Reactive Transformer (RxT) -- Stateful Real-Time Processing for
  Event-Driven Reactive Language Models
The Transformer architecture has become the de facto standard for Large
Language Models (LLMs), demonstrating remarkable capabilities in language
understanding and generation. However, its application in conversational AI is
fundamentally constrained by its stateless nature and the quadratic
computational complexity (O(L^2)) with respect to sequence length L.
Current models emulate memory by reprocessing an ever-expanding conversation
history with each turn, leading to prohibitive costs and latency in long
dialogues. This paper introduces the Reactive Transformer (RxT), a novel
architecture designed to overcome these limitations by shifting from a
data-driven to an event-driven paradigm. RxT processes each conversational turn
as a discrete event in real-time, maintaining context in an integrated,
fixed-size Short-Term Memory (STM) system. The architecture features a distinct
operational cycle where a generator-decoder produces a response based on the
current query and the previous memory state, after which a memory-encoder and a
dedicated Memory Attention network asynchronously update the STM with a
representation of the complete interaction. This design fundamentally alters
the scaling dynamics, reducing the total user-facing cost of a conversation
from quadratic (O(N^2 cdot T)) to linear (O(N cdot T)) with respect to
the number of interactions N. By decoupling response generation from memory
updates, RxT achieves low latency, enabling truly real-time, stateful, and
economically viable long-form conversations. We validated our architecture with
a series of proof-of-concept experiments on synthetic data, demonstrating
superior performance and constant-time inference latency compared to a baseline
stateless model of comparable size.
68e46ffee4e093a7044e4ccb
Adam Filipek
claimed_verified
2025-10-07T12:27:21.892Z
675197c3ae96d7ba4b4a6c66
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/I2GHgrv70cfT8C5EbK6Q5.png
Adam Filipek
user
AdamF92
Reactive Transformer (RxT) -- Stateful Real-Time Processing for
  Event-Driven Reactive Language Models
The Transformer architecture has become the de facto standard for Large
Language Models (LLMs), demonstrating remarkable capabilities in language
understanding and generation. However, its application in conversational AI is
fundamentally constrained by its stateless nature and the quadratic
computational complexity (O(L^2)) with respect to sequence length L.
Current models emulate memory by reprocessing an ever-expanding conversation
history with each turn, leading to prohibitive costs and latency in long
dialogues. This paper introduces the Reactive Transformer (RxT), a novel
architecture designed to overcome these limitations by shifting from a
data-driven to an event-driven paradigm. RxT processes each conversational turn
as a discrete event in real-time, maintaining context in an integrated,
fixed-size Short-Term Memory (STM) system. The architecture features a distinct
operational cycle where a generator-decoder produces a response based on the
current query and the previous memory state, after which a memory-encoder and a
dedicated Memory Attention network asynchronously update the STM with a
representation of the complete interaction. This design fundamentally alters
the scaling dynamics, reducing the total user-facing cost of a conversation
from quadratic (O(N^2 cdot T)) to linear (O(N cdot T)) with respect to
the number of interactions N. By decoupling response generation from memory
updates, RxT achieves low latency, enabling truly real-time, stateful, and
economically viable long-form conversations. We validated our architecture with
a series of proof-of-concept experiments on synthetic data, demonstrating
superior performance and constant-time inference latency compared to a baseline
stateless model of comparable size.
The Reactive Transformer (RxT) addresses the limitations of stateless Transformers in conversational AI by using an event-driven paradigm with a fixed-size Short-Term Memory (STM) system, achieving linear scaling and low latency.
Transformer architecture Large Language Models (LLMs) stateless nature quadratic computational complexity Reactive Transformer (RxT) event-driven paradigm Short-Term Memory (STM) generator-decoder memory-encoder Memory Attention network linear scaling constant-time inference latency