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ConvMem is a training-free, highly parallelizable framework that reformulates long-context reasoning as a hierarchical convolution, Inspired by CNNs, that outperforms training-free baselines and avoids the risk of overfitting to parametric priors often observed in RL-trained models on out-of-distribution tasks.
Adaptive Memory via Multi-Agent Collaboration (AMA), a novel framework that leverages coordinated agents to manage memory across multiple granularities, significantly outperforms state-of-the-art baselines while reducing token consumption by approximately 80% compared to full-context methods.
HyMem is a hierarchical framework that explicitly separates the agent's context into distinct functional layers to separate high-level planning from execution and complex analysis, allowing the model to maintain focus and accuracy across complex, long-horizon tasks.
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