Dynamics-Level Watermarking of Flow Matching Models with Random Codes
This paper proposes embedding watermarks directly into the velocity field (continuous dynamics) of flow matching generative models, rather than into weights or outputs. The method uses key-dependent perturbations added during training, formulated as random coding over a continuous channel, allowing black-box message recovery at detection time. The perturbation is designed to leave the generated distribution unchanged. Experiments on MNIST and CIFAR-10 demonstrate reliable message recovery, preserved generation quality, and chance-level decoding without the secret key.
Related guides (2)
Related events (8)
AI Watermarking 101: Tools and Techniques
Hugging Face published an educational overview of AI watermarking methods for generated content, covering both text and image watermarking techniques. The post surveys existing tools and approaches for embedding detectable signals into AI-generated outputs. This is relevant to provenance tracking, content authentication, and regulatory compliance efforts around AI-generated media.
Channel-wise Vector Quantization (CVQ): A New Image Tokenization Paradigm with Next-Channel Prediction
Researchers introduce Channel-wise Vector Quantization (CVQ), which replaces conventional patch-wise discrete tokens with channel-wise tokens that represent an image as discrete levels of visual detail. Built on CVQ, the Channel-wise Autoregressive (CAR) model uses a 'next-channel prediction' objective, generating images by progressively refining from global structure to fine-grained attributes. CVQ achieves 100% codebook utilization with a 16K+ codebook and the CAR model scores 86.7 on DPG and 0.79 on GenEval for text-to-image generation. The approach offers a structural alternative to raster-order patch-based autoregressive image generation.
The Matching Principle: A Geometric Theory Unifying Robustness, Domain Adaptation, and Alignment via Nuisance Covariance
This paper proposes the 'matching principle': a unified geometric framework arguing that robustness methods (CORAL, IRM, adversarial training, augmentation, metric learning, Jacobian penalties, alignment constraints) are all estimators of the same object—the covariance of label-preserving deployment nuisance—and that regularizing the encoder Jacobian along this covariance's range is the core statistical problem. The authors prove closed-form optimality results in a linear-Gaussian model, introduce the Trajectory Deviation Index (TDI) as a label-free embedding sensitivity probe, and validate predictions across 13 pre-registered experimental blocks including Qwen2.5-7B. At 7B scale, matched style-PMH improves selective honesty while standard DPO degrades Style TDI, connecting the theory to alignment safety.
AdaCodec: Predictive Visual Coding for Efficient Video MLLMs
AdaCodec introduces a predictive visual code interface for video multimodal large language models that exploits temporal redundancy in video. Instead of encoding every sampled frame as an independent RGB image, it sends full visual tokens only for reference frames with high conditional predictive cost, and encodes inter-frame changes as compact P-tokens. Evaluated against a Qwen3-VL-8B per-frame baseline across eleven benchmarks, AdaCodec at 1/7 the token budget (32k vs 224k tokens) surpasses the baseline on all long-video benchmarks while reducing time-to-first-token from 9.26s to 1.62s.
Glow: Better reversible generative models
OpenAI introduces Glow, a reversible generative model using invertible 1x1 convolutions that extends prior work on normalizing flows. The model generates realistic high-resolution images, supports efficient sampling, and learns disentangled features for attribute manipulation. Code and an online visualization tool are released alongside the paper.
VQ-Diffusion: Vector Quantized Diffusion Models on Hugging Face
This Hugging Face blog post introduces VQ-Diffusion, a text-to-image generation approach that combines vector quantization with diffusion models. The method operates in a discrete latent space defined by a VQ-VAE codebook, applying the diffusion process to token sequences rather than continuous pixel or latent representations. The post likely covers integration into the Hugging Face diffusers ecosystem and demonstrates generation capabilities.
Kolmogorov Regression lifts diffusion policies to Cameron-Martin space for robust long-horizon control
Researchers introduce a backward Kolmogorov equation framework that reformulates diffusion policy training as a deterministic boundary-value PDE problem in Cameron-Martin space, replacing stochastic score matching. The approach uses a precision-weighted Cameron-Martin loss and a Kolmogorov residual as an inference-time failure detector, yielding convergence guarantees tied to kernel effective rank rather than action dimension. Validation on the PushT manipulation benchmark shows 17% improvement in episode reward and 67.6% reduction in inter-step drift; a 6-station manufacturing scheduling task shows 28.4% lower RMSE than LSTM baselines and 96% reduction in deadlock events via Hamilton-Jacobi reachability certification.
Trajectory Analysis of Masked Diffusion LMs for Graph-to-Text Generation with Lambda-Scaled Structural Decoding
This paper presents the first systematic study of masked diffusion language models (MDLMs) for graph-to-text generation, analyzing the order in which tokens are unmasked during iterative decoding. The authors find MDLMs naturally unmask entities first, then relational/function words, then structural tokens—a pattern disrupted by supervised fine-tuning, which prematurely anchors structural tokens and causes hallucination or omission. They propose lambda-scaled structural decoding, a training-free inference-time fix that recovers +9.4 BLEU-4, and introduce Graph-LLaDA, which integrates a Graph Transformer encoder into LLaDA's decoding process. Cross-dataset evaluation on the LAGRANGE benchmark shows prior baselines overfit to dataset-specific patterns while MDLM-based approaches generalize better.