Reactive Transformer: Stateful Real-Time Language Models

The October 2025 paper introduces the **Reactive Transformer (RxT)**, a novel neural network architecture designed by Adam Filipek and Reactive AI to overcome the scaling and latency issues of current Large Language Models (LLMs) in long-form conversations. Unlike traditional **stateless LLMs**, which suffer from quadratic computational complexity by reprocessing the entire conversation history, RxT adopts an **event-driven, stateful paradigm**. The core innovation is an integrated, fixed-size **Short-Term Memory (STM)** system and an **asynchronous operational cycle** that decouples the fast response generation from the computationally intensive memory update, leading to linear scaling of total conversational cost. Experimental results on synthetic data demonstrate that RxT models, even smaller ones, **significantly outperform comparable stateless LLMs** in perplexity and conversational coherence while maintaining constant, low inference latency, validating the efficiency and design of the architecture and its four-stage training curriculum.Source:https://arxiv.org/pdf/2510.03561https://rxai.dev

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