Flowing Through Layers: A Continuous Dynamical Systems Perspective on Transformers
- URL: http://arxiv.org/abs/2502.05656v2
- Date: Fri, 23 May 2025 02:47:52 GMT
- Title: Flowing Through Layers: A Continuous Dynamical Systems Perspective on Transformers
- Authors: Jacob Fein-Ashley,
- Abstract summary: We show that the standard discrete update rule of transformer layers can be naturally interpreted as a forward Euler discretization of a continuous dynamical system.<n>Our Transformer Flow Approximation Theorem demonstrates that, under standard Lipschitz continuity assumptions, token representations converge uniformly to the unique solution of an ODE as the number of layers grows.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We show that the standard discrete update rule of transformer layers can be naturally interpreted as a forward Euler discretization of a continuous dynamical system. Our Transformer Flow Approximation Theorem demonstrates that, under standard Lipschitz continuity assumptions, token representations converge uniformly to the unique solution of an ODE as the number of layers grows. Moreover, if the underlying mapping satisfies a one-sided Lipschitz condition with a negative constant, the resulting dynamics are contractive, causing perturbations to decay exponentially across layers. Beyond clarifying the empirical stability and expressivity of transformer models, these insights link transformer updates to a broader iterative reasoning framework, suggesting new avenues for accelerated convergence and architectural innovations inspired by dynamical systems theory.
Related papers
- KoopGen: Koopman Generator Networks for Representing and Predicting Dynamical Systems with Continuous Spectra [65.11254608352982]
We introduce a generator-based neural Koopman framework that models dynamics through a structured, state-dependent representation of Koopman generators.<n>By exploiting the intrinsic Cartesian decomposition into skew-adjoint and self-adjoint components, KoopGen separates conservative transport from irreversible dissipation.
arXiv Detail & Related papers (2026-02-15T06:32:23Z) - Deep Delta Learning [91.75868893250662]
We introduce Deep Delta Learning (DDL), a novel architecture that generalizes the standard residual connection.<n>We provide a spectral analysis of this operator, demonstrating that the gate $(mathbfX)$ enables dynamic between identity mapping, projection, and geometric reflection.<n>This unification empowers the network to explicitly control the spectrum of its layer-wise transition operator, enabling the modeling of complex, non-monotonic dynamics.
arXiv Detail & Related papers (2026-01-01T18:11:38Z) - Fighter: Unveiling the Graph Convolutional Nature of Transformers in Time Series Modeling [33.595964789473065]
This work demystifies the Transformer encoder by establishing its fundamental equivalence to a Graph Convolutional Network (GCN)<n>We propose textbfFighter (Flexible Graph Convolutional Transformer), a streamlined architecture that removes redundant linear projections and incorporates multi-hop graph aggregation.
arXiv Detail & Related papers (2025-10-20T02:42:14Z) - PDE-Transformer: A Continuous Dynamical Systems Approach to Sequence Modeling [4.1812935375151925]
We propose PDE-Transformer, a sequence modeling paradigm that casts the forward pass of a Transformer as the numerical discretization of a continuous reaction-diffusion system.<n>In our framework, token embeddings evolve under a partial differential equation whose nonlocal integral term models self-attention.<n>We design an Adaptive PDE Diffusion Layer that enforces local smoothness in feature space with linear time complexity.
arXiv Detail & Related papers (2025-09-27T08:58:47Z) - Wavy Transformer [5.4806374384787695]
We propose Wavy Transformer, which consists of a novel attention layer based on second-order wavy dynamics.<n>We also introduce a feed-forward network and a normalization layer designed to preserve the physical state-velocity relationship under the chain rule.
arXiv Detail & Related papers (2025-08-18T10:03:38Z) - Can We Achieve Efficient Diffusion without Self-Attention? Distilling Self-Attention into Convolutions [94.21989689001848]
We propose (Delta)ConvFusion to replace conventional self-attention modules with Pyramid Convolution Blocks ((Delta)ConvBlocks)
By distilling attention patterns into localized convolutional operations while keeping other components frozen, (Delta)ConvFusion achieves performance comparable to transformer-based counterparts while reducing computational cost by 6929$times$ and surpassing LinFusion by 5.42$times$ in efficiency--all without compromising generative fidelity.
arXiv Detail & Related papers (2025-04-30T03:57:28Z) - Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning [30.781578037476347]
We introduce a novel approach to modeling transformer architectures using highly flexible non-autonomous neural ordinary differential equations (ODEs)
Our proposed model parameterizes all weights of attention and feed-forward blocks through neural networks, expressing these weights as functions of a continuous layer index.
