce redundancy and retrieval conflicts, storing incremental variations of experiences for continual learning.
Proposes MedExpMem, an experience memory framework that enables medical vision-language models to accumulate and retrieve discriminative diagnostic experience from past cases, improving differential diagnosis accuracy by up to 7.0% on a radiology benchmark.