Remote VAEs for decoding with Inference Endpoints 🤗

Universal Assisted Generation: Faster Decoding with Any Assistant Model

Transformer-based Encoder-Decoder Models

Ettin Suite: SoTA Paired Encoders and Decoders

Low-Latency Task-Oriented Image Transmission with Opportunistic Spectrum Access

arXiv:2607. 01921v1 Announce Type: cross Abstract: Communication systems designed for reliable data reconstruction, rather than task-oriented communication, typically rely on separate source and channel coding and incur high latency under limited spectrum availability and fading channels.

On the quantitative analysis of decoder-based generative models

$\mathbf{\lambda}$-VAE: Variance Equalization for Posterior Collapse

arXiv:2607. 05531v1 Announce Type: new Abstract: Variational Autoencoders (VAEs) frequently suffer from posterior collapse, a failure mode in which the approximate posterior converges to the prior, rendering the latent code uninformative.

Point2Radio: A Foundation Model for Cross-Scene Radio Fields from Material-Aware Point Clouds

arXiv:2607. 28994v1 Announce Type: cross Abstract: High-fidelity radio fields are typically simulated for every scene--transmitter configuration or fitted separately to each scene, failing to exploit propagation structures shared across environments.

WhiteMatter: All-to-All Cross-Layer Connections via KV Mixing

WhiteMatter introduces a novel architecture for Transformers that connects every attention layer to representations from all layers of each past token, allowing connection weights to vary across consumer layers and adapt to the source token. The design uses a router to mix the $L$ layer states of each token into $k$ KV channels, which are cached for subsequent tokens; each consumer layer attends to one channel. Experiments show that WhiteMatter outperforms a vanilla Transformer with 50% more layers and maintains most of this advantage even when the KV-cache is compressed by 50%.

Speculative Decoding for 2x Faster Whisper Inference

JSCGC: Joint Source-Channel-Generation Coding for Wireless Generative Communications

arXiv:2606. 12858v1 Announce Type: cross Abstract: Conventional communication systems, including both separation-based coding and learning-based joint source-channel coding (JSCC), are typically designed under Shannon's rate-distortion theory.

$\beta$-VAEs as Effective Theories: Tolerance-Dependent Dimension

arXiv:2608. 10599v1 Announce Type: new Abstract: In a $\beta$-VAE, increasing the regularization strength acts as a spectral cutoff by collapsing low-utility latent coordinates.

Markov Chain Decoders Overcome the Heavy-Tail Limitations of Lipschitz Generative Models

arXiv:2605. 18931v2 Announce Type: replace-cross Abstract: Heavy-tailed distributions are prevalent in performance evaluation, network traffic, and risk modeling.