Reactive Transformer (RxT) -- Stateful Real-Time Processing for Event-Driven Reactive Language Models

• URL: http://arxiv.org/abs/2510.03561v1
• Date: Fri, 03 Oct 2025 23:18:07 GMT
• Title: Reactive Transformer (RxT) -- Stateful Real-Time Processing for Event-Driven Reactive Language Models
• Abstract summary: Reactive Transformer (RxT) is a novel architecture designed to overcome limitations by shifting from a data-driven to an event-driven paradigm.<n> RxT processes each conversational turn as a discrete event in real-time, maintaining context in an integrated Short-Term Memory (STM) system.<n>We show that RxT achieves low latency, enabling truly real-time, stateful, and economically viable long-form conversations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• EmambaIR: Efficient Visual State Space Model for Event-guided Image Reconstruction [7.034388167235335]
  EmambaIR is an Efficient visual State Space Model designed for image reconstruction using spatially sparse and temporally continuous event streams.<n>Our framework introduces two key components: the cross-modal Top-k Sparse Attention Module (TSAM) and the Gated State-Space Module (GSSM)
  arXiv  Detail & Related papers  (2026-05-08T17:56:01Z)
• DexWorldModel: Causal Latent World Modeling towards Automated Learning of Embodied Tasks [54.32016216994156]
  We introduce the Causal Latent World Model (CLWM), which employs DINOv3 features as generative targets to disentangle interaction semantics from visual noise.<n>To overcome memory scaling, CLWM features a Dual-State Test-Time Training (TTT) Memory that guarantees a strict $mathcalO(1)$ footprint for long-horizon tasks.<n>We present EmbodiChain, an online framework that establishes the Efficiency Law by injecting an infinite flow of physics-grounded trajectories during training.
  arXiv  Detail & Related papers  (2026-04-13T03:19:36Z)
• Memory Caching: RNNs with Growing Memory [56.25483647131372]
  We introduce Memory Caching (MC), a technique that enhances recurrent models by caching checkpoints of memory states (a.k.a. hidden states)<n>We propose four variants of MC, including gated aggregation and sparse selective mechanisms, and discuss their implications on both linear and deep memory modules.<n>The results indicate that while Transformers achieve the best accuracy, our MC variants show competitive performance, close the gap with Transformers, and performs better than state-of-the-art recurrent models.
  arXiv  Detail & Related papers  (2026-02-27T18:53:41Z)
• CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling [40.705016911274]
  We introduce a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity.<n>CoMeT can be integrated into pre-trained models with only minimal fine-tuning.<n>A model equipped with CoMeT and fine-tuned on 32k contexts can accurately retrieve a passkey from any position within a 1M token sequence.
  arXiv  Detail & Related papers  (2026-02-02T07:49:44Z)
• Generative AI for Video Translation: A Scalable Architecture for Multilingual Video Conferencing [0.21748200848556343]
  Real-time deployment of cascaded generative AI pipelines for applications like video translation is constrained by significant system-level challenges.<n>This paper proposes and evaluates a practical system-level framework designed to mitigate these critical bottlenecks.<n>The proposed architecture incorporates a turn-taking mechanism to reduce computational complexity from quadratic to linear in multi-user scenarios.
  arXiv  Detail & Related papers  (2025-12-15T21:21:09Z)
• DeepCoT: Deep Continual Transformers for Real-Time Inference on Data Streams [63.27233749591346]
  Transformer-based models have dramatically increased their size and parameter count to tackle increasingly complex tasks.<n>Stream data inference is typically performed over a sliding temporal window, leading to highly redundant computations.<n>We propose the Deep Continual Transformer (DeepCoT), a redundancy-free encoder-only model that can be applied over existing deep encoder architectures with minimal changes.