Our neural ODE transformer demonstrates performance comparable to or better than vanilla transformers across various configurations and datasets.
arXiv Detail & Related papers (2025-03-03T09:12:14Z) - Latent Convergence Modulation in Large Language Models: A Novel Approach to Iterative Contextual Realignment [0.0]
A structured modulation mechanism was introduced to regulate hidden state transitions.<n>Lattice adjustments contributed to reductions in perplexity fluctuations, entropy variance, and lexical instability.
arXiv Detail & Related papers (2025-02-10T09:46:33Z) - OT-Transformer: A Continuous-time Transformer Architecture with Optimal Transport Regularization [1.7180235064112577]
We consider a dynamical system whose governing equation is parametrized by transformer blocks.<n>We leverage optimal transport theory to regularize the training problem, which enhances stability in training and improves generalization of the resulting model.
arXiv Detail & Related papers (2025-01-30T22:52:40Z) - Interpreting Affine Recurrence Learning in GPT-style Transformers [54.01174470722201]
In-context learning allows GPT-style transformers to generalize during inference without modifying their weights.
This paper focuses specifically on their ability to learn and predict affine recurrences as an ICL task.
We analyze the model's internal operations using both empirical and theoretical approaches.
arXiv Detail & Related papers (2024-10-22T21:30:01Z) - Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? [69.4145579827826]
We show a fast flow on the regression loss despite the gradient non-ity algorithms for our convergence landscape.
This is the first theoretical analysis for multi-layer Transformer in this setting.
arXiv Detail & Related papers (2024-10-10T18:29:05Z) - Latent Space Energy-based Neural ODEs [73.01344439786524]
This paper introduces novel deep dynamical models designed to represent continuous-time sequences.<n>We train the model using maximum likelihood estimation with Markov chain Monte Carlo.<n> Experimental results on oscillating systems, videos and real-world state sequences (MuJoCo) demonstrate that our model with the learnable energy-based prior outperforms existing counterparts.
arXiv Detail & Related papers (2024-09-05T18:14:22Z) - A Unified Framework for Interpretable Transformers Using PDEs and Information Theory [3.4039202831583903]
This paper presents a novel unified theoretical framework for understanding Transformer architectures by integrating Partial Differential Equations (PDEs), Neural Information Flow Theory, and Information Bottleneck Theory.
We model Transformer information dynamics as a continuous PDE process, encompassing diffusion, self-attention, and nonlinear residual components.
Our comprehensive experiments across image and text modalities demonstrate that the PDE model effectively captures key aspects of Transformer behavior, achieving high similarity (cosine similarity > 0.98) with Transformer attention distributions across all layers.
arXiv Detail & Related papers (2024-08-18T16:16:57Z) - Clustering in pure-attention hardmax transformers and its role in sentiment analysis [0.0]
We rigorously characterize the behavior of transformers with hardmax self-attention and normalization sublayers as the number of layers tends to infinity.
We show that the transformer inputsally converge to a clustered equilibrium determined by special points called leaders.
We then leverage this theoretical understanding to solve sentiment analysis problems from language processing using a fully interpretable transformer model.
arXiv Detail & Related papers (2024-06-26T16:13:35Z) - Investigating Recurrent Transformers with Dynamic Halt [64.862738244735]
We study the inductive biases of two major approaches to augmenting Transformers with a recurrent mechanism.<n>We propose and investigate novel ways to extend and combine the methods.
arXiv Detail & Related papers (2024-02-01T19:47:31Z) - Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in
Transformer-Based Variational AutoEncoder for Diverse Text Generation [85.5379146125199]
Variational Auto-Encoder (VAE) has been widely adopted in text generation.
We propose TRACE, a Transformer-based recurrent VAE structure.
arXiv Detail & Related papers (2022-10-22T10:25:35Z) - Learning stochastic dynamics and predicting emergent behavior using
transformers [0.0]
We show that a neural network can learn the dynamical rules of a system by observation of a single dynamical trajectory of the system.
We train a neural network called a transformer on a single trajectory of the model.
Transformers have the flexibility to learn dynamical rules from observation without explicit enumeration of rates or coarse-graining of configuration space.
arXiv Detail & Related papers (2022-02-17T15:27:21Z) - XAI for Transformers: Better Explanations through Conservative
Propagation [60.67748036747221]
We show that the gradient in a Transformer reflects the function only locally, and thus fails to reliably identify the contribution of input features to the prediction.
Our proposal can be seen as a proper extension of the well-established LRP method to Transformers.
arXiv Detail & Related papers (2022-02-15T10:47:11Z)
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.