Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents. This challenge raises the imperative of a cohesive memory module, which can elevate vanilla LLMs into autonomous reading agents. Despite the emergence of some heuristic approaches, a systematic design principle remains absent. To fill this void, we draw inspiration from Jean Piaget's Constructivist Theory, illuminating three traits of the agentic memory--structured schemata, flexible assimilation, and dynamic accommodation.

Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents. This challenge raises the imperative of a cohesive memory module, which can elevate vanilla LLMs into autonomous reading agents. Despite the emergence of some heuristic approaches, a systematic design principle remains absent. To fill this void, we draw inspiration from Jean Piaget's Constructivist Theory, illuminating three traits of the agentic memory---structured schemata, flexible assimilation, and dynamic accommodation.

Few-shot classification aims to recognize unlabeled samples from unseen classes given only few labeled samples.

Heterogeneous federated learning without assuming any structure is challenging due to the conflicts among non-identical data distributions of clients.

Inthiswork,wepropose anovelCrossAttention Network to address the challenging problems in few-shot classification.

This same algorithm can then be used to reconstruct the true DAGGfrom the true ordering . Once the ordering is known, existing nonlinear variable selection methods [4, 11, 16, 25, 28, 46] suffice to learn the parent setspa(j)and hence the graphG. In our experiments, we use exactly this procedure to learnGfrom the order, based on the data. There are two cases: (i)Bj =, and (ii)Bj 6= . If instead we haveσ23 = var(z3) = 1/3, the condition would be violated.

We train MoCov2 and BYOL under the ResNet-18 architecture on theORIGINALdataset of the Background Challenge for 800 epochs with batch size 256.