larnn

Larnn is a term used in artificial intelligence to denote a family of neural network architectures that seek to combine recurrent processing with localized attention and memory components to improve modeling of sequential data. The term does not refer to a single standardized design; variations share the aim of capturing both short-range dynamics and longer-range dependencies without the full computational cost of global attention mechanisms.

In typical larnn designs, a recurrent core (such as an LSTM or GRU) is augmented with a local attention module that focuses on a sliding window of recent timesteps. Some variants introduce an external memory or differentiable scratchpad that stores compact representations of past events, along with gating mechanisms to control when information is read or written. The result is a model that can maintain state over longer horizons while remaining more efficient than architectures that rely on full-sequence attention. Training generally proceeds with standard supervised objectives for sequence data, using backpropagation through time or its modern variants, with regularization and sometimes self-supervised pretraining.

Common applications include language modeling, time-series forecasting, music generation, and control problems in robotics, where maintaining context over long sequences is valuable. The approach is studied mainly in research contexts and is evaluated on benchmark datasets to assess trade-offs between accuracy, latency, and memory usage. Critics note the increased architectural complexity and the need for careful memory management, while proponents argue that larnn-style models can offer practical advantages on data with irregular timing or long-range dependencies. See also: recurrent neural networks, LSTM, Transformer, attention mechanisms.