arXiv AI
Jul 3
Subliminal Clocks: Latent Time Modelling in Diffusion Language Models
arXiv:2607. 01774v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) have recently emerged as a promising alternative to autoregressive models.
On the Redundancy of Timestep Embeddings in Diffusion Models
arXiv:2606. 20416v1 Announce Type: new Abstract: Diffusion models rely heavily on explicit timestep embeddings to modulate the denoising process across various noise scales.
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arXiv Machine Learning
Jun 16
Towards a Unified Generative Model for Scarce Time Series with Domain Experts
arXiv:2606. 15172v1 Announce Type: new Abstract: Synthesizing realistic time series with generative models has wide-ranging applications in real-world scenarios.
arXiv AI
6d ago
Does Uniform Discrete Diffusion Need Time?
Uniform discrete diffusion models (UDMs) typically rely on explicit time conditioning, yet this study finds that such conditioning is often unnecessary in practice. While the population‑optimal UDM predictor generally depends on time—controlling how much the model should trust the observed context—the dependence becomes negligible in finite‑data language settings. Empirical results show that trained language UDMs exhibit limited time sensitivity across most of the diffusion trajectory, and time‑agnostic predictors can match or outperform time‑conditioned models on various datasets and training objectives.
arXiv Machine Learning
Jun 16
Temporal Difference Learning for Diffusion Models
arXiv:2606. 15048v1 Announce Type: new Abstract: Diffusion models are typically trained with objectives that focus on local denoising targets at individual time steps (or adjacent pairs), which do not enforce consistency between predictions along the denoising trajectory.
arXiv AI
Jul 14
Unified Backbone Refinement for Diffusion Models via Internal-Latent Analysis
arXiv:2607. 09753v1 Announce Type: cross Abstract: Diffusion models have achieved remarkable success across diverse domains, with performance closely related to the denoising backbones that parameterize the score function.
arXiv Computer Vision
4d ago
LDM-is-AE: Latent Diffusion Model is an Auto-Encoder for End-to-End Image Generation
arXiv:2609.37080v1 Announce Type: new Abstract: Latent Diffusion Models (LDMs) typically adopt a two-stage pipeline: an auto-encoder (AE) is first pre-trained to define a latent space, then a diffusi...
arXiv Machine Learning
Sep 14
DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning
The paper introduces Diffusion-Conditioned Representation Alignment (DCRA), a training framework that uses the forward diffusion process as a structured corruption scheduler for time‑series representation learning. DCRA aligns representations across noise levels with a feature‑level consistency objective, preserving class‑discriminative structure and enabling smooth, semantically coherent trajectories in latent space. Experiments on the CHB‑MIT EEG dataset demonstrate that DCRA improves seizure detection performance under various noise conditions, achieving higher sensitivity at low false‑positive rates and producing more balanced, structured representations than baseline methods.
arXiv Machine Learning
Jun 8
Generalization of Diffusion Models Arises with a Balanced Representation Space
arXiv:2512. 20963v3 Announce Type: replace Abstract: Diffusion models excel at generating high-quality, diverse samples, yet they risk memorizing training data when overfit to the training objective.
arXiv AI
Jun 2
Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms
arXiv:2503. 07154v3 Announce Type: replace-cross Abstract: Generative pre-training is often framed through a false dichotomy between autoregressive models for discrete signals and diffusion models for continuous signals.
arXiv AI
Jun 15
DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation
arXiv:2506. 14202v4 Announce Type: replace-cross Abstract: End-to-end backpropagation requires storing activations throughout all layers, creating memory bottlenecks that limit model scalability.
Hugging Face Trending Papers
Jun 22
PeLAP-A: Adaptive Latent Pruning for Lightweight Latent Diffusion Models
Latent diffusion models achieve strong generative performance by operating in a compressed latent space produced by a variational autoencoder (VAE). However, it remains unclear whether all latent channels contribute equally to the diffusion process, or whether significant redundancy exists.
arXiv AI
Jun 17
Rethinking Cross-Layer Information Routing in Diffusion Transformers
arXiv:2605. 20708v2 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) have become a de facto backbone of modern visual generation, and nearly every major axis of their design -- tokenization, attention, conditioning, objectives, and latent autoencoders -- has been extensively revisited.