arXiv AI
Jun 19
Bi-Anchor Interpolation Solver for Accelerating Generative Modeling
arXiv:2601. 21542v3 Announce Type: replace-cross Abstract: Flow Matching (FM) models have emerged as a leading paradigm for high-fidelity synthesis.
Ghost Attractor Networks: Basin-Structured Dynamical Decoders for Closed-Loop Sequential Generation
arXiv:2606. 18315v1 Announce Type: cross Abstract: Sequential output generation with large-scale Transformer and diffusion decoders pays a memory cost that grows with sequence length, plus iterative per-step computation.
Related stories
arXiv AI
Jul 7
The Map Behind the Flow: Finite-Step Gradient Descent as a Dynamical System
arXiv:2607. 04993v1 Announce Type: cross Abstract: Many phenomena of deep learning are dynamical: they concern not only which minima exist, but how gradient descent reaches, avoids, or selects among them.
arXiv Machine Learning
Jul 23
Koopman Dreamer: Spectrally Constrained Latent Dynamics for Stable World-Model Imagination
arXiv:2607. 19719v1 Announce Type: new Abstract: Latent world models improve sample efficiency in continuous control by optimizing policies over imagined latent trajectories, but common neural transitions offer limited direct control over modal persistence and error accumulation in long rollouts.
arXiv Machine Learning
Jul 28
The balance between compactness and forecast accuracy of data-driven latent-space reduced-order models in controlled wake flows
arXiv:2607. 24569v1 Announce Type: cross Abstract: Model-based active flow control requires predictive models that are accurate, stable, and fast enough for real-time optimisation.
arXiv Machine Learning
Jun 5
Let It Be Simple: One-Step Action Generation for Vision-Language-Action Models
arXiv:2606. 05737v1 Announce Type: cross Abstract: Diffusion-based vision-language-action (VLA) models often inherit the image-generation view: actions are generated by iterative denoising.
Hugging Face Trending Papers
Jun 4
Let It Be Simple: One-Step Action Generation for Vision-Language-Action Models
Diffusion-based vision-language-action (VLA) models often inherit the image-generation view: actions are generated by iterative denoising. We argue that VLA action generation has a different condition-target structure: the policy is conditioned on rich observations, language, and state, but predicts only a compact, low-dimensional action chunk.
arXiv Machine Learning
Jun 29
CANNs: A Toolkit for Research on Continuous Attractor Neural Networks
arXiv:2606. 27783v1 Announce Type: cross Abstract: Continuous attractor neural networks (CANNs) are the canonical computational framework for how the brain encodes continuous variables such as spatial position, head direction, and movement direction, and explain the activity of hippocampal place cells, entorhinal grid cells, and head-direction cells.
arXiv AI
Aug 6
Lindblad-Inspired Multi-Timescale Reservoir Computing with Separable Rotation and Dissipation
arXiv:2608. 04028v1 Announce Type: cross Abstract: Echo-state networks enable efficient temporal learning by fixing the recurrent dynamics and training only a linear readout.
arXiv AI
1d ago
PhyxMamba: Chaotic System Reconstruction from Short Context Observations with Generative State-Space Models
arXiv:2505. 23863v3 Announce Type: replace-cross Abstract: Understanding chaotic dynamics is a fundamental problem across scientific disciplines, including climate science, neuroscience, and fluid dynamics, yet direct experimentation and intervention in such systems are often infeasible.
arXiv Machine Learning
Jul 3
Dendritic In-Context Learning in a Single-Layer Spiking Neural Network
arXiv:2607. 02283v1 Announce Type: cross Abstract: In-context learning (ICL) operates via implicit gradient descent embedded in the forward pass of modern AI architectures -- Transformers, Mamba, state-space models, and MLPs.
arXiv AI
Jun 12
Diffusion Transformer World-Action Model for AV Scene Prediction
arXiv:2606. 12987v1 Announce Type: cross Abstract: Action-conditioned world models let an autonomous vehicle predict future camera scenes from its own planned controls, enabling planning and simulation without real-world rollouts, but at compact, trainable scale the futures are ambiguous and the field's standard distortion metrics actively mislead: they reward a blurry regression mean over a realistic prediction.
arXiv Machine Learning
Jun 11
Mechanical Field Networks: Structured Neural Dynamics for Multivariate Systems
arXiv:2606. 11251v1 Announce Type: new Abstract: Many multivariate dynamical systems are observed only through trajectories, leaving the mechanisms governing their joint dynamics hidden.