Richer Representations for Neural Algorithmic Reasoning via Auxiliary Reconstruction 事件

Richer Representations for Neural Algorithmic Reasoning via Auxiliary Reconstruction arXiv:2606.00559v1 Announce Type: cross Abstract: Neural algorithmic reasoning has emerged as a popular research direction. It aims to train neural networks to mimic the step-by-step behavior of classical rule-based algorithms. More specifically, the execution of such algorithms can be abstracted as a sequence of states, where each state represents the intermediate outcome after an execution step. The training