ConvMem: Convolutional Memory for Long-Context Reasoning
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.
Hongming Zhang, Zhao-Zhen Gu, Fengshuo Bai et al.
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