passformer

Passformer is a family of neural network architectures that blends ideas from transformer models with pass-through processing to handle sequential or structured data more efficiently. The core idea is to propagate information through a sequence of lightweight processing units, or passes, each applying local transformations before passing its output to the next stage. This contrasts with a single, global attention mechanism dominating the computation, offering a modular approach to information flow.

In a passformer, each pass receives an input representation, performs targeted computations such as small attention blocks, feed-forward transformations, or gating, and then forwards a refined representation onward. Passes can be arranged in stacks, with skip connections and optional residual paths to preserve information across layers. Some designs restrict inter-pass communication to local windows or predefined subsets to reduce computational load, while others employ sparse or dynamic routing to maintain performance on longer sequences.

Variants of passformer differ in how passes are organized and how information is merged across stages. Common variants include single-pass configurations optimized for low latency, multi-pass stacks that trade extra passes for higher accuracy, and hierarchical forms that operate at multiple temporal or spatial scales. These designs allow practitioners to tailor trade-offs between speed, memory usage, and modeling capacity.

Applications of passformer span natural language processing, time-series forecasting, video analysis, and multimodal tasks where long-range dependencies are important. Advantages include improved scalability to long sequences, modularity for customization, and potential reductions in memory usage. Limitations involve training stability, need for careful architectural choices, and a developing ecosystem compared with established transformer implementations. See also Transformer, efficient transformers, and sequence models.