PRISM: Parallel Residual Iterative Sequence Model
Researchers have developed PRISM, a novel sequence modeling architecture designed to balance the expressivity of Transformers with the efficiency of linear models. PRISM addresses the serial dependencies found in iterative methods like Test-Time Training by reconstructing the iterative process in a parallelizable form. This is achieved through a Write-Forget Decoupling strategy and a two-stage proxy architecture, enabling significantly higher throughput compared to existing optimization methods. AI
IMPACT Introduces a new parallelizable architecture that significantly boosts throughput for sequence modeling tasks.