Title: Sessa: Selective State Space Attention

Source: http://arxiv.org/abs/2604.18580v2

Summary:

This paper introduces a novel architectural primitive that integrates attention into a recurrent feedback path, achieving power-law memory tails for superior long-context information preservation. It represents a significant breakthrough by combining the strengths of Transformers and State-Space Models to enable flexible selective retrieval that does not decay with sequence distance.