  arXiv  Detail & Related papers  (2025-11-21T16:15:43Z)
• ResFormer: All-Time Reservoir Memory for Long Sequence Classification [4.298381633106637]
  Sequence classification is essential in NLP for understanding and categorizing language patterns in tasks like sentiment analysis, intent detection, and topic classification.<n> Transformer-based models, despite achieving state-of-the-art performance, have inherent limitations due to quadratic time and memory complexity.<n>We propose ResFormer, a novel neural network architecture designed to model varying context lengths efficiently through a cascaded methodology.
  arXiv  Detail & Related papers  (2025-09-28T21:20:49Z)
• Echo State Transformer: When chaos brings memory [2.07180164747172]
  We introduce Echo State Transformers (EST), a hybrid architecture for sequential data processing.<n>EST integrates the Transformer attention mechanisms with Reservoir Computing principles to create a fixedsize window distributed memory system.<n>EST achieves constant computational complexity at each processing step, effectively breaking the quadratic scaling problem of standard Transformers.
  arXiv  Detail & Related papers  (2025-06-25T09:56:25Z)
• Pseudo-Autoregressive Neural Codec Language Models for Efficient Zero-Shot Text-to-Speech Synthesis [64.12708207721276]
  We introduce a novel pseudo-autoregressive (PAR) language modeling approach that unifies AR and NAR modeling.<n>Building on PAR, we propose PALLE, a two-stage TTS system that leverages PAR for initial generation followed by NAR refinement.<n>Experiments demonstrate that PALLE, trained on LibriTTS, outperforms state-of-the-art systems trained on large-scale data.
  arXiv  Detail & Related papers  (2025-04-14T16:03:21Z)
• Ladder-residual: parallelism-aware architecture for accelerating large model inference with communication overlapping [36.71999572939612]
  We introduce Ladder Residual, a simple architectural modification applicable to all residual-based models.<n>Applying Ladder Residual to all its layers can achieve 29% end-to-end wall clock speed up at inference time with TP sharding over 8 devices.<n>We train a 1B and 3B Ladder Transformer from scratch and observe comparable performance to a standard dense transformer baseline.
  arXiv  Detail & Related papers  (2025-01-11T17:06:30Z)
• COrAL: Order-Agnostic Language Modeling for Efficient Iterative Refinement [80.18490952057125]
  Iterative refinement has emerged as an effective paradigm for enhancing the capabilities of large language models (LLMs) on complex tasks. We propose Context-Wise Order-Agnostic Language Modeling (COrAL) to overcome these challenges. Our approach models multiple token dependencies within manageable context windows, enabling the model to perform iterative refinement internally.
  arXiv  Detail & Related papers  (2024-10-12T23:56:19Z)
• Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference [2.9302211589186244]
  Large language models (LLMs) have transformed natural language processing, enabling machines to generate human-like text and engage in meaningful conversations. Developments in computing and memory capabilities are lagging behind, exacerbated by the discontinuation of Moore's law. compute-in-memory (CIM) technologies offer a promising solution for accelerating AI inference by directly performing analog computations in memory.
  arXiv  Detail & Related papers  (2024-06-12T16:57:58Z)
• RWKV: Reinventing RNNs for the Transformer Era [54.716108899349614]
  We propose a novel model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. We scale our models as large as 14 billion parameters, by far the largest dense RNN ever trained, and find RWKV performs on par with similarly sized Transformers.
  arXiv  Detail & Related papers  (2023-05-22T13:57:41Z)
• Token Turing Machines [53.22971546637947]
  Token Turing Machines (TTM) is a sequential, autoregressive Transformer model with memory for real-world sequential visual understanding. Our model is inspired by the seminal Neural Turing Machine, and has an external memory consisting of a set of tokens which summarise the previous history.
  arXiv  Detail & Related papers  (2022-11-16T18:59:18Z)
• TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding [60.292702363839716]
  Current SOTA backbone networks for speaker embedding are designed to aggregate multi-scale features from an utterance with multi-branch network architectures for speaker representation. We propose an effective temporal multi-scale (TMS) model where multi-scale branches could be efficiently designed in a speaker embedding network almost without increasing computational costs.
  arXiv  Detail & Related papers  (2022-03-17T05:49:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